An intelligent reflective surface (IRS) is a novel and revolutionizing communication technology destined to enable the control of the radio environment. An IRS is a real-time controllable reflectarray with a massive number of low-cost passive elements which introduce a phase shift to the incoming signals from the sources before the propagation towards the destination. This technology introduces the notion of a smart propagation environment with the aim of improving the system performance. In this paper, we provide a comprehensive literature overview on IRS technology, including its basic concepts and reconfiguration, as well as its design aspects and applications for wireless communication systems. We also study the performance metrics and the setups considered in recent publications related to IRS and provide suggestions of future research lines based on still unexplored use cases in the state-of-the-art.

Analog Joint Source-Channel Coding (JSCC) is a communication strategy that does not follow the separation principle of conventional digital systems but approaches the optimal distortion-cost tradeoff over AWGN channels. Conventional Maximum Likelihood (ML) analog JSCC decoding schemes suffer performance degradation at low Channel Signal to Noise Ratio (CSNR) values, while Minimum Mean Square Error (MMSE) decoding presents high complexity. In this letter we propose an alternative two step decoding approach which achieves the near-optimal performance of MMSE decoding at all CSNR values while maintaining a low complexity comparable to that of ML decoding. An additional advantage of the proposed analog JSCC decoding approach is that it can also be used in Multiple Input Multiple Output (MIMO) fading channels.

The combination of multiple-input multiple-output (MIMO) systems and intelligent reflecting surfaces (IRSs) is foreseen as a critical enabler of beyond 5G (B5G) and 6G. In this work, two different approaches are considered for the joint optimization of the IRS phase-shift matrix and MIMO precoders of an IRS-assisted multi-stream (MS) multi-user MIMO (MU-MIMO) system. Both approaches aim to maximize the system sum-rate for every channel realization. The first proposed solution is a novel contextual bandit (CB) framework with continuous state and action spaces called deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG). The second is an innovative deep reinforcement learning (DRL) formulation where the states, actions, and rewards are selected such that the Markov decision process (MDP) property of reinforcement learning (RL) is appropriately met. Both proposals perform remarkably better than state-of-the-art heuristic methods in scenarios with high multi-user interference.

Recently, analog joint source-channel coding has been proposed as a means of achieving near-optimum performance for high data rates with a very low complexity. However, no experimental evaluation showing the practical feasibility of this scheme has been performed to date. In this paper, we describe a software-defined radio implementation of an analog joint source-channel coded wireless transmission system. Experimental evaluation carried out in an indoor environment making use of a wireless testbed show that the performance perfectly matches that originally reported by simulations in additive white Gaussian noise channels for signal-to-noise ratio values below 20 dB.

Analog joint source-channel coding (JSCC) is a communication strategy that does not follow the separation principle of conventional digital systems but has been shown to approach the optimal distortion-cost tradeoff over additive white Gaussian noise channels. In this work, we investigate the feasibility of analog JSCC over multiple-input multiple-output (MIMO) fading channels. Since, due to complexity constraints, directly recovering the analog source information from the MIMO channel output is not possible, we propose the utilization of low-complexity two-stage receivers that separately perform detection and analog JSCC maximum likelihood decoding. We study analog JSCC MIMO receivers that utilize either linear minimum mean square error or decision feedback MIMO detection. Computer experiments show the ability of the proposed analog JSCC receivers to approach the optimal distortion-cost tradeoff both in the low and high channel signal-to-noise ratio regimes. Performance is analyzed over both synthetically computer-generated Rayleigh fading channels and real indoor wireless measured channels.

We address the evaluation of low-complexity analog Joint Source Channel Coding (JSCC) methods for the transmission of discrete-time analog symbols over Multiple-Input Multiple-Output (MIMO) Multiple Access Channels (MAC). Analog JSCC is employed to encode the source information at each transmitter prior to be directly input to the MAC access scheme. Three channel access methods are considered to ensure the receiver is able to recover the user information: Code Division Multiple Access (CDMA), linear MMSE access codes and opportunistic access. CDMA allows the orthogonal transmission of the user data requiring only Channel State Information (CSI) at reception. On the other hand, linear MMSE access codes exploit CSI knowledge at transmission and exhibit better performance. Finally, opportunistic access also exploits CSI at transmission and allocates all MAC resources to the user with the strongest channel. This latter access scheme exhibits the best performance in terms of sum distortion although it may lead to unfair rate distributions among users.