Learning to Reflect and to Beamform for Intelligent Reflecting Surface With Implicit Channel Estimation

Intelligent reflecting surface (IRS), which consists of a large number of tunable reflective elements, is capable of enhancing the wireless propagation environment in a cellular network by intelligently reflecting the electromagnetic waves from the base-station (BS) toward the users. The optimal tuning of the phase shifters at the IRS is, however, a challenging problem, because due to the passive nature of reflective elements, it is difficult to directly measure the channels between the IRS, the BS, and the users. Instead of following the traditional paradigm of first estimating the channels then optimizing the system parameters, this paper advocates a machine learning approach capable of directly optimizing both the beamformers at the BS and the reflective coefficients at the IRS based on a system objective. This is achieved by using a deep neural network to parameterize the mapping from the received pilots (plus any additional information, such as the user locations) to an optimized system configuration, and by adopting a permutation invariant/equivariant graph neural network (GNN) architecture to capture the interactions among the different users in the cellular network. Simulation results show that the proposed implicit channel estimation based approach is generalizable, can be interpreted, and can efficiently learn to maximize a sum-rate or minimum-rate objective from a much fewer number of pilots than the traditional explicit channel estimation based approaches.

I. Introduction

Conventional physical-layer communication system design has always been based on the paradigm of first modeling the channel, then optimizing the transmitter and the receiver design parameters according to the channel model. While this conventional approach has served the communication engineers well for many practical communication scenarios, the recent emergence of new communication modalities involving passive reflectors for which channel estimation may not be straightforward to perform has motivated the need for new approaches. This paper investigates the design of reflective patterns for intelligent reflecting surfaces (IRS), also known as reconfigurable intelligent surface, composed of a large number of tunable reflective elements. We advocate the use of machine learning techniques to bypass explicit channel estimation and to directly design the beamforming and reflective patterns to optimize a system-wide objective.