Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication

The adoption of a reconfigurable intelligent surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to 300% higher energy efficiency in comparison with the use of regular multi-antenna amplify-and-forward relaying.

I. Introduction

The highly demanding data rate requirements of emerging and future wireless networks (5-th Generation (5G) and beyond) have raised serious concerns on their energy consumption [2], [3]. These networks are anticipated to connect over 50 billions of wireless capability devices by 2020 [4] via dense deployments of multi-antenna base stations and access points [5]–[7]. As a consequence, the bit-per-Joule Energy Efficiency (EE) has emerged as a key performance indicator to ensure green and sustainable wireless networks [2], [3], [8], and several energy efficient wireless solutions have been proposed. A survey on the different approaches to implement energy efficient 5G wireless networks has recently appeared in [9]. Therein, the authors conclude that the energy challenge can be conquered only by the joint use of multiple approaches ranging from the use of renewable energy sources, energy efficient hardware components and relevant deployment techniques, as well as green resource allocation and transceiver signal processing algorithms. The issue of radio resource allocation for EE maximization in wireless networks is addressed in detail in [10], where the related mathematical tools are discussed. In [11]–[14] it is established that deploying a massive number of antennas can bring substantial energy-efficient benefits.