Machine Learning - Application to Wireless Comm.                Home : www.sharetechnote.com

 

 

 

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 Ericsson, Verizon, Cisco, Networking applications in the page.

 

 

 

Motivation

 

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.

 

 

 

Typical Neural Network Architecture for Wireless Application

 

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.

 

Following two is summary of the model suggested in Ref 01.

 

 

 

 

 

Following three is summary of the model suggested in Ref 06.

 

 

 

 

Following is an example of reinforcement learning for mmMIMO operation from Ref 7.

 

 

 

Following is an example of a CNN for modulation identification Ref 8.

 

 

 

 

Questions/Challenges

 

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).

 

1. 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.

 

2. 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 ?'

 

3. 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) ?

 

4. 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) ?

 

 

 

References

 

[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)

 

 

 

 

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