Role of Deep Learning in Wireless Communications

  title={Role of Deep Learning in Wireless Communications},
  author={Wei Yu and Foad Sohrabi and Tao Jiang},
—Traditional communication system design has al- ways been based on the paradigm of first establishing a mathematical model of the communication channel, then designing and optimizing the system according to the model. The advent of modern machine learning techniques, specifically deep neural networks, has opened up opportunities for data-driven system design and optimization. This article draws examples from the optimization of reconfigurable intelligent surface, distributed channel estimation… 

Machine Learning for Large-Scale Optimization in 6G Wireless Networks




Learning to Optimize: Training Deep Neural Networks for Interference Management

A new learning-based perspective is provided to treat the input and output of an SP algorithm as an unknown nonlinear mapping and use a deep neural network (DNN) to approximate it, which can be accurately approximated by a fully connected DNN.

Spatial Deep Learning for Wireless Scheduling

It is shown that it is possible to bypass the channel estimation stage altogether and to use a deep neural network to produce a near optimal schedule based solely on geographic locations of the transmitters and receivers in the network.

Deep Active Learning Approach to Adaptive Beamforming for mmWave Initial Alignment

  • Foad SohrabiZhilin ChenWei Yu
  • Computer Science
    ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2021
This paper proposes to use the minimum mean squared error (MMSE) estimate of the fading coefficient to compute an approximation of the posterior distribution of the AoA posterior distribution, and demonstrates that as compared to the existing adaptive beamforming schemes utilizing predesigned hierarchical codebooks, the proposed deep learning-based adaptive beamsforming achieves a higher AoA detection performance.

Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems

The learned denoising-based approximate message passing (LDAMP) network is exploited and significantly outperforms state-of-the-art compressed sensing-based algorithms even when the receiver is equipped with a small number of RF chains.

Physical Layer Communication via Deep Learning

This manuscript surveys recent advances towards demonstrating the hypothesis that deep learning methods can play a crucial role in solving core goals of coding theory: designing new (encoder, decoder) pairs that improve state of the art performance over canonical channel models.

Waveform Learning for Next-Generation Wireless Communication Systems

We propose a learning-based method for the joint design of a transmit and receive filter, the constellation geometry and associated bit labeling, as well as a neural network (NN)-based detector. The

Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems

The proposed deep learning-based approach to handle wireless OFDM channels in an end-to-end manner is more robust than conventional methods when fewer training pilots are used, the cyclic prefix is omitted, and nonlinear clipping noise exists.

An Introduction to Deep Learning for the Physical Layer

  • Tim O'SheaJ. Hoydis
  • Computer Science
    IEEE Transactions on Cognitive Communications and Networking
  • 2017
A fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process is developed.

Active Learning and CSI Acquisition for mmWave Initial Alignment

This paper establishes the first practically viable solution for initial access and, hence, the first demonstration of stand-alone mmWave communication in the relevant regime of low (−10 dB to +5 dB) raw SNR.

Active Sensing for Communications by Learning

A long short-term memory (LSTM) network is proposed to be used to exploit the temporal correlations in the sequence of observations and to map each observation to a fixed-size state information vector.