Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

Abstract

The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented.

Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning

“…MIMO is different from single antenna systems as data transmission, and reception in MIMO is done on multiple antennas. Moreover, MIMO introduces signaling degrees of freedom, also known as the spatial degree of freedom, and it is absent in single antenna systems [ 37 ]. Exploiting spatial degrees of freedom may be done for “multiplexing”, “diversity”, or a combination of both.…”

Projection based inverse reinforcement learning for the analysis of dynamic treatment regimes

“…The application of Machine Learning (ML) techniques in biomedicine [31], minimizing medication errors during home treatment [32,33], risk management [34], communication [35][36][37] and healthcare [38][39][40] has been increased extensively in recent years. DTRs [41,42] oversimplify personalized medicine to time-varying treatment settings in which the treatment is frequently tailored to a patient's dynamic-state.…”

A Low-Complexity Algorithm for a Reinforcement Learning-Based Channel Estimator for MIMO Systems

“…Although perfect channel information is unavailable, many studies have been conducted to improve the accuracy of channel estimation [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. These investigations were mostly based on the use of pilots whose information is shared by both the transmitter and receiver and employed least-squares and linear minimum-mean square-error (LMMSE) estimations [10][11][12].…”

“…This limitation can be overcome using data in channel estimation, i.e., conducting data-aided channel estimation [13][14][15][16][17][18][19][20][21]. Its concept is to exploit a detected data symbol as an additional pilot.…”