RISMA: Reconfigurable Intelligent Surfaces Enabling Beamforming for IoT Massive Access

TL;DR

This paper introduces RISMA and Lo-RISMA algorithms for optimizing reconfigurable intelligent surfaces in beyond 5G IoT networks, significantly improving sum rate performance in challenging radio environments.

Contribution

It develops a joint optimization framework for RIS and precoding in massive IoT access, proposing two algorithms that outperform benchmarks, including low-resolution RIS configurations.

Findings

Algorithms outperform benchmarks with 20%-120% sum rate gains.

Low-resolution RISs still achieve significant performance improvements.

Joint optimization effectively manages non-line-of-sight paths in IoT networks.

Abstract

Massive access for Internet-of-Things (IoT) in beyond 5G networks represents a daunting challenge for conventional bandwidth-limited technologies. Millimeter-wave technologies (mmWave)---which provide large chunks of bandwidth at the cost of more complex wireless processors in harsher radio environments---is a promising alternative to accommodate massive IoT but its cost and power requirements are an obstacle for wide adoption in practice. In this context, meta-materials arise as a key innovation enabler to address this challenge by Re-configurable Intelligent Surfaces (RISs). In this paper we take on the challenge and study a beyond 5G scenario consisting of a multi-antenna base station (BS) serving a large set of single-antenna user equipments (UEs) with the aid of RISs to cope with non-line-of-sight paths. Specifically, we build a mathematical framework to jointly optimize the precoding strategy of the BS and the RIS parameters in order to minimize the system sum mean squared error (SMSE). This novel approach reveals convenient properties used to design two algorithms, RISMA and Lo-RISMA, which are able to either find simple and efficient solutions to our problem (the former) or accommodate practical constraints with low-resolution RISs (the latter). Numerical results show that our algorithms outperform conventional benchmarks that do not employ RIS (even with low-resolution meta-surfaces) with gains that span from 20% to 120% in sum rate performance.