Wireless Communications Through Reconfigurable Intelligent Surfaces

Abstract

The future of mobile communications looks exciting with the potential new use cases and challenging requirements of future 6th generation (6G) and beyond wireless networks. Since the beginning of the modern era of wireless communications, the propagation medium has been perceived as a randomly behaving entity between the transmitter and the receiver, which degrades the quality of the received signal due to the uncontrollable interactions of the transmitted radio waves with the surrounding objects. The recent advent of reconfigurable intelligent surfaces in wireless communications enables, on the other hand, network operators to control the scattering, reflection, and refraction characteristics of the radio waves, by overcoming the negative effects of natural wireless propagation. Recent results have revealed that reconfigurable intelligent surfaces can effectively control the wavefront, e.g., the phase, amplitude, frequency, and even polarization, of the impinging signals without the need of complex decoding, encoding, and radio frequency processing operations. Motivated by the potential of this emerging technology, the present article is aimed to provide the readers with a detailed overview and historical perspective on state-of-the-art solutions, and to elaborate on the fundamental differences with other technologies, the most important open research issues to tackle, and the reasons why the use of reconfigurable intelligent surfaces necessitates to rethink the communication-theoretic models currently employed in wireless networks. This article also explores theoretical performance limits of reconfigurable intelligent surface-assisted communication systems using mathematical techniques and elaborates on the potential use cases of intelligent surfaces in 6G and beyond wireless networks.INDEX TERMS 6G, large intelligent surfaces, meta-surfaces, reconfigurable intelligent surfaces, smart reflect-arrays, software-defined surfaces, wireless communications, wireless networks. I. INTRODUCTIONAccording to the February 2019 report of Cisco [2], by the year of 2022, the number of networked devices and connections will reach up to 28.5 billions, and 12.3 billions of them will consist of mobile-ready devices and connections.The associate editor coordinating the review of this manuscript and approving it for publication was Feng Li.

Intelligent Reflecting Surfaces: Physics, Propagation, and Pathloss Modeling

“…Importantly, the received signal power is proportional to the square of the IRS area and to 1/(d i r) 2 , where d i is the distance between the transmitter and IRS, and r is the distance between the IRS and receiver. This disproves the conjecture in [5] that the received power would be proportional to 1/(d i + r) 2 . That conjecture might hold for an infinitely large IRS acting or in the near-field, but provably not in the far-field setup studied herein.…”

Intelligent Reflecting Surface for Massive Device Connectivity: Joint Activity Detection and Channel Estimation

“…Consider an IRS-assisted IoT network in Fig 1, where the IRS consists of N reflecting elements and the BS is equipped with M antennas to serve K single-antenna mobile devices. Due to unfavorable propagation conditions, the direct link between the BS and the devices has negligible received signals and thus we ignore the device-BS channel [1,18,19]. We define h r k ∈ C N ×1 as the channel vector from the k-th device to the IRS.…”

“…Intelligent Reflecting Surface (IRS) has recently emerged as a promising new technology for enhancing wireless communications both spectral-efficiently and energy-efficiently by controlling the propagation environment [1][2][3]. Specifically, IRS is a planar surface consisting of a massive number of passive and programmable elements reflecting incident signals with phase shifts [3].…”

Reconfigurable Intelligent Surface for Green Edge Inference in Machine Learning

“…We first consider an ideal scenario that the MDs have unlimited power budgets (i.e., P UL k,max = +∞, ∀ k ∈ K). Problem (16) is then equivalent to the following virtual downlink power minimization problem…”