TDoc Detatils - Synchornization Signal

Let's look at synchronization as a central design problem in 6G initial access. It shapes overall system efficiency as well as preparing the UE for communication. In previous generations, synchronization signal design mainly targeted robust detection and wide-area coverage. That starting point no longer fits 6G. Highly directional transmission, wider bandwidth variation and stronger energy constraints require a more adaptive design that responds to operating conditions.

Start with beam-based operation. At higher frequencies, especially mmWave and beyond, reliable communication depends on precise beam alignment. We can no longer assume omni-directional synchronization, because the UE also needs to discover the right beam. We must therefore design synchronization and beam management together. Synchronization signals become part of a dynamic search and prediction process, rather than just �always-on references,� as before.

Next, consider the balance between coverage and efficiency. Traditional designs transmitted synchronization signals frequently to ensure accessibility. In 6G, however, continuous transmission creates excessive energy consumption and unnecessary overhead. The design therefore moves toward sparse, event-driven or adaptive synchronization. We need mechanisms that remain reliable when signals are intermittent. The system must still let the UE detect the network and align with it under those conditions.

We also need consistency across system components. Synchronization must align with frame structure, numerology and bandwidth part configuration. Using consistent subcarrier spacing and timing structures across synchronization and data channels simplifies UE processing and avoids unnecessary switching overhead. This follows a broader 6G design principle: reducing fragmentation across layers improves performance and implementation efficiency.

Finally, we need to consider how intelligence can assist synchronization in 6G. The UE and network can use prediction, historical context and environmental awareness instead of blindly scanning every possibility. This changes synchronization from reacting to signals to anticipating them. The system begins to �guide� the UE toward the correct timing and spatial alignment. Initial access then becomes faster, more reliable and more energy efficient.

Together, these requirements show why we need more than signal detection for 6G initial access. We need one coordinated synchronization mechanism that integrates beam management, energy efficiency, system consistency and intelligence.

• What is the overall objective of 6G initial access design?
• What is the role of synchronization signals and channels?
• How should time-frequency resources for synchronization be designed?
• How should synchronization signals (PSS/SSS) be designed?
• How should PSS be designed?
• How should SSS be designed?
• How should synchronization coexist with NR (MRSS scenarios)?
• What information should be delivered via PBCH/MIB?
• How should PBCH be designed for performance?
• How should synchronization periodicity and patterns be designed?
• How does beam-based operation impact synchronization?
• How should broadcast channels be delivered?
• How can coverage be extended for broadcast channels?
• What additional signals are needed for synchronization?
• How should mobility measurements be supported?
• How can AI/ML be integrated into 6GR initial access?

Executive Summary

What is the overall objective of 6G initial access design?

Let's start with what 6G initial access needs to achieve: reliable, efficient and scalable network entry across devices and deployment scenarios. We need to optimize the system, not just detect a signal. The design must balance network energy efficiency, UE computational complexity, latency and coverage. These requirements interact closely, so improving one often degrades another. Our goal is a balance that works across different environments and device capabilities.

Why is initial access important in 6G systems?

Initial access is the UE's first interaction with the network. It determines how quickly and reliably the UE connects, directly affecting user experience, power consumption and system efficiency. In 6GR, the focus on energy saving and flexibility makes this step even more important. Every subsequent communication procedure depends on it.

How does energy efficiency influence initial access design?

To lower network power consumption in 6GR, we need to reduce always-on transmissions. But fewer transmissions mean more search effort for the UE. Initial access must therefore minimize transmission overhead while maintaining detection reliability. We need to balance the network energy saving against the added UE complexity.

How does UE complexity affect initial access mechanisms?

UE complexity affects device cost, power consumption and whether an implementation is practical. When we reduce signaling, the UE must do more of the work. It searches across time and frequency using multiple hypotheses, increasing processing load and energy use. We must limit those hypotheses so that even low-tier devices can detect signals efficiently.

What are the limitations of NR initial access that motivate 6G improvements?

NR improved initial access over LTE, but it still has limitations against 6GR requirements. Energy efficiency, UE complexity and flexibility across deployment scenarios remain areas for improvement. These limitations explain why we need to revisit the design for 6GR.

Why are always-on signals (e.g., SS/PBCH) problematic?

Always-on signals provide reliable detection, but the network must transmit them even when no UE is present. This continuous energy cost limits deep sleep. In dense deployments, continuous transmission also increases interference. Always-on signaling therefore becomes inefficient in both energy use and performance.

How does increasing periodicity impact latency and complexity?

If we increase periodicity, the network transmits less often and saves energy. But the UE may wait longer for the next signal, increasing access latency. The larger detection window also requires more timing and frequency hypotheses, increasing UE complexity. We therefore need to balance energy saving, latency and UE processing effort.

How do different deployment scenarios (standalone vs multi-cell vs NTN) change initial access requirements?

6GR must support standalone, multi-cell and NTN deployments. Each places different demands on synchronization, signaling and UE behavior, so we need a flexible, adaptable initial access design.

How do coverage and latency requirements differ across these scenarios?

Let's compare the priorities across deployments. In standalone operation, the UE must connect quickly while detecting the cell reliably, so both latency and coverage matter. Multi-cell operation can tolerate some latency because cells can assist each other. In NTN, coverage dominates: signals must reach very large areas, and longer latency may be acceptable. We need to adapt the design to these different priorities.

How does UE behavior change in NTN vs terrestrial networks?

In NTN, large Doppler shifts and long propagation delays increase the UE's frequency and timing uncertainty. The UE must handle wider detection windows and more frequency hypotheses. Long periodicity may also make it rely on one-shot detection. These conditions differ substantially from terrestrial networks, so we must adapt UE behavior and detection strategies accordingly.

How do economy-of-scale considerations influence initial access design choices?

6GR must support both low-cost and high-performance devices within a large, diverse ecosystem. We therefore need design choices that scale economically. The goal is widespread adoption without excessive implementation complexity.

How does device diversity affect design constraints?

Compare the capabilities at each end of the device range. Low-tier devices have limited processing power and bandwidth, so they need simple detection mechanisms. High-end devices can support more advanced features and optimizations. We must scale the design across these classes, using a common framework with optional enhancements for more capable devices.

How do cost and complexity trade-offs impact standardization decisions?

Every design choice affects cost and performance. Reducing complexity lowers device cost and supports large-scale deployment, while better performance often needs more processing. During standardization, we must balance these factors to achieve acceptable performance with practical implementations. That balance is essential for a successful ecosystem.

What is the role of synchronization signals and channels?

Synchronization signals and channels let the UE detect a cell, align in time and frequency, and acquire essential system information. Their design also controls UE complexity, directly affecting processing effort, detection reliability and system efficiency. We therefore need to balance robustness and complexity when designing these initial access signals.

What does synchronization mean at the physical layer?

At the physical layer, synchronization means aligning the UE with the network in time and frequency. The UE detects signal timing, corrects frequency offset and establishes symbol alignment. Without that alignment, it cannot correctly decode OFDM signals, so synchronization must precede data communication.

How does UE achieve time synchronization?

The UE searches across time for known signal patterns such as PSS, typically using correlation. Once it identifies the correct timing, it aligns its internal clock with the received signal. This lets the UE detect symbol boundaries correctly. Accurate timing is essential for OFDM demodulation.

How does UE achieve frequency synchronization?

The UE estimates and corrects the frequency offset between itself and the network. Oscillator mismatch and Doppler effects cause this offset. The UE estimates it from synchronization signals, then compensates for it to align subcarriers correctly. Without frequency synchronization, inter-carrier interference degrades signal quality and decoding performance.

What roles do PSS, SSS, and PBCH play in NR?

Let's separate the roles of PSS, SSS and PBCH, the core NR initial access components. Each handles a specific part of detection or configuration. Together, they give the UE a structured, hierarchical path through synchronization and system information acquisition.

How does PSS contribute to timing and detection?

The UE detects PSS first through blind search over time and frequency. PSS must support robust detection under uncertainty. Detection gives the UE coarse timing alignment and partial cell identification, reducing the search space for later steps. PSS therefore determines where the whole initial access process starts.

How does SSS refine synchronization and identify cell ID?

After detecting PSS, the UE processes SSS to refine timing and frequency synchronization and determine the full physical cell ID. Because the UE already has partial synchronization, it needs fewer hypotheses for SSS than for PSS. PSS and SSS together therefore provide reliable cell identification with a less complex second detection stage.

How does PBCH provide system timing and configuration?

PBCH carries the Master Information Block (MIB), including timing configuration, control channel information and system bandwidth details. After decoding PBCH, the UE knows where to find further system information such as SIB1. PBCH therefore connects synchronization to higher layer procedures. Without it, the UE cannot proceed to full access.

What additional information (e.g., cell type such as TN vs NTN) should be embedded in synchronization signals?

One suggestion is to include additional information in synchronization signals for new scenarios. For example, the signals may identify terrestrial (TN) or non-terrestrial (NTN) cell types. We need to assess this carefully: the added functionality may also increase detection complexity.

What information is critical at initial detection stage?

At initial detection, we should provide only essential information: a timing reference, coarse cell identification and basic configuration indicators. More information increases complexity. We should therefore defer non-essential information until later stages, such as PBCH decoding, to keep initial detection efficient.

How does adding information impact detection complexity?

Adding information to synchronization signals makes the UE decode more bits during initial detection. This may require more complex algorithms and can increase the number of hypotheses. The added processing raises UE complexity and power consumption, so we need to balance functionality against simplicity.

How should time-frequency resources for synchronization be designed?

Let's consider bandwidth, signal placement and transmission structure together. These choices affect UE capability, detection performance and system efficiency. Wider bandwidth can improve detection, but also increases UE complexity. We must support different device capabilities and deployments while balancing performance, complexity and flexibility in the time-frequency resource design.

What are the candidate bandwidth options for 6G initial access?

The candidate initial access bandwidths include 3 MHz and 5 MHz. Each offers a different balance between UE capability and detection performance. The choice directly affects how easily the UE can detect synchronization signals.

Should design assume 3 MHz or 5 MHz bandwidth?

Compare 3 MHz with 5 MHz from the UE's perspective. A smaller bandwidth such as 3 MHz allows simpler RF design and less processing. But lower signal energy may reduce detection performance. A larger bandwidth such as 5 MHz captures more signal energy and provides more robust detection. We therefore need to choose based on the target device capability and deployment scenario.

How does bandwidth affect UE capability?

Wider bandwidth requires the UE to sample faster and perform more processing, increasing hardware complexity and power consumption. Low-tier devices may struggle to support it efficiently. Narrow bandwidth reduces these requirements. To maintain broad compatibility, we must consider the system's least capable devices when choosing bandwidth.

How does synchronization signal bandwidth affect complexity?

Synchronization signal bandwidth determines how much frequency space the UE must process. Larger bandwidth improves detection but increases complexity; smaller bandwidth reduces complexity but may reduce robustness. This is the basic balance we need to assess.

Why does larger bandwidth increase raster density?

With a wider synchronization signal, the search must cover more frequency space across the band. Coverage may therefore require more raster points. Each point adds a candidate frequency location for the UE to search, increasing overall search complexity.

How does it impact UE search complexity?

The UE must process every candidate frequency location within the bandwidth. A larger bandwidth adds both data and hypotheses to that processing. The UE therefore performs more correlations and takes longer to detect the signal, increasing its processing load.

What techniques can unify designs across bandwidths?

We need to support multiple bandwidth options without building a completely different implementation for each one. A unified design avoids that duplication, and puncturing is one technique that can help.

What is puncturing and how does it help?

Puncturing removes selected subcarriers from a wider signal to emulate a narrower bandwidth. We can then reuse the same base design across bandwidth configurations. The UE processes only a subset of the signal, reducing complexity while maintaining compatibility.

How can performance loss due to puncturing be compensated?

Removing subcarriers reduces signal energy and may degrade detection. We can compensate through repetition or power boosting, or optimize sequence properties for better robustness. These methods help preserve detection reliability at reduced bandwidth.

How does fixed power spectral density (PSD) influence the trade-off between bandwidth and detection performance?

Let's consider scenarios where transmit power is limited by PSD constraints. In such cases, increasing bandwidth spreads the same power over a larger frequency range. This affects detection performance and design flexibility.

How does wider bandwidth improve SNR?

Wider bandwidth allows the UE to capture more total signal energy. This can improve detection performance if power is not strictly limited. However, under PSD constraints, power per subcarrier decreases. Therefore, the benefit depends on how power is allocated. The relationship between bandwidth and SNR must be carefully evaluated.

How does PSD constraint limit design flexibility?

When power is constrained by PSD, increasing bandwidth does not increase total transmit power. Instead, power is distributed over more subcarriers. This reduces per-subcarrier power. As a result, detection performance may not improve. This limits the ability to use bandwidth as a tool for improving robustness. Therefore, design flexibility is constrained.

How does synchronization signal bandwidth impact beam sweep duration?

In beam-based systems, the network transmits synchronization signals across multiple beams. Signal bandwidth affects the duration of each transmission and therefore the overall beam sweep duration.

How does time-domain footprint change with bandwidth?

Wider bandwidth typically allows shorter symbol duration, which can reduce each transmission's duration. But we also need to consider the overall synchronization block structure. To assess the time footprint, we must examine both frequency and time domain parameters.

How does this affect latency of beam sweeping?

The network sweeps synchronization signals across multiple directions. Longer individual transmissions extend the total sweep and increase access latency; shorter transmissions allow a faster sweep. Bandwidth and signal design therefore directly affect beam sweeping latency and overall access time.

Which subcarrier spacing applies around 7 GHz?

For the new mid-band around 7 GHz, the baseline SCS is now 30 kHz. The study no longer includes 60 kHz. We can therefore narrow the SS/PBCH design choices, because subcarrier spacing directly determines symbol duration and burst length.

Why does a single subcarrier spacing matter?

Within a band, SS/PBCH uses the same subcarrier spacing as data and control channels. The UE therefore avoids changing numerology between initial access and normal operation. This simplifies implementation and keeps the frame structure consistent across procedures.

What bandwidth does the network assume?

Around 7 GHz, the network assumes channel bandwidth up to 400 MHz. Don't confuse that with a requirement for every UE to support the full width; that remains a separate question. Synchronization must work across a band much wider than the SS/PBCH block itself.

How is the synchronization raster itself constructed?

The raster tells the UE where to search, so it also determines cold-search duration. We can inherit the NR principle of at least one raster point within each channel bandwidth. The remaining discussion concerns what should determine the spacing and how many hypotheses the UE must carry during search.

Why measure the raster against PSS bandwidth?

Consider why we might derive raster spacing from PSS bandwidth rather than the full SSB bandwidth. The UE detects PSS first, so only PSS must fall inside the searched band to start detection. Using the wider SSB bandwidth creates a tighter raster than detection requires. A PSS-based raster therefore allows wider spacing.

What does supporting narrow channels cost?

Each additional low channel bandwidth adds raster points, giving the UE more frequencies to try. Restricting narrow bandwidth options directly reduces the search space. But we lose deployment flexibility, especially in bands where spectrum is already scarce.

Should NTN share the raster?

One proposal uses separate raster locations for NTN. A terrestrial UE would then avoid searching satellite cells it will not select. With one shared raster, every terrestrial cold search must include the NTN hypotheses too.

How should synchronization signals (PSS/SSS) be designed?

Let's examine PSS and SSS with the UE's initial uncertainty in mind. The UE processes these signals without accurate timing or frequency alignment. Their design must provide reliable detection with limited hypotheses and manageable UE complexity. We also need scalability and future extensibility, so we must balance several requirements rather than optimize detection alone.

What information should synchronization signals carry?

Synchronization signals give the UE its first network information. We must provide enough for detection and basic identification, but extra information increases complexity. At this stage, we should therefore include only what is essential.

Should they include full physical cell ID (PCI)?

In NR, PSS and SSS together provide the full PCI through hierarchical detection. This lets the UE uniquely identify the cell early, but also increases the number of hypotheses. We must balance early identification against detection complexity. Partial identification followed by refinement may be preferable.

How many IDs must be supported?

The system needs enough ID space for many cells, especially in dense deployments. But more IDs increase detection complexity. We must optimize ID space and complexity together, using a sequence design that scales without degrading performance.

Why use multiple synchronization signals?

With separate signals such as PSS and SSS, we can divide detection into stages. The UE then avoids detecting everything at once. This hierarchical approach reduces complexity and helps control its processing load.

How does hierarchical detection reduce complexity?

PSS starts with coarse detection and a limited set of hypotheses. SSS then refines the result. By dividing detection into these smaller steps, the UE avoids testing every combination at once. That reduces the total hypothesis count and substantially lowers processing complexity.

How does splitting information reduce hypotheses?

When we split information across signals, each detection stage handles only part of the search space. For example, PSS may identify a cell group, while SSS identifies the exact cell. The UE processes fewer hypotheses at a time, improving efficiency and reducing power consumption.

What is the minimum necessary information that must be available at the earliest stage of cell detection?

The UE faces high uncertainty at initial detection, so we should include only essential information then. We can provide additional information later through PBCH. This keeps complexity low while preserving functionality.

What can be deferred to PBCH?

We can defer non-critical configuration to PBCH, including detailed system parameters and higher layer configuration. Synchronization signals then remain simpler, reducing detection complexity and improving robustness under uncertainty.

How does early information reduce search iterations?

Key information provided early can reduce search steps. Partial identification, for example, narrows the candidate cells and reduces repeated searches. But too much information increases early detection complexity. We should therefore include only the information with the greatest effect on the search.

Should synchronization signals support extensibility for future features?

6GR must accommodate future features, so we should allow for extensibility in synchronization signal design. That flexibility must not significantly increase complexity or break compatibility.

How can reserved bits or structures be used?

We can include reserved bits or flexible structures for future extensions. These allow new features without redesigning the signal. But we must design these additions carefully so they do not harm current performance.

How does extensibility affect backward compatibility?

New features should be optional and backward compatible. Legacy devices should still detect and process the signals. We must control how we add extensions so that we preserve compatibility and long-term system stability.

How should PSS be designed?

PSS detection dominates UE complexity because the UE still lacks accurate timing and frequency alignment. Keep that uncertainty in mind when comparing sequence designs. PSS must support robust detection while minimizing computation. We want fewer hypotheses and correlation operations during blind search, which makes PSS design critical to 6GR initial access.

What are the key requirements for PSS sequences?

PSS sequences must remain reliable under noise, interference and frequency offset. They must also support efficient correlation-based detection. We need to assess both requirements together because they directly affect UE complexity and detection performance.

How important is robustness to frequency offset (CFO)?

Before PSS detection, the UE has no frequency synchronization, so the received signal may have a large frequency offset. The PSS sequence must retain good correlation properties under that offset. If it is sensitive to CFO, the UE must test more frequency hypotheses, increasing complexity. CFO robustness is therefore a key PSS requirement.

Why is time-domain correlation critical?

The UE typically detects PSS through time-domain correlation, sliding a known sequence across the received signal to find a match. It repeats this operation many times, so the process must be efficient. Strong correlation properties support reliable detection with fewer operations. Poor properties increase false or missed detection, so we must design sequences for efficient time-domain correlation.

What are the trade-offs between sequence types?

Let's compare Zadoff-Chu sequences and m-sequences. They offer different correlation performance, CFO robustness and implementation complexity. We need to consider all three when choosing a PSS sequence.

Why did NR move from Zadoff-Chu to m-sequence?

Zadoff-Chu sequences have ideal correlation properties under perfect synchronization, but they are sensitive to frequency offset. NR adopted m-sequences for better CFO robustness, making them more suitable for practical conditions. Their correlation properties are not perfect, however. We therefore need to choose sequences based on the operating conditions.

What are the limitations of m-sequence under CFO?

m-sequences tolerate CFO better than Zadoff-Chu, but they still degrade under frequency offset. Their correlation peaks may spread or weaken, making detection less reliable in extreme conditions. The UE may still need additional processing or hypotheses. We therefore need careful sequence design even with m-sequences.

What PSS sequence options are being considered?

The baseline option keeps the NR-style m-sequence. It avoids the correlation peak shift that LTE Zadoff-Chu based PSS can experience under CFO, giving stable timing detection. But large residual CFO can still weaken the peak. The UE may therefore need several frequency hypotheses during initial search.

What is the benefit of using the NR baseline?

The main benefit is timing stability: the correlation peak tends to stay at the same position even with frequency offset. This improves timing acquisition, especially while the UE is still performing blind search. But reliable detection may require enough CFO hypotheses, increasing UE processing.

What is the 1/3 symbol PSS option?

Another option shortens PSS to about one third of an OFDM symbol. Concentrating the sequence in less time can improve frequency-error tolerance and may reduce the density of CFO hypotheses. The UE may then need less initial search processing. But check whether the design uses multiple PSS locations or sequences. Those choices may transfer some complexity to SSS detection or symbol-position hypotheses.

< R1-2603518 : Figure 3. PSS Design with 1/3 Symbol Duration   >

What is the main trade-off?

Compare PSS detection complexity with the work needed in later synchronization steps. A full-symbol NR-style design is simple and timing-stable, but may require more CFO searching. A shorter PSS can improve frequency-error tolerance. However, we must avoid adding ambiguity to SSS detection and cell identification.

Should the number of PSS sequences be reduced?

Reducing the number of PSS sequences can significantly reduce UE complexity, because each sequence adds a search dimension. We therefore benefit from fewer sequences, but must retain enough information for cell identification.

How does reducing sequences impact complexity?

With fewer sequences, the UE tests fewer candidates and performs fewer correlations. This reduces processing load, detection time and power consumption. Limiting sequence count therefore gives us an effective way to reduce complexity.

What is the impact on detection robustness?

Fewer sequences may make cells harder to distinguish, increasing ambiguity or collision risk. We may then need additional mechanisms to resolve that ambiguity. We must therefore balance sequence-count reduction against sufficient cell identification capability.

How does PSS design influence the number of required CFO hypotheses?

CFO uncertainty contributes substantially to detection complexity. The PSS sequence's sensitivity to frequency offset determines how many frequency hypotheses the UE needs. Sequence design therefore directly affects hypothesis count.

How does sequence robustness reduce hypothesis count?

If the PSS sequence keeps strong correlation under frequency offset, the UE can detect it despite imperfect alignment. The UE then tests fewer CFO values, substantially reducing complexity.

How does this affect UE processing load?

Reducing CFO hypotheses directly reduces the UE's correlation operations. The UE processes less data, detects the signal sooner and consumes less power. Sequence robustness therefore directly improves UE efficiency.

How does PSS design affect UE power consumption during blind search?

Blind search is one of the UE's most power-intensive operations. PSS design determines how much processing the UE needs during that search, strongly affecting power consumption.

How does correlation complexity scale?

Count three factors when assessing correlation complexity: time samples, frequency hypotheses and sequence candidates. Increasing any one adds processing effort. Efficient sequence design can reduce one or more of these factors and control overall complexity.

How does search duration impact battery usage?

During a longer search, the UE stays active longer and its processing units consume power continuously. Slow detection therefore affects battery life. Reducing search duration improves energy efficiency, and PSS design plays a key role in achieving that reduction.

How should SSS be designed?

Now consider SSS after the UE has detected PSS and gained partial synchronization. SSS must provide reliable cell identification with good correlation properties, scalability and measurement support. Compared with PSS, the design focuses more on reliability and stability than on reducing blind-search complexity.

Why are Gold sequences used for SSS?

Gold sequences combine good correlation properties with flexibility, which explains their widespread use. For SSS, we need many IDs without sacrificing detection performance. Gold sequences offer a practical balance between performance and implementation complexity.

What are their autocorrelation and cross-correlation properties?

Good autocorrelation helps the UE identify the correct timing position. Gold sequences also have low cross-correlation between different sequences, reducing interference between cells with different IDs. Together, these properties improve multi-cell detection reliability and suit large-scale deployments.

What are the limitations of SSS under frequency offset?

The UE processes SSS after PSS detection, but residual frequency offset still affects it. SSS detection assumes some synchronization already exists. If frequency correction remains imperfect, performance can degrade.

How does CFO degrade performance?

Residual CFO can distort SSS, weakening the correlation peak and increasing detection noise. Detection probability then falls, and severe distortion may cause incorrect cell identification. SSS performance therefore depends on how accurately the UE corrects frequency during PSS processing.

What assumptions are needed for reliable SSS detection?

For SSS detection, we assume the UE already has coarse timing and frequency alignment. Without that partial synchronization, the UE would need more hypotheses, making SSS detection more complex.

How much synchronization accuracy is required?

The UE must align timing well enough to locate SSS in the correct symbol. It must also reduce frequency offset to a manageable level. With those conditions met, the UE can detect SSS efficiently using limited hypotheses. PSS performance therefore directly affects SSS reliability.

How does SSS design impact mobility measurements (e.g., RSRP accuracy)?

The UE uses SSS for measurement as well as detection. Synchronization signals may support mobility functions, so we must consider measurement accuracy when designing SSS.

How does sequence quality affect measurement stability?

Good sequence properties produce stable correlation results and more consistent signal strength measurements such as RSRP. Mobility decisions depend on that stability. Poor sequence quality may cause measurement fluctuations and degrade handover performance.

How does CFO impact measurement accuracy?

Residual CFO changes the received signal's amplitude and phase, introducing measurement errors. The UE may then underestimate or overestimate signal strength. Accurate frequency correction improves measurement reliability, so synchronization quality directly affects measurement performance.

How does SSS robustness affect cell-edge performance?

At the cell edge, low signal strength and high interference make SSS detection harder. This points to the importance of robust sequence design for maintaining cell-edge performance.

How does interference affect detection?

Neighboring-cell interference can distort SSS and make its correlation peak less clear, reducing detection reliability. Strong cross-correlation properties help limit that effect. Sequence design therefore plays an important role in interference resilience.

How does fading impact correlation reliability?

Fading changes signal amplitude and phase, affecting correlation results. In deep fading, the UE may struggle to detect the signal. Robust sequence design and possible repetition can help, so we must design SSS for realistic channel conditions.

How should synchronization coexist with NR (MRSS scenarios)?

In MRSS, NR and 6GR share the same or nearby spectrum. Their synchronization signals may overlap in time and frequency, so we must let UEs distinguish the RATs reliably. We also need to avoid interference and excessive UE complexity. Reusing existing structures may improve efficiency, but we must balance compatibility, complexity and performance.

Should 6G reuse NR synchronization raster?

Reusing the NR synchronization raster lets the UE search NR and 6GR through a common structure. This can simplify implementation, but the UE must still distinguish the two systems.

What happens if raster locations are aligned?

If NR and 6GR share raster locations, the UE can reuse search results and reduce scanning effort. That simplifies implementation. But signals from the two RATs may overlap, increasing misdetection risk. We therefore need additional mechanisms to distinguish them.

How can interference with NR devices be avoided?

To maintain reliable detection, we need to minimize interference between NR and 6GR signals. Signal design and resource allocation must provide separation or orthogonality where possible.

How can orthogonality between sequences be ensured?

6GR synchronization sequences should have low cross-correlation with NR sequences to reduce detection interference. Through careful sequence design, we can achieve orthogonality and let the UE distinguish signals from different RATs. This improves detection performance.

What is the impact of alignment on UE complexity?

Alignment changes how the UE searches NR and 6GR. It can reduce or increase complexity depending on the implementation, so we must design the alignment carefully.

Aligned structures can reduce search effort.

If both systems use similar timing and frequency structures, the UE can reuse detection results. Fewer independent searches then reduce processing load and improve efficiency.

But misdetection risk may increase.

With overlapping signals, the UE may mistake one RAT for the other, increasing false detection risk. It may need extra processing to resolve the ambiguity. That processing can offset the complexity saving from alignment.

What mechanisms prevent false detection of 6GR signals by legacy NR UEs?

Legacy NR UEs should not mistake 6GR synchronization signals for valid NR signals. We need mechanisms that preserve backward compatibility and prevent interference.

How can sequence orthogonality be enforced?

We can design 6GR sequences with low correlation to NR sequences, reducing false detection by an NR UE. Careful sequence-set selection is necessary to maintain compatibility between the systems.

How can detection thresholds be adjusted?

Detection algorithms use thresholds to decide whether a signal is valid. Adjusting those thresholds can reduce false detection, possibly through tuning for expected signal properties. We need appropriate thresholds to maintain reliability.

How does partial frequency proximity (within CFO range) affect coexistence?

If NR and 6GR signals are close in frequency, CFO can make them harder to distinguish and separate. We therefore need to account for frequency proximity in the design.

How does CFO blur separation between RATs?

Frequency offset spreads signal energy across subcarriers. If two signals are close in frequency, that spreading can make them overlap. The UE may then struggle to distinguish them, making detection harder.

How does this impact detection reliability?

Overlap and interference make correlation peaks less clear, increasing missed or false detection probability. Detection algorithms must account for this. We therefore need to consider CFO in both sequence design and frequency planning.

Should resource block boundaries align between NR and 6GR?

On a shared NR and 6GR carrier, we need to align more than the synchronization raster. Both systems must also agree on resource block boundaries. Aligning those boundaries has broad support, but the discussion has not settled the point yet.

What breaks when the grids do not align?

Misaligned grids create partial overlaps between NR and 6GR resource blocks. The network must then protect fractional resources rather than whole blocks. Scheduling and rate matching become harder, and more shared blocks mean more wasted edge resources.

Why settle this explicitly?

A large group of companies jointly asked for a specification guarantee of alignment instead of leaving it to implementation. They view shared-carrier coexistence as a deployment requirement. A specification guarantee would make that requirement predictable.

What remains open?

We still have two open questions. Does alignment apply only to downlink, or to both directions? Also, how would it work with a half-subcarrier shift of the uplink centre frequency? That shift moves the uplink grid away from the NR grid.

What information should be delivered via PBCH/MIB?

PBCH/MIB gives the UE its first system information after synchronization. We need enough information for timing alignment, frequency alignment and further system information acquisition. But a larger MIB increases decoding complexity and latency. We must therefore keep PBCH/MIB minimal while giving the UE enough information to continue access.

What essential information must UE obtain during initial access?

During initial access, the UE needs a small set of critical parameters to align with the network and prepare for further decoding. We should include only essential information at this stage.

How is timing information conveyed?

Timing information lets the UE align its frame and symbol boundaries with the network. Parameters such as frame timing and subframe structure tell it how to interpret subsequent transmissions. Without accurate timing, the UE cannot continue decoding. Timing information is therefore one of the most critical MIB elements.

How is frequency alignment provided?

The UE first gains frequency alignment through synchronization signals. PBCH then provides configuration that refines the alignment, possibly including subcarrier spacing and frequency-resource information. The UE uses this information for correct demodulation. Reliable decoding requires accurate frequency alignment.

What configuration is needed for system information acquisition?

After decoding MIB, the UE must locate and decode further system information such as SIB1. It needs configuration parameters that tell it where and how to find that information.

How does UE locate SIB1?

MIB tells the UE about SIB1 location and scheduling, possibly including its time and frequency resources. The UE can then monitor the correct resources. Without that guidance, it would need blind search, so this information is essential for efficient access.

What control channel information is required?

The UE needs basic PDCCH configuration to decode control channels and interpret scheduling grants. Without it, the UE cannot receive further system information. We must therefore include the basic control channel configuration needed for that step.

Should additional features be supported?

We also need to consider whether MIB should support additional features. They may improve energy efficiency or coverage, but increase MIB size and complexity. We must justify each addition carefully.

How can energy efficiency (NES) be incorporated?

Energy saving may need extra configuration, such as parameters for reduced monitoring or on-demand signaling. These can help the UE operate more efficiently, but also enlarge MIB. We should therefore include only essential parameters.

How can coverage extension be supported?

Coverage extension may need repetition or configuration parameters that help the UE detect weak signals. Including them can improve cell-edge performance, but also increases complexity. We must weigh those costs against the coverage benefit.

How should information be split between SS, PBCH, and DMRS?

We should distribute information across signals and channels to balance complexity and performance. Keep early signals simple, then provide more detailed information later.

Which layer carries timing vs configuration?

Let's separate the functions. Synchronization signals provide timing and coarse identification, while PBCH carries essential configuration. DMRS supports channel estimation for decoding. Each component then handles a specific task, simplifying processing at each stage.

How does split affect decoding latency?

With information split across stages, the UE must finish one step before starting the next. This sequential processing can increase latency, although each step becomes simpler. We must therefore balance latency against processing efficiency.

What information enables new delivery methods (e.g., on-demand SI, fixed PDSCH)?

New system information delivery methods need additional configuration. The UE must know how to use those methods before it can obtain the information.

What parameters must UE know in advance?

The UE must know the scheduling patterns and resource locations for system information channels. For on-demand delivery, it may also need trigger conditions. Without these parameters, it cannot use the new delivery methods, so we must include the key parameters in MIB.

How does this impact MIB size?

Each new parameter enlarges MIB, requiring more resources for transmission and decoding. That increases latency and complexity. We should keep only critical parameters in MIB and deliver other information later to preserve efficiency.

How should PBCH be designed for performance?

PBCH must remain decodable under difficult conditions, especially at the cell edge. But it must also avoid excessive delay and UE complexity. We therefore need to balance payload size, repetition and combining efficiency. The goal is high decoding probability with manageable latency and processing, supporting coverage and reliable initial access.

How does PBCH allocation size affect coverage?

Consider what happens when we give PBCH more symbols or bandwidth. The transmitter can spread coded information across more resources, improving the SNR required for one-shot decoding. But the larger allocation consumes more SSB resources. The UE may also need to test more allocation hypotheses.

< R1-2603518 : Figure 9. Illustration of considered PBCH allocation options >

What benefit comes from adding more PBCH symbols?

Adding one or two OFDM symbols to PBCH gives a clear coverage benefit. For the 20 PRB case, compare the NR-like 48 PRB allocation with 68 PRBs. The required SNR improves by about 1.5 dB. At 88 PRBs, the improvement reaches about 2.4 to 2.5 dB. Symbol extension therefore gives us an effective way to improve one-shot PBCH reliability.

What benefit comes from increasing PBCH bandwidth?

With the symbol count unchanged, increasing PBCH bandwidth from 20 PRBs to 24 PRBs gives roughly 0.9 dB SNR improvement. Adding symbols as well gives a larger gain. The 108 PRB case combines 24 PRBs with two additional symbols. Its required SNR improves by about 3 dB against the NR-like baseline.

What is the design trade-off?

Larger PBCH allocations improve coverage but enlarge the SSB structure. Consider a 24 PRB PBCH with small channel bandwidth and blind detection: the UE may need many allocation hypotheses. We must therefore assess UE search complexity and SSB size as well as SNR. We also need to check whether PSS/SSS lets the UE derive PBCH location without excessive hypotheses.

What is the role of PBCH payload size?

PBCH payload size determines how much information the Master Information Block delivers. A larger payload adds functionality but makes decoding harder. A smaller payload improves robustness but limits flexibility, so we must choose the size carefully.

How does payload size affect coverage?

A larger payload means the network transmits more bits, increasing the SNR needed for successful decoding. Coverage may then decrease, especially at the cell edge. Reducing payload size improves decoding probability under weak signals and can therefore improve coverage.

What is PBCH combining and why is it important?

With PBCH combining, the UE combines multiple receptions of the same signal to improve decoding reliability. This is a key coverage technique, especially at low SNR.

How does combining improve detection?

The UE combines multiple transmissions to increase effective signal energy and improve the signal-to-noise ratio. Decoding then becomes more reliable, especially at the cell edge where signal strength is low.

What conditions enable efficient combining?

Efficient combining needs sufficiently similar transmissions, including consistent channel conditions and timing alignment. Large channel variation reduces combining efficiency. We must therefore account for channel stability and coherence time when designing the transmission.

How much does combining actually recover at 7 GHz?

Let's compare PBCH link budgets at 7.0 GHz and 5G mid-band. For urban macro outdoor-to-indoor, the gap is about 8.3 dB without combining. When the UE combines four transmissions, that gap falls to about 3.1 dB.

How does this change the role of combining?

Combining recovers roughly 5 dB for PBCH and about 3 dB for PSS/SSS. Most other candidate techniques currently offer less margin. We therefore need to treat combining as a basic coverage design assumption, rather than a later refinement.

Should PBCH payload be time-invariant?

Consider keeping the PBCH payload constant over time. That would simplify combining and reduce UE complexity, but may limit flexibility when updating system information.

How does this simplify UE processing?

If repeated payloads stay identical, the UE can combine signals without extra processing to track content changes. This reduces complexity and improves decoding efficiency. A time-invariant payload therefore supports a simpler UE implementation.

How does PBCH repetition pattern affect latency vs coverage trade-off?

More repetitions improve PBCH decoding reliability and coverage, but extend transmission time and delay access. We need to choose the repetition pattern carefully to balance coverage and latency.

How does repetition improve decoding probability?

Each repetition gives the UE another chance to receive the signal. The UE can combine these receptions to improve SNR and decoding probability. Repetition therefore provides an effective coverage enhancement.

How does it increase access delay?

Repetition extends transmission over a longer period. The UE may need to wait for several repetitions before it can decode, increasing access latency. We therefore gain reliability at the cost of access delay.

How does clustering of PBCH occasions impact combining efficiency?

Now consider the timing between PBCH transmissions. Clustering places several transmissions close together, which can improve combining efficiency under suitable conditions.

How does time proximity affect diversity gain?

Closely spaced transmissions see similar channel conditions, allowing coherent combining. But the small channel variation may limit diversity gain. Spreading transmissions over time increases diversity while making combining more complex. We therefore need to balance coherence against diversity when choosing transmission timing.

How does it influence UE buffering requirements?

The UE must buffer received signals before combining them. Widely spaced transmissions require longer buffering and more memory. Clustering reduces that requirement and can therefore simplify UE implementation.

How should synchronization periodicity and patterns be designed?

Synchronization periodicity and transmission patterns directly affect energy efficiency, access latency, and UE complexity. The key point is that increasing periodicity is a key method to reduce always-on signaling. But this introduces trade-offs in detection delay and processing effort. In addition, the structure of transmission patterns, such as clustering, affects combining efficiency and resource usage. So periodicity and pattern design must balance energy saving, performance, and resource efficiency.

What are the benefits of increasing SS/PBCH periodicity?

Increasing periodicity reduces how often synchronization signals are transmitted. This lowers network activity and improves energy efficiency. This is a key 6GR design goal.

How does it improve energy efficiency?

When synchronization signals are transmitted less frequently, the network can remain idle for longer periods. That reduces transmit power usage. It also enables deeper sleep modes. Over time, this leads to significant energy savings. This is especially important in large-scale deployments.

How much energy does a longer period actually save?

Evaluations from a large group of companies now quantify the saving against a 20 ms baseline. On an unloaded carrier, moving to 80 ms or 160 ms saves a large fraction of network energy. The largest reported gains run from about 40 to 87 per cent, and they vary with the sleep state assumed.

Why does load change the picture?

The gains fall steeply once traffic is present. Evaluations that report more than 50 per cent at zero load report under 10 per cent at 15 per cent load. Sleep opportunities come from the gaps between transmissions, and traffic fills those gaps whatever the synchronization period.

Does clustering always help?

No. Some evaluations show clustering roughly doubling the saving, and others show it reducing the saving and even turning it negative under light and medium load. The benefit depends on whether the clustered occasions fall inside sleep opportunities that the traffic leaves free.

What are the drawbacks?

Although increasing periodicity improves energy efficiency, it introduces several challenges. These include increased latency and higher UE complexity. So we must carefully consider these trade-offs.

How does it affect latency?

With longer periodicity, the UE may need to wait longer for the next synchronization signal. If the UE arrives just after a transmission, it must wait until the next one. That increases access delay. In worst cases, the delay equals the full periodicity. So latency becomes less predictable.

How does it increase UE complexity?

Longer periodicity increases uncertainty in timing. The UE needs to search over a wider time window. That increases the number of time hypotheses. Combined with frequency hypotheses, the search space grows. So processing complexity increases. This also increases power consumption.

What is clustered transmission?

Clustered transmission refers to grouping multiple synchronization signal transmissions within a short time interval. Instead of spreading transmissions evenly, they are concentrated in a cluster. This structure can improve combining efficiency and reduce UE complexity in some cases.

How do multiple repetitions within a period help?

Multiple transmissions within a short interval allow the UE to combine signals effectively. Since channel conditions are similar, combining is more efficient. That improves detection probability. It also reduces the need for long-term buffering. So clustering can enhance reliability.

What are the trade-offs in time diversity and overhead?

Clustering reduces time diversity because transmissions occur close together. If channel conditions are poor, all repetitions may be affected. Spreading transmissions increases diversity but reduces combining efficiency. Clustering also increases short-term resource usage. So there is a trade-off between diversity and overhead.

How does synchronization periodicity affect measurement accuracy?

Synchronization signals are also used for measurements such as RSRP. So periodicity affects how often the UE can update measurements. The key point is that sparse signaling may impact measurement accuracy.

How does sparse signaling impact tracking?

With less frequent signals, the UE has fewer opportunities to update measurements. This makes tracking of channel conditions less accurate. Rapid changes in the channel may not be captured. This can affect link adaptation and synchronization maintenance. So measurement accuracy decreases with sparse signaling.

How does it affect mobility decisions?

Mobility decisions rely on measurement reports. If measurements are infrequent or inaccurate, decisions may be delayed or incorrect. This can lead to suboptimal handovers. In extreme cases, connection quality may degrade. So periodicity must support reliable mobility performance.

How does clustering impact resource availability for data transmission?

Synchronization signals occupy time-frequency resources that could otherwise be used for data. Clustering affects how these resources are distributed over time. The key point is that this has implications for system throughput.

How much time-domain resource is blocked?

When transmissions are clustered, a larger portion of resources is occupied in a short time. That creates a temporary reduction in available resources for data. Outside the cluster, resources are free. So resource usage becomes uneven over time.

How does this affect throughput?

Short-term clustering may reduce instantaneous throughput during transmission periods. But longer idle periods allow efficient scheduling of data. Overall throughput impact depends on how resources are managed. So clustering must be designed to minimize negative impact on data transmission.

How should SSB candidate locations be patterned?

SSB candidate locations are important because the UE must know where synchronization opportunities may appear before it can search efficiently. In NR, these locations are fixed within a slot and help the UE establish timing. For 6GR, fixed candidate locations can still be useful, especially if PBCH combining or SSB beam-sweep combining is expected. Without predictable candidate locations, the UE may need to test more timing hypotheses, which increases complexity.

Why keep fixed candidate time locations?

Fixed locations reduce uncertainty. They allow the UE to search only a limited set of symbol and slot positions instead of scanning a wider time window. This is useful when SSB periodicity is extended, because fewer SSB occasions are available and each missed opportunity has a larger latency impact. So fixed candidate locations can help preserve detection reliability while keeping UE processing manageable.

How should symbol-level SSB locations be simplified?

NR symbol-level SSB locations were selected with several constraints in mind, including DL control at the start of a slot, UL control near the end of a slot, mixed numerology, mini-slot operation, and different TDD cases. For 6GR, some of these constraints may be relaxed. For example, if mixed numerology is not a prerequisite in FR1, the reserved symbols for DL control and SSB placement can be reconsidered.

< R1-2603518 : Figure 10. Illustration of NR SSB candidate locations within a slots for μ={0,1,2,3,4} >

Why are two SSB locations per slot attractive?

Using two SSB candidate locations in a slot can reduce beam sweep duration. This is useful when many beams must be transmitted and when the system wants to limit the total synchronization sweep time. Reserving a small number of symbols for DL control can still help multiplex SSB with other transmissions, but reserving UL control symbols at the end of every slot may not be needed in most slots.

Is a gap between SSBs needed?

A gap between SSB candidates does not appear necessary for many FR1 and FR2-1 cases. Avoiding unnecessary gaps can simplify the symbol pattern and make the candidate locations more compact. Special slots still need separate handling, because UL symbols and guard periods can reduce the number of usable SSB locations.

How should slot-level SSB patterns handle TDD constraints?

Slot-level SSB patterns must be aligned with the actual UL/DL slot structure. A pattern that works for one TDD configuration may collide with special slots or UL symbols in another configuration. So the relationship between SSB slot pattern and TDD UL/DL slot pattern needs to be considered explicitly in 6GR.

< R1-2603518 : Figure 11. Illustration of NR slot level SSB pattern for μ={0,1,2,3,4} >

< R1-2603518 : Figure 12. Illustration of required number of slots to reach actual number of possible SSB candidate locations, Lactual, equal to {16,32} with two alternative TDD patterns >

Why can NR-style slot patterns be difficult for 6GR?

In NR, SSBs can be placed across a set of continuous slots. This is simple, but it may not align well with practical TDD patterns such as slots reserved for UL or special-slot operation. If some candidate locations cannot be used, the effective number of SSB occasions is reduced. This can limit the number of beams supported within a target time window such as 5 ms.

Should the pattern be TDD-agnostic?

A TDD-agnostic SSB pattern is simpler from a specification perspective, but it may require more candidate locations to guarantee enough actually usable SSB occasions. A TDD-aware pattern can reduce unused candidates, but it depends on the configured UL/DL pattern and special-slot assumptions. So there is a trade-off between common design and efficient use of usable slots.

How do SSB repetitions affect combining and beam sweep duration?

If repetitions are transmitted before completing a beam sweep, the UE may be able to combine repeated SSBs more easily. But this can extend the time required to cover all beams. If the beam sweep is completed first and then repeated, the measurement gap may be shorter, but the UE may need additional assumptions about repetition timing and candidate locations.

Why does this matter for inter-frequency measurements?

Inter-frequency measurements are sensitive to the total duration of the synchronization sweep. If SSB repetitions cause the sweep to exceed the available measurement gap, the UE may not be able to measure all beams efficiently. So the mapping between repetitions and SSB candidate locations must consider both combining gain and measurement feasibility.

How should clustered SSB transmissions be used?

Clustered SSB transmission is a way to support long SSB periodicity while still giving the UE multiple synchronization opportunities within a shorter interval. Instead of spreading individual SSB occasions uniformly over time, the network can transmit a cluster at the start of a longer period. This can reduce the average latency for acquiring enough SSB occasions for measurements or PBCH decoding.

< R1-2603518 : Examples of Clustered SSB Transmission >

< R1-2603518 : Figure 14. Illustration of (a) legacy NR SSB transmission pattern and (b) clustered SSB transmission pattern >

< R1-2603518 : Figure 15. Illustration of possible combining over clusters and within a cluster based on Pcluster and Pburst >

What terminology is useful for clustered SSB?

The useful distinction is between an SSB burst, an SSB burst set, and repetitions within the burst set. A 6GR SSB burst can contain multiple 6GR SSBs, while a burst set can contain one or more bursts. Repetitions may occur consecutively or with time separation inside the cluster. These definitions help describe whether the UE is seeing different beams, repeated transmissions, or multiple burst groups.

How can clustering reduce latency?

When the SSB periodicity is extended to values such as 80 ms or 160 ms, a UE may otherwise wait a long time to collect enough SSB occasions. Clustering can place multiple occasions near the beginning of the period. This can reduce average acquisition latency, especially when the UE needs several SSB occasions for measurement, tracking, or PBCH combining.

What UE assumptions are needed for clustered SSB operation?

Clustered operation should minimize the amount of prior information required by the UE. For PSS detection, the UE can still perform blind search without strict assumptions about the cluster. For SSS detection, the UE may use the detected PSS timing and then try SSS detection based on the expected SSB structure. For PBCH combining, more information is useful because the UE needs to know which SSBs can be combined.

What does the UE need to know for PBCH combining?

The UE benefits from knowing the SSB cluster periodicity and the repetition periodicity within the cluster. With this information, it can combine PBCH over likely repetition candidates. Without it, the UE may need to test more hypotheses across SSB burst locations, which increases complexity. So clustered SSB operation should define enough timing structure to make combining practical.

Can repeated SS/PBCH occasions be self-detectable?

Repeated synchronization signals and channels can help create self-detectable or decodable SS/PBCH occasions within a cluster. This is useful because the UE may not need full prior knowledge of every repetition if the repeated occasions can be detected from the signal structure itself. The design still needs to define how many repetitions are used, whether all repetitions are identical, and how far apart they are placed.

Is the periodicity gain real in deployment?

The evaluations above are simulations, and one line of argument at RAN1 #126 asks whether they survive contact with a network. The objection is not that the model is wrong. It is that the gain depends on the base station implementation, that it shrinks under load, and that no operator has yet deployed the periodicities being proposed.

What do deployments actually use?

No known commercial deployment uses an SSB periodicity longer than 20 ms, apart from one regional deployment at 40 ms. The proposals under study reach 80 ms and 160 ms. They are therefore four to eight times beyond anything currently in service.

How much is left under load?

Around 8 per cent, and about 3.5 per cent for the shallower sleep state, once the cell carries 15 per cent load. Those values sit at the low end of the reported range, and they are the ones a loaded network would see.

What has not been shown?

Mobility performance beyond 40 ms is unproven rather than disproven. Longer periods also cost connected UE throughput, because the same occasions carry measurement for UEs that are not idle. Both points are raised as evidence gaps, not as objections in principle.

How does beam-based operation impact synchronization?

Beam-based operation is a fundamental feature of 6GR, especially at higher frequencies. The key point is that synchronization must now be performed across multiple spatial directions. This significantly impacts how synchronization signals are transmitted and detected. Instead of omnidirectional transmission, signals are beamformed and swept across directions. That increases both transmission overhead and UE search complexity. So synchronization design must consider beam management as a core factor.

Why are more beams needed at higher frequencies?

At higher frequencies, signal propagation characteristics change. Path loss increases and signals become more directional. So beamforming is required to maintain coverage. This leads to an increase in the number of beams that must be supported.

How does antenna scaling affect beam count?

Higher frequencies allow more antenna elements to be packed into the same physical area. This enables narrow beamforming. Narrow beams provide higher gain but cover smaller spatial regions. So more beams are required to cover the entire cell. That increases the number of beam directions that must be scanned during synchronization.

How many SS/PBCH beams should be supported?

The number of SS/PBCH beams determines how many directions synchronization signals are transmitted. The proposal is that increasing beam count improves coverage but increases overhead. So the number of beams must be carefully chosen.

How does this compare with NR?

NR already supports beam sweeping for synchronization, especially in FR2. But 6GR may require even more beams due to higher frequencies and narrower beams. That increases both transmission and detection complexity. So scaling beyond NR requires new optimizations.

How many broadcast beams may be needed around 7 GHz?

For new frequency ranges around 7 GHz, one objective is to keep a similar site grid to existing FR1 deployments. That requires enough antenna gain to compensate for the higher path loss compared with lower FR1 frequencies. If the physical array size remains similar, the number of antenna elements can increase, which narrows the beamwidth and increases the number of beams needed to cover the same area.

Why can the beam count increase?

If the number of antenna elements is doubled in both horizontal and vertical dimensions, the number of possible beams can increase by about four times. So if FR1 supports up to eight SS/PBCH beams, a similar coverage target around 7 GHz may require support for 16 or 32 SS/PBCH beams. That improves coverage but also increases synchronization overhead and UE search effort.

How does subcarrier spacing affect beam transmission?

Subcarrier spacing affects symbol duration and transmission timing. This has implications for beam sweeping and synchronization overhead. The key point is that higher SCS can reduce time-domain overhead.

Why is 240 kHz SCS beneficial in FR2?

Higher SCS results in shorter symbol duration. That allows faster transmission of synchronization signals. In beam sweeping, this reduces the total time required to cover all beams. So higher SCS improves efficiency in high-frequency scenarios.

How does it reduce time-domain overhead?

Shorter symbols mean that each beam transmission occupies less time. When multiple beams are transmitted sequentially, total duration decreases. That reduces synchronization overhead. So access latency can be improved.

Why consider 240 kHz SCS for SS/PBCH in FR2-1?

For FR2-1 operation, especially when 120 kHz SCS would otherwise be used, 240 kHz SCS can reduce the time-domain footprint of SS/PBCH. This is useful for analog beamforming, where many SSB beams may need to be swept. If 64 SSB beams are transmitted, using 240 kHz SCS can reduce the burst duration compared with 120 kHz SCS.

< R1-2603518 : Figure 16. Mapping of SSBs with 120 and 240 kHz SCSs, respectively, in a 14-symbol slot defined by 120 kHz SCS >

< R1-2603518 : Figure 17. NR mapping of SSBs into 14 symbol slots with 120 and 240 kHz SCSs within 5 ms half-frame >

How does it change SSB mapping?

With 120 kHz SCS, fewer SSBs fit into a 14-symbol slot. With 240 kHz SCS, more SSBs can be mapped into the same slot duration because each symbol is shorter. So the number of affected slots within a given time window can be reduced. That creates more opportunities for scheduling PDSCH and other downlink transmissions.

How does SS/PBCH SCS affect downlink resource overhead?

SSB transmissions are usually broadcast and periodic. They are intended to cover the cell even when there may not be active UEs under every beam. In analog beamforming, this can limit data multiplexing because the beam direction used for SSB may not match the beam direction needed for PDSCH. So the time-domain footprint of SSB directly affects available downlink resources.

Why is FDM with PDSCH limited?

FDM between SSB and PDSCH is not always practical with analog beamforming. The gNB may only be able to transmit data toward a beam direction aligned with one of the SSBs in the slot. For other SSB beams, the same symbols may effectively be unavailable for PDSCH scheduling. This makes the SSB footprint a real cell-level overhead, not only a physical resource mapping issue.

What overhead gain is expected from 240 kHz SCS?

The numerical examples show that 240 kHz SCS can reduce the percentage of downlink resources affected by SSB compared with 120 kHz SCS. For a 64-beam, 20 ms SSB burst case, 120 kHz SCS affects about 32 out of 160 slots, while 240 kHz SCS affects about 16 out of 160 slots. That reduces SSB-related overhead and improves the amount of resource available for PDSCH.

What is the design implication?

For FR2-1 and possibly upper bands around 15 GHz, 240 kHz SCS for SS/PBCH can improve spectral efficiency by reducing the SSB time-domain footprint. The trade-off is that we must still align with the SCS assumptions for other channels and signals in the band. So SS/PBCH SCS should be selected together with beam count, analog beamforming constraints, and downlink multiplexing assumptions.

How does analog beamforming limit multiplexing of SSB and data?

In analog beamforming, a single beam direction is typically used at a time. This limits the ability to transmit multiple signals simultaneously in different directions. The key point is that this constraint affects how synchronization and data transmissions are scheduled.

Why is FDM not fully applicable?

Frequency division multiplexing assumes that different signals can be transmitted simultaneously in the same time slot. But with analog beamforming, the antenna can only focus in one direction at a time. So transmitting synchronization and data simultaneously in different beams is not straightforward. This limits multiplexing flexibility.

How does beam direction constrain scheduling?

Since only one beam direction can be active at a time, transmissions must be scheduled sequentially. This affects both synchronization and data transmission. The network must decide when to transmit synchronization signals versus data. This introduces scheduling constraints and potential inefficiencies.

How does beam count scaling affect synchronization overhead?

As the number of beams increases, synchronization overhead also increases. Each beam requires its own transmission opportunity. This affects both network resource usage and UE search effort.

How do more beams increase transmission overhead?

Each beam requires a separate transmission of synchronization signals. As the number of beams increases, total transmission time increases. This consumes more time-frequency resources. So less resource is available for data transmission.

How does it affect UE search time?

The UE needs to search across all possible beam directions. That increases the number of hypotheses. Detection time increases as more beams are scanned. This also increases UE power consumption. So beam count directly impacts UE complexity and energy usage.

How should broadcast channels be delivered?

Broadcast channel delivery is a key part of initial access and system operation. The key point is that traditional always-on broadcast mechanisms may not be efficient for 6GR. Instead, more flexible delivery methods are needed. These include periodic and on-demand approaches. We must balance energy efficiency, latency, and UE complexity. It must also support different deployment scenarios and scalable system information delivery.

What are the options for system information delivery?

System information can be delivered using different methods depending on system requirements. The discussion covers both periodic and on-demand approaches. Each method has its own advantages and trade-offs.

What is on-demand SIB1?

On-demand SIB1 is transmitted only when needed. The UE may request system information or receive it based on network triggers. That reduces unnecessary transmissions. So network energy consumption decreases. But it requires additional signaling and coordination. This may introduce additional latency.

What is periodic SIB1?

Periodic SIB1 is transmitted at regular intervals regardless of UE demand. This lets the UE that the UE can access system information without requesting it. It simplifies UE operation. But it leads to continuous transmission even when no UE is present. That reduces energy efficiency.

How does on-demand delivery improve energy efficiency?

On-demand delivery reduces unnecessary broadcast transmissions. The network transmits system information only when required. This aligns with 6GR goals of reducing always-on signaling. But it introduces trade-offs in latency and complexity.

What are the latency trade-offs?

With on-demand delivery, the UE may need to wait for system information to be transmitted after a request. That increases access delay compared to periodic transmission. The delay depends on scheduling and signaling procedures. So energy efficiency is improved at the cost of increased latency.

What deployment scenarios must be supported?

Broadcast delivery must work across different deployment scenarios. The key point is that standalone and multi-cell environments have different requirements. So we must be flexible.

Standalone vs multi-cell scenarios?

In standalone scenarios, the UE relies entirely on a single cell for system information. So broadcast delivery must be self-contained. In multi-cell scenarios, system information may be distributed across cells. One cell may provide synchronization, while another provides system information. That allows more flexible and efficient delivery. But it requires coordination between cells.

How can area-specific system information be supported?

The proposal is that system information may vary across geographic areas. So mechanisms are needed to deliver area-specific information efficiently. This avoids unnecessary transmission of irrelevant information.

How is geographic segmentation handled?

The network can divide coverage into smaller regions. Each region may have different system information. The UE needs to identify which region it belongs to. That allows it to receive only relevant information. That reduces overhead and improves efficiency.

How does UE identify relevant SI?

The UE may use identifiers or indicators provided during initial access. These indicators point to the correct system information. The UE then monitors specific resources for relevant data. This avoids unnecessary decoding of unrelated information. So UE processing load is reduced.

What mechanisms allow UE to request system information without anchor cells?

In flexible architectures, the UE may not rely on a fixed anchor cell. Instead, it may need to request system information dynamically. We therefore need mechanisms that support this operation.

What signaling is needed for request?

The UE may send a request signal indicating its need for system information. This signal must be simple and efficient. The network then schedules the delivery of requested information. This enables on-demand operation without continuous broadcasting.

How is configuration obtained initially?

Initial configuration must be provided through synchronization signals or minimal broadcast information. That allows the UE to know how to request further information. Without this, the UE cannot initiate the request process. So a minimal bootstrap mechanism is required.

How can coverage be extended for broadcast channels?

Coverage extension is a key requirement for 6GR, especially for scenarios such as LPWA and NTN. The key point is that broadcast channels must be decodable even under very low signal conditions. That requires techniques that improve detection probability without excessive complexity. At the same time, these techniques must not significantly increase latency or resource usage. So coverage extension design involves trade-offs between robustness, efficiency, and delay.

How large is the coverage gap at 7 GHz?

Coverage extension needs a target, and link budget work now supplies one. The signals used during initial access were compared at 7.0 GHz against 5G mid-band at 3.5 GHz. The values below are trimmed means across many independent evaluations, so they carry more weight than a single simulation.

The size of the gap depends on whether the UE is indoors, and the difference is large.

A negative value means worse coverage at 7 GHz. Outdoors the gap almost disappears, and combining turns it positive. Indoors it remains several decibels even after combining. Building penetration, rather than the carrier frequency alone, drives the requirement.

Which channel sets the hardest target?

The uplink does. Msg3 PUSCH sits about 8.7 dB behind mid-band for urban macro outdoor-to-indoor, and Msg5 PUSCH about 13 dB. Downlink broadcast is therefore not the binding constraint, although it must still close its own gap.

What techniques enable coverage extension?

Several techniques can be used to improve coverage. These include repetition, power boosting, and simplified transmission structures. The discussion focuses on repetition as a primary method. But the choice of technique depends on system constraints.

Should repetition be used?

Repetition increases the probability that the UE successfully receives the signal. Multiple transmissions allow combining, which improves effective SNR. This is particularly useful at the cell edge. But repetition increases transmission time and resource usage. So it improves coverage at the cost of latency and efficiency.

Should PDCCH and PDSCH both be repeated?

Both control and data channels may require repetition to ensure reliable decoding. Repeating only one may not be sufficient. For example, if PDCCH is not reliably decoded, the UE cannot access PDSCH. So both may need to be repeated in coverage-limited scenarios. That increases overhead but ensures end-to-end reliability.

When is fixed allocation preferable?

Fixed allocation refers to predefined resource locations for broadcast channels. This simplifies UE operation because it eliminates the need for dynamic scheduling. The proposal is that this can be beneficial in coverage-limited scenarios.

How does it simplify UE processing?

With fixed allocation, the UE knows exactly where to look for broadcast channels. This eliminates the need to decode control information first. It reduces the number of search steps. So UE complexity and power consumption decrease. This is especially useful for low-power devices.

How does coverage extension differ between LPWA and NTN scenarios?

LPWA and NTN scenarios have different characteristics. So coverage extension techniques must be adapted accordingly. Their propagation conditions and delay constraints differ.

How do propagation conditions differ?

In LPWA scenarios, coverage extension is needed for indoor or deep coverage. Signal attenuation is moderate but persistent. In NTN scenarios, signals travel very long distances. Path loss and Doppler effects are significant. So NTN requires more robust techniques such as higher repetition or stronger signals. LPWA may rely more on simplified processing.

How do delay constraints change?

LPWA applications may tolerate higher latency due to low data rate requirements. NTN scenarios also tolerate delay due to long propagation time. But the reasons differ. In NTN, delay is inherent to the system. In LPWA, delay is a design choice. So periodicity and repetition can be adjusted differently for each case.

How does extremely long periodicity (e.g., 160 ms) impact synchronization strategy?

The discussion considers very long periodicity as a way to reduce energy consumption. But this introduces new challenges for synchronization and detection. The UE needs to adapt its behavior to handle such conditions.

How does it require one-shot detection?

With very long periodicity, the UE may only have a single opportunity to detect the signal within a long interval. It cannot rely on combining multiple closely spaced transmissions. So detection must succeed in one attempt. That requires strong signal design and robust detection algorithms.

How does it affect UE wake-up timing?

The UE needs to carefully schedule its wake-up time to coincide with signal transmission. If it misses the transmission, it must wait for the next period. That increases latency. Accurate timing knowledge is required to avoid unnecessary wake-ups. So synchronization and scheduling become more critical.

What additional signals are needed for synchronization?

The key point is that relying only on SS/PBCH may not be sufficient for all scenarios in 6GR. As periodicity increases and energy efficiency becomes a priority, additional synchronization signals may be needed. These signals can support specific functions such as paging, system information acquisition, and RACH. They can also enable more flexible and efficient operation. So we must consider when and how to introduce additional signals without increasing unnecessary complexity.

When are on-demand synchronization signals useful?

On-demand synchronization signals are transmitted only when needed. That reduces continuous transmission and improves energy efficiency. The proposal is that such signals can be useful in specific procedures where synchronization must be refreshed.

For paging?

Before paging, the UE may need to re-align with the network. On-demand synchronization signals can be transmitted just before paging occasions. That allows the UE to wake up and synchronize only when needed. So UE power consumption is reduced. It also reduces the need for continuous monitoring of SS/PBCH.

For system information acquisition?

When the UE needs to acquire system information, synchronization may need to be refreshed. On-demand signals can provide this support. That allows the UE to avoid continuous scanning. It also improves efficiency in idle or inactive states. So on-demand synchronization supports flexible system information delivery.

Are additional signals needed for RACH?

Random access requires accurate timing alignment. The discussion considers whether additional synchronization support is needed for this procedure. This depends on the limitations of existing mechanisms.

What are the limitations for Msg1?

Msg1 is transmitted without full timing alignment. This can lead to timing uncertainty. In challenging conditions, this may reduce success probability. Additional synchronization signals can help refine timing before RACH. That improves reliability of initial access attempts.

How can synchronization be maintained in carrier aggregation?

In carrier aggregation, multiple carriers may be used simultaneously. Not all carriers may transmit SS/PBCH. So synchronization must be maintained across carriers using alternative methods.

What is SS/PBCH-less SCell operation?

In some cases, secondary cells may not transmit SS/PBCH. The UE relies on synchronization from the primary cell. Additional signals such as CSI-RS may be used to maintain synchronization. That reduces overhead on secondary carriers. But it requires coordination between cells.

What is the trade-off between UE-triggered and network-triggered synchronization signals?

Synchronization signals can be triggered either by the UE or by the network. Each approach has advantages and disadvantages. The key point is that the choice depends on system requirements.

When is each approach beneficial?

UE-triggered signals are useful when synchronization is needed on demand. That reduces unnecessary transmissions. Network-triggered signals are useful for scheduled operations such as paging. They provide predictable timing. So each approach is suitable for different scenarios.

How does signaling overhead differ?

UE-triggered signals require additional signaling for request and response. That increases control overhead. Network-triggered signals avoid this overhead but may result in unnecessary transmissions. So there is a trade-off between flexibility and efficiency.

How does wake-up signal (WUS) interaction affect synchronization design?

Wake-up signals are used to reduce UE power consumption. They allow the UE to wake up only when needed. The proposal is that WUS must be coordinated with synchronization signals.

How does WUS reduce UE power consumption?

WUS allows the UE to remain in low-power mode most of the time. It wakes up only when necessary. That reduces continuous monitoring of synchronization signals. So battery life is improved. This is especially important for low-power devices.

How does it interact with SS periodicity?

WUS timing must align with synchronization signal transmission. If SS periodicity is long, WUS must ensure that the UE wakes up at the correct time. That requires accurate scheduling. Proper coordination ensures that the UE does not miss synchronization opportunities. So WUS and SS design must be integrated.

How should mobility measurements be supported?

Mobility measurements are essential for cell selection, reselection, and handover. We must ensure accurate, stable, and low-latency measurements across different UE states. It must also balance measurement accuracy with UE complexity and power consumption. So the system should reuse existing signals where possible and introduce new mechanisms only when necessary.

What signals are used for measurements in NR?

Why is SSS dominant?

SSS provides cell identity information. It is robust and always transmitted as part of SS/PBCH. It allows reliable detection even in low SNR conditions. So it is well suited for initial detection and mobility measurements. It also simplifies UE implementation since it is always available.

Why is CSI-RS less used?

CSI-RS is more flexible but also more complex. It may not always be present or configured. It requires additional signaling and processing. So it is less suitable for basic mobility. It is mainly used when more accurate channel information is needed, such as for beam management.

Should synchronization signals be reused for mobility?

How does this simplify design?

Reusing synchronization signals simplifies the design. It avoids introducing additional measurement signals. That reduces overhead and UE complexity. The UE does not need to track multiple signal types. Measurement procedures become more consistent across states. So implementation becomes simpler and more power efficient.

What measurement quantities are important?

What are SS-RSRP, SS-RSRQ, and SS-SINR?

SS-RSRP measures received signal power. It reflects coverage level. SS-RSRQ combines signal strength and interference. It reflects quality. SS-SINR measures signal-to-interference-plus-noise ratio. It reflects link reliability. Together, they provide a complete picture of radio conditions for mobility decisions.

How should measurements be consistent across RRC states?

Measurements should behave similarly in idle, inactive, and connected states. This avoids abrupt changes in behavior. It also improves prediction accuracy. So consistent measurement definitions and procedures are important.

How can early CSI acquisition be integrated into mobility procedures?

How does it improve handover performance?

Early CSI acquisition allows the UE to gather channel information before handover. This can be done using pre-configured CSI-RS. The UE already has channel knowledge when the handover starts. That reduces uncertainty and improves link setup speed. So handover success probability increases.

How does it reduce latency?

Without early CSI acquisition, the UE must measure the target cell after handover. This adds delay before stable communication can begin. With early acquisition, measurements are already available. So the UE can adapt more quickly and overall handover latency is reduced.

How does measurement design impact handover stability across RRC states?

How does consistency prevent ping-pong effects?

Ping-pong occurs when measurements fluctuate around mobility thresholds. Consistent filtering, reporting, and decision logic reduce rapid changes in the measured result. This stabilizes handover decisions. So unnecessary back-and-forth handovers can be avoided.

How does it impact reselection decisions?

In idle state, reselection depends on measured signal strength and quality. Stable and accurate measurements help the UE choose the most suitable cell. That improves user experience and reduces unnecessary reselection attempts. It also lowers signaling overhead.

How can AI/ML be integrated into 6GR initial access?

AI/ML can enhance initial access by improving efficiency, reducing latency, and lowering UE complexity. It enables predictive and adaptive behavior instead of exhaustive search. We must ensure that AI/ML integration does not increase signaling overhead or implementation complexity. So AI/ML should complement existing mechanisms rather than replace them.

How can AI/ML improve beam selection during initial access?

How can AI/ML reduce UE search complexity?

AI/ML can predict the most likely beam directions based on context such as location, history, and environment. That reduces the need for exhaustive beam sweeping. The UE can focus on a subset of candidate beams. So search complexity and power consumption are reduced.

How can prediction models improve synchronization timing?

How can spatial beam prediction reduce synchronization overhead?

Spatial prediction allows the UE to estimate the best beam direction before receiving synchronization signals. That reduces the number of beams that need to be monitored. So synchronization overhead is reduced.

How can historical beam patterns be leveraged?

The UE or network can store past beam selections and mobility patterns. These patterns can be used to predict future beam directions. This is especially useful in repetitive environments such as daily commute routes. So beam acquisition becomes faster and more reliable.

How does prediction reduce beam sweeping?

Instead of scanning all beams, the UE scans only predicted beams. That reduces the number of measurements required. It also reduces latency during initial access. So prediction directly improves efficiency.

How can temporal prediction reduce latency in system information acquisition?

How can UE predict SI transmission timing?

AI/ML can learn periodic patterns of system information transmission. The UE can predict when SI will be transmitted. That allows the UE to wake up only at the expected time. So waiting time is reduced.

How does this reduce waiting time?

Without prediction, the UE may wait for the next SI window. This can introduce delay. With prediction, the UE aligns its reception with the expected transmission time. So system information can be acquired faster.

Which Rel-19 AI/ML beam management techniques can be reused in 6GR?

What models are already standardized?

Rel-19 introduces AI/ML-assisted beam management techniques. These include models for beam prediction, selection, and tracking. They are designed to improve performance while keeping signaling manageable.

How can they be adapted for initial access?

These models can be extended to the initial access phase. Instead of using them only for connected mode beam management, they can guide synchronization and access procedures. That allows reuse of existing frameworks. So standardization effort is reduced and interoperability is improved.

Update Note

• 2026-08-30 - Subsections added under time-frequency resources and synchronization periodicity, from the RAN1 #126 contributions of the nine tracked companies.
• 2026-08-29 - Sections on time-frequency resources, MRSS coexistence, PBCH performance, synchronization periodicity and broadcast coverage updated from the RAN1 #126 documents.
• 2026-08-29 - New subsection added under Reference, listing the RAN1 #126 contributions on synchronization acquisition and beam measurement.

Reference

• R1-2600032 : TSG RAN WG1 #124 - On Synchronization Acquisition and Beam Measurement
• R1-2600051 : TSG RAN WG1 #124 - 6G Synchronization Acquisition and Beam Measurement
• R1-2603518 : TSG RAN WG1 #125 - On Synchronization Acquisition and Beam Measurement
• R1-2605291 : TSG RAN WG1 #126 - Synchronization acquisition and beam measurement for 6GR (Huawei, HiSilicon)
• R1-2605382 : TSG RAN WG1 #126 - On  synchronization acquisition and beam measurement (Nokia)
• R1-2605419 : TSG RAN WG1 #126 - Discussion on Synchronization acquisition and beam measurement (Jio Platforms)
• R1-2605431 : TSG RAN WG1 #126 - Discussion on Synchronization acquisition and beam measurement (ZTE Corporation, Sanechips)
• R1-2605521 : TSG RAN WG1 #126 - Synchronization acquisition and beam measurement (Ericsson Inc.)
• R1-2605563 : TSG RAN WG1 #126 - Discussion on synchronization acquisition and beam measurement for 6GR (Xiaomi)
• R1-2605761 : TSG RAN WG1 #126 - Discussion on synchronization acquisition and beam measurement (KT Corp.)
• R1-2605823 : TSG RAN WG1 #126 - Discussion on synchronization acquisition and beam measurement for 6GR (Samsung)
• R1-2605874 : TSG RAN WG1 #126 - Discussion on synchronization acquisition and beam measurement during initial access (NEC)
• R1-2605917 : TSG RAN WG1 #126 - Discussion on Synchronization acquisition and beam measurement for 6GR (OPPO)
• R1-2606022 : TSG RAN WG1 #126 - Synchronization acquisition and beam measurement (Apple)
• R1-2606143 : TSG RAN WG1 #126 - Views on synchronization acquisition and beam measurement (MediaTek Inc.)
• R1-2606186 : TSG RAN WG1 #126 - Discussion on synchronization acquisition and beam measurement (HONOR)
• R1-2606375 : TSG RAN WG1 #126 - Synchronization acquisition and beam measurement (Qualcomm Incorporated)
• R1-2606427 : TSG RAN WG1 #126 - On resource block alignment between LTE, NR, and 6GR (AT&T, Bouygues Telecom, BT, Deutsche Telecom, Ericsson, FirstNet, KT Corp., Nokia, Orange, Spark NZ, Telecom Italia, Verizon, Vodafone)
• R1-2606439 : TSG RAN WG1 #126 - Discussion of synchronization of 6GR (ASUSTeK)
• R1-2606479 : TSG RAN WG1 #126 - Discussion on synchronization acquisition and beam measurement (CSCN)
• R1-2606501 : TSG RAN WG1 #126 - Discussion on synchronization acquisition and beam measurement (NTT DOCOMO, INC.)
• R1-2606646 : TSG RAN WG1 #126 - Synchronization acquisition and beam measurement (CEWiT)
• R1-2606721 : TSG RAN WG1 #126 - FL summary #1 of Synchronization acquisition and beam measurement (Huawei) (Moderator, Huawei)
• R1-2606722 : TSG RAN WG1 #126 - FL summary #2 of Synchronization acquisition and beam measurement (Huawei) (Moderator, Huawei)
• R1-2606723 : TSG RAN WG1 #126 - FL summary #3 of Synchronization acquisition and beam measurement (Moderator, Huawei)
• R1-2606724 : TSG RAN WG1 #126 - FL summary #4 of Synchronization acquisition and beam measurement (Moderator, Huawei)
• R1-2606725 : TSG RAN WG1 #126 - FL summary #5 of Synchronization acquisition and beam measurement (Moderator, Huawei)
• R1-2606726 : TSG RAN WG1 #126 - FL summary #6 of Synchronization acquisition and beam measurement (Moderator, Huawei)
• R1-2606727 : TSG RAN WG1 #126 - FL summary of [Post-125-R20-6GR-Coverage] (Moderator, Huawei)