Intelligent Reflecting Surfaces for Compute-and-Forward

TL;DR

This paper explores how intelligent reflecting surfaces (IRSs) can significantly improve the computation rate in compute-and-forward wireless systems by optimizing IRS parameters to enhance signal quality and decoding capabilities.

Contribution

It introduces the use of IRSs in compute-and-forward systems, formulates an optimization problem for IRS phase shifts, and proposes an efficient alternating optimization method.

Findings

IRS design significantly boosts computation rate

Proposed AO approach effectively optimizes IRS parameters

Numerical results confirm IRS's potential for future wireless networks

Abstract

Compute-and-forward is a promising strategy to tackle interference and obtain high rates between the transmitting users in a wireless network. However, the quality of the wireless channels between the users substantially limits the achievable computation rate in such systems. In this paper, we introduce the idea of using intelligent reflecting surfaces (IRSs) to enhance the computing capability of the compute-and-forward systems. For this purpose, we consider a multiple access channel(MAC) where a number of users aim to send data to a base station (BS) in a wireless network, where the BS is interested in decoding a linear combination of the data from different users in the corresponding finite field. Considering the compute-and-forward framework, we show that through carefully designing the IRS parameters, such a scenario's computation rate can be significantly improved. More specifically, we formulate an optimization problem which aims to maximize the computation rate of the system through optimizing the IRS phase shift parameters. We then propose an alternating optimization (AO) approach to solve the formulated problem with low complexity. Finally, via various numerical results, we demonstrate the effectiveness of the IRS technology for enhancing the performance of the compute-and-forward systems, which indicates its great potential for future wireless networks with massive computation requirements, such as 6G.