Intelligent Reflecting Surface Enhanced Wireless Network: Joint Active and Passive Beamforming Design

Intelligent reflecting surface (IRS) is envisioned to have abundant applications in future wireless networks by smartly reconfiguring the signal propagation for performance enhancement. Specifically, an IRS consists of a large number of low-cost passive elements each reflecting the incident signal with a certain phase shift to collaboratively achieve beamforming and suppress interference at one or more designated receivers. In this paper, we study an IRS-enhanced point-to-point multiple-input single-output (MISO) wireless system where one IRS is deployed to assist in the communication from a multi-antenna access point (AP) to a single-antenna user. As a result, the user simultaneously receives the signal sent directly from the AP as well as that reflected by the IRS. We aim to maximize the total received signal power at the user by jointly optimizing the (active) transmit beamforming at the AP and (passive) reflect beamforming by the phase shifters at the IRS. We first propose a centralized algorithm based on the technique of semidefinite relaxation (SDR) by assuming the global channel state information (CSI) available at the IRS. Since the centralized implementation requires excessive channel estimation and signal exchange overheads, we further propose a low-complexity distributed algorithm where the AP and IRS independently adjust the transmit beamforming and the phase shifts in an alternating manner until the convergence is reached. Simulation results show that significant performance gains can be achieved by the proposed algorithms as compared to benchmark schemes. Moreover, it is verified that the IRS is able to drastically enhance the link quality and/or coverage over the conventional setup without the IRS.

I. Introduction

Although there has been a quantum leap in spectrum efficiency of wireless networks in the last few decades thanks to various technological advances such as ultra-dense network (UDN), massive multiple-input multiple-output (M-MIMO), and millimeter wave (mmWave)communications, the network energy consumption and hardware cost are still critical issues faced in practical implementation [1]. For example, UDNs almost linearly scale the circuit and cooling energy consumption with the number of newly deployed base stations (BSs), while costly radio frequency (RF)chains and complex signal processing techniques are needed for efficient communication at mm Wave frequencies. On the other hand, adding an excessively large number of active components such as small-cell BSs/relays in wireless networks also causes a more severe interference issue. Therefore, research on finding both spectral and energy efficient techniques with low hardware cost is still imperative for realizing sustainable and green fifth-generation (5G) wireless networks and beyond [2].