Machine learning for communication resources

IEEE Communications Society Machine Learning For Communications Emerging Technologies Initiative home
 * https://mlc.committees.comsoc.org/

ML for Channel Estimation

 * 1) Deep Networks for Equalization in Communications
 * 2) Deep Learning-Based Channel Estimation for Doubly Selective Fading Channels
 * 3) A Denoising Autoencoder based wireless channel transfer function estimator for OFDM communication system
 * 4) Learning the MMSE Channel Estimator
 * 5) Machine Learning-Based Channel Estimation in Massive MIMO with Channel Aging
 * 6) Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models
 * 7) Realistic Channel Models Pre-training
 * 8) Learning-Based Channel Estimation for Various Antenna Array Configurations
 * 9) Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems

ML for Joint Channel Estimation and Detection

 * 1) Joint Neural Network Equalizer and Decoder
 * 2) Quantized Variational Bayesian Joint Channel Estimation and Data Detection for Uplink Massive MIMO Systems with Low resolution ADCS

ML for Peak-to-Average Power Ratio Reduction

 * 1) A Novel PAPR Reduction Scheme for OFDM System Based on Deep Learning
 * 2) A New Approach to Iterative Clipping and Filtering PAPR Reduction Scheme for OFDM Systems
 * 3) Improved Tone Reservation Method Based on Deep Learning for PAPR Reduction in OFDM System
 * 4) Neural Network Assisted Active Constellation Extension for PAPR Reduction of OFDM System

Channel State Generation

 * 1) Correlation matrices from Wikipedia
 * 2) MIMO channel object of MATLAB  at 'Algorithm' section
 * 3) 5G broadcast of MATLAB