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

Abstract

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 lowcost 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 multipleinput 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.

Sum Rate Optimization for Two Way Communications With Intelligent Reflecting Surface

“…Note that the unit of distance is meter. Similar to [4], [5], the large scale fading is modelled as κ = ςd −α , where d is the distance between the transmitter and receiver, α is the path loss exponent, and ς is the path loss at the reference distance 1m which is set to −30dB. Since in practice the position of IRS can be carefully chosen, we assume that the IRS-source link has better channel condition compared with the direct link.…”

“…zero-mean complex Gaussian and the residual self-interference under 0dBm transmit power is at the same level as the background noise. Finally, the relative reflection gain of the IRS elements over the antenna gain of both sources (assumed to be 0dBi) is set to 5dB [5] and the reflection efficiency is set to 1. Fig.…”

“…where s i is the data symbol vector with unit covariance matrix I N and F i is the N -by-N source precoder subject to the power constraint tr F i F H i ≤ P . The IRS re-scatters the superposition of all incident signals [4], [5]. Assuming only first-order reflection from IRS [5], the reflected signal is:…”

“…The IRS re-scatters the superposition of all incident signals [4], [5]. Assuming only first-order reflection from IRS [5], the reflected signal is:…”

“…Note that the achievable rate (5) of the source S i is derived from I (s i ; yī) where the input probability distribution p (s i ) is CN (0, I N ) and the channel transition probability p(yī|s i ) is from (4). Then according to Lemma 1,(5) can also be reexpressed as:…”