An Overview of Signal Processing Techniques for RIS/IRS-Aided Wireless Systems

In the past as well as present wireless communication systems, the wireless propagation environment is regarded as an uncontrollable black box that impairs the received signal quality, and its negative impacts are compensated for by relying on the design of various sophisticated transmission/reception schemes. However, the improvements through applying such schemes operating only at two endpoints (i.e., transmitter and receiver) are limited even after five generations of wireless systems. Reconfigurable intelligent surface (RIS) or intelligent reflecting surface (IRS) have emerged as a new and promising technology that can configure the wireless environment in a favorable manner by properly tuning the phase shifts of a large number of quasi passive and low-cost reflecting elements, thus standing out as a promising candidate technology for the next/sixth-generation (6G) wireless system. However, to reap the performance benefits promised by RIS/IRS, efficient signal processing techniques are crucial, for a variety of purposes such as channel estimation, transmission design, radio localization, and so on. In this paper, we provide a comprehensive overview of recent advances on RIS/IRS-aided wireless systems from the signal processing perspective.We also highlight promising research directions that are worthy of investigation in the future.

I. Introduction

While the fifth-generation (5G) of wireless communication systems is under deployment worldwide, research interest has shifted to the future sixth-generation (6G) of wireless systems [1]–[3], which targets supporting not only cutting-edge applications like multisensory augmented/virtual reality applications, wireless brain computer interactions, and fully autonomous systems, but also the wireless evolution from “connected things” to “connected intelligence”. The required key performance indicators (KPIs), including data rates, reliability, latency, spectrum/energy efficiency, and connection density, will be superior to those for 5G. For example, the energy and spectrum efficiency for 6G are expected to be 10-100 times and 5 times better than those of 5G, respectively. These KPIs, however, cannot be fully achieved by the existing three-pillar 5G physical layer techniques [4], which include massive multiple-input multiple-output (MIMO), millimeter wave (mmWave) communications, and ultra-dense heterogeneous networks. In particular, a large number of antennas along with active radio frequency (RF) chains are needed for massive MIMO to achieve high spectrum efficiency, which leads to high energy consumption and hardware cost. Moreover, moving to the mmWave frequency band renders the electromagnetic waves more susceptible to blockage by obstacles such as furniture and walls in indoor scenarios. In addition, more costly RF chains and sophisticated hybrid precoding are necessary for mmWave communication systems. The dense deployment of small base stations (BSs) also incurs in high maintenance cost, network energy consumption, and hardware cost due to high-speed backhaul links. Furthermore, sophisticated interference management techniques are necessary in ultra-dense networks.