Machine Learning    




Application to Wireless Communication 

The high end wireless communication like cellular communication is largely made up of two big section, radio access network and core network. It seems obvious that AI/Machine Learning would be important part of core network operation. Questions are how much it can be applicable to radio access part especially Physical layer / MAC layer operations. In this page, I am going to chase the ideas and use cases of Machine Learning in Radio Access Network. For core network and application layer, you may refer to a lot of videos that I linked in WhatTheyDo page. Check with QualcommEricsson, Verizon, Cisco, Networking applications in the page.

NOTE : This note is following up more generic ideas about Machine Learning application to Wireless communication.  For those cases more specific to 3GPP activities, I keep another note here.


One of the early motivation to use Machine Learning in wireless communication would be to improve signal detection and classification required for CR (Coganitive Radio) - Ref [1].

Main algorithms in cognitive radio are two types as shown below. One is just to detect the existence of signal and the other one is to figure out more detailed properties of the signal.

For the simplicity of implementation, in most case we focused on detecting the existence of the signal only in most of Cognitive Radio applications. There are roughly two types of algorithms being used for the dection of signal presence as shown below. One is to use ED(Energy Detection) Algorithm based on Threshold and the other one is non-threshold based algorithm.

Even though these are major algorithms being used in signal existance(presence) detection, there some some major issues with these algorithms.

  • Issues with Threshold Based Algorithm
    • Frequent False Alarm
    • Sensitive to Noise
  • Issues with Non-Threshold based Algorithm
    • High Computational Complexity
    • (As a result) Poor online detection performance

In some case, they used various method to figure out the detailed properties of the signal (like modulation scheme). But those methods has issues as follows.

  • Highly affected by Noise
  • Poor Performance in low SNR

To overcome various issues mentioned above, Machine Learning method is being investigated as alternatives to those conventional method mentioned above.

Use Cases and Neural Network Structure

In this section, I am trying to summarize technical papers or videos with ML (Machine Learning) application to wireless PHY/MAC in simple diagrams mainly with focus on Input and Output of the network. For the details, I would recommend you to read the original papers and video that I put in the reference section. Once you have read those original documents/video, you can use the illustrations here as a visual que to refresh your memory and understanding.

In this section, I would focus more on lower layer (PHY/MAC) of common wireless system or cellular communication system.

NOTE : What I talking in this section is mostly from generic application of AI in wireless communication, not specific to cellular communication like 5G, 6G etc. For specific application to application to cellular communication, refer to notes below.

Use Cases

Common Use Cases that I found (probably biased based on my personal interest) is listed as below.  At the very early stages of adopting ML(Machine Learning) in this area was mostly on Modulation Detection, but it does not seem to get strong attention especially in high end communication (like mobile/cellular system) because we already have simple / deterministic way of figuring out the modulation scheme. Then various other use cases (like channel estimation, MIMO optimization, Beam Management) has been investigated and some of the use cases are being adopted in real application.

  • Modulation Detection
  • Channel Estimation / CSI estimation
  • MIMO Antenna Configuration Opmimization
  • Beam Selection / Management
  • Estimation of Channel Model Parameters
  • Power Savings
  • Predictive Resource Allocation
  • Admisson Control
  • Congestion Control

Neural Network Architecture

In terms of the architecture (algorithm) of the neural network, I see almost every types of well-known architectures being used in wireless area even though it would not be a complicated as those used in Google, Facebook, Tesla etc. Followings are the network types that I see in literatures most frequently

  • Fully Connected (commonly known as FC or Linear) only
  • CNN (Convolutional Neural Network)
  • Reinforement Learning


Most of the examples shown here is from various papers or articles that I read, it doesn't necessarily mean that they are really used in real applications. I am just to trying to gather various ideas and get myself familiar to Machine Learning Application to wireless commuincation protocol stack.

Case 1: Application of Machine Learning for Modulation Classification. Following two is summary of the model suggested in Ref 01.



Case 2 : Application of Machine Learning for 5G Physical Layer. Following three is summary of the model suggested in Ref 06.


Case 3 :  Application of Machine Learning for mmMIMO operation and Beam Selection. Following is an example of reinforcement learning for mmMIMO operation from Ref 7.

Case 4 : Application of Machine Learning for Modulation Recognition. Following is an example of a CNN for modulation identification Ref 8.

Case 5 : Application of Machine Learning for 5G Beam Selection. Following is an example of using multiple layers of FC(Fully Connected) network for 5G Beam Selection (Ref 13)


Case 6 : Application of Machine Learning for Channel Model. Following is an example of using multiple layers of FC(Fully Connected) network for estimating channel model parameters (Ref 14).


Case 7 : Application of Machine Learning for Equalization : Following is an example of channel estimation (Ref 16)

NOTE : The block labeled as 'DL model' is Machine Learning part


Case 8 : Machine learning models for each layer : Following is an example of Potential Machine Learning Application and Models in 6G wireless network systems. (Ref 17)


Case 9 : Application of Machine Learning for mmWave Beam and Blockage Prediction : Following is an example of Machine learning application for prediction of mmWave Beam and Blockage prediction (Ref 19)


I personally interested in application of machine learning in physical layer of wireless communication and followings are a list of my personal question (to myself and as investigation topics for myself) in terms of applying the neural network to wireless PHY (and low MAC).

  • Data Aquisition for Training : For training phase, we can easily generate the data with software tool(e.g, Matlab) and convert it into pictorial form like vector diagram, Eye diagram, spectrogram. But how can we expect real wireless device can do the same thing ? It would require a huge additional cost and performance. It would be like carrying a VSA(Vector Signal Analyzer) or Digital Oscilloscope within the device.
  • Data Availability for Training : One of the critical factors for the success of Machine Learning as we see today (as of Dec 2019) is the availability of huge data set largely thanks to the internet, social network etc and largely thanks to groups of dedicated experts. By the nature of Neural Network/Deep learning, it cannot learn and produce any meaningful output without huge set of training data. Now the question is 'Do we have large enough data set to train the neural network for wireless communication Phy/Mac ?'
  • Is it Simple enough to be implemented for low energy ? : Even assuming that we have some means to resolve the issues mentioned above, how can we implement the network simple enough that can come out with the solution fast enough and with low energy consumption that can be utilized in mobile terminal (e.g, mobile phone) ?  ==> Recently (Sep 2021) I came across a YouTube video to present some brilliant idea of implementing Machine Learning with reduced power demand. See Pushing the boundaries of AI research at Qualcomm - Max Welling (University of Amsterdam & Qualcomm)
  • Is it fast enough for real time and latency requirement ? : In most of high end wireless communication, the latency requirement is very tight. The factors affecting the latency requirement would be :
    • The latency requirement for PHY procedure (e.g, TTI timing , HARQ response timing etc)
    • coherence time of the radio channel
  • Usually tho latency requirement in PHY layer for those high end wireless communication is milisecond or submilisecond scale, meaning that the processing time of the machine learning algorithm should be fit into this time scale. ==> Refer to III. DEEP LEARNING AT THE PHYSICAL LAYER: SYSTEM REQUIREMENTS AND CHALLENGES of Ref [18]
  • Can it meet the accuracy requirement  : In most of neural network application, the requirement for the level of accuracy does not seem to be as strict as what is required for most of high end wireless physical layer (e.g, cellular communication). In wireless communication, it is expected to give the result of 0% BLER in relatively good channel condition (or less than 10% even in a relaxed criteria). For example, if we replace some PHY process (e.g, channel estimation, modulation detection etc) with the neural network, would it give 0% BLER in a good lab condition (or in less than 10% BLER in a good live condition) ?
  • Feature Engineering  : Wireless communication systems are complex and have many parameters that can affect the performance, and selecting the relevant features for the machine learning model can be difficult.
  • Model interpretability: Some machine learning models, such as deep neural networks, can be difficult to interpret, which can make it challenging to understand the behavior of the model and make adjustments.
  • Privacy and security: Wireless communication systems often handle sensitive data and applying machine learning to these systems can raise privacy and security concerns


[1] Deep Learning Framework for Signal Detection and Modulation Classification (2019) 

[2] Fast Deep Learning for Automatic Modulation Classification (2019)  

[3] Automatic Modulation Recognition Using Deep Learning Architectures

[4] Modulation Classification with Deep Learning(Mathworks)

[5] GRCon18 - Advances in Machine Learning for Sensing and Communications Systems (YouTube, 2019)

[6] Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions (2019)

[7] 5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning (2018)

[8] Convolutional Radio Modulation Recognition Networks (2016)

[9] Machine Learning for Beam Based Mobility Optimization in NR (2017)

[10] TWS 18: Machine Learning for Context and Can ML/AI build better wireless systems? (2018)

[11] The Future of Wireless and What It Will Enable (2018)

[12] Deep Learning for Wireless Physical Layer: Opportunities and Challenges (2017)

[13] Deep Learning-Based mmWave Beam Selection for 5G NR/6G With Sub-6 GHz Channel Information: Algorithms and Prototype Validation (2020)

[14] Machine Learning for Wireless Communication Channel Modeling: An Overview (2019)

[15] Predicting the Path Loss of Wireless Channel Models Using Machine Learning Techniques in MmWave Urban Communications (2020)

[16] Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems

[17] Machine Learning Techniques for 5G and Beyond (2021)

[18] Deep Learning at the Physical Layer: System Challenges and Applications to 5G and Beyond  

[19] Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels (2019)