Design of RIS-aided mMTC+ Networks for Rate Maximization under the Finite Blocklength Regime with Imperfect Channel Knowledge

TL;DR

This paper proposes a robust RIS-assisted network design for massive machine-type communications that maximizes data rate in finite blocklength conditions, accounting for channel estimation errors and using successive convex optimization.

Contribution

It introduces a novel robust optimization framework for RIS in mMTC networks under finite blocklength and imperfect channel knowledge, using concave bounds and SCO techniques.

Findings

SCO effectively maximizes weighted sum rate in RIS-aided mMTC networks.

Robust RIS design improves performance under channel estimation errors.

Numerical results confirm the efficiency and practicality of the proposed method.

Abstract

Within the context of massive machine-type communications+, reconfigurable intelligent surfaces (RISs) represent a promising technology to boost system performance in scenarios with poor channel conditions. Considering single-antenna sensors transmitting short data packets to a multiple-antenna collector node, we introduce and design an RIS to maximize the weighted sum rate (WSR) of the system working in the finite blocklength regime. Due to the large number of reflecting elements and their passive nature, channel estimation errors may occur. In this letter, we then propose a robust RIS optimization to combat such a detrimental issue. Based on concave bounds and approximations, the nonconvex WSR problem for the RIS response is addressed via successive convex optimization (SCO). Numerical experiments validate the performance and complexity of the SCO solutions.