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

Sergi Liesegang, Antonio Pascual-Iserte, Olga Muñoz, Alessio Zappone

TL;DR

The paper addresses maximizing the weighted sum rate in RIS-aided mMTC+ networks operating under finite blocklength and imperfect CSI. It introduces a two-timescale design with CN filters and RIS reflections, and solves the nonconvex WSR problem via successive convex optimization (SCO) by lifting to $\Phi = \psi \psi^H$ and applying semidefinite relaxation with concave/convex bounds. It provides specialized derivations for both single-antenna and multi-antenna CNs, including SDR-based recovery of the RIS phase shifts through Gaussian randomization. Numerical results show significant WSR gains with larger RIS and CN arrays, with SCO offering near-alternative-optimization performance at much lower complexity, highlighting the practical viability of robust RIS-aided mMTC+ deployments under short-packet communication. The work thus extends prior perfect-CSI, single-antenna analyses to a robust, multi-antenna setting compatible with finite blocklength regimes, enabling energy-efficient RIS-enabled enhancements for dense IoT networks.

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.

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

TL;DR

The paper addresses maximizing the weighted sum rate in RIS-aided mMTC+ networks operating under finite blocklength and imperfect CSI. It introduces a two-timescale design with CN filters and RIS reflections, and solves the nonconvex WSR problem via successive convex optimization (SCO) by lifting to and applying semidefinite relaxation with concave/convex bounds. It provides specialized derivations for both single-antenna and multi-antenna CNs, including SDR-based recovery of the RIS phase shifts through Gaussian randomization. Numerical results show significant WSR gains with larger RIS and CN arrays, with SCO offering near-alternative-optimization performance at much lower complexity, highlighting the practical viability of robust RIS-aided mMTC+ deployments under short-packet communication. The work thus extends prior perfect-CSI, single-antenna analyses to a robust, multi-antenna setting compatible with finite blocklength regimes, enabling energy-efficient RIS-enabled enhancements for dense IoT networks.

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.
Paper Structure (9 sections, 22 equations, 2 figures, 1 table)

This paper contains 9 sections, 22 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Proposed Solution
  4. Single-antenna CN
  5. Multiple-antenna CN
  6. Numerical Evaluation
  7. WSR Performance
  8. Complexity Analysis
  9. Conclusions

Figures (2)

  • Figure 1: Setup with $M = 9$ active sensors, $L = 81$ RIS elements, and $K = 3$ CN antennas. Solid/dotted lines indicate strong/weak paths.
  • Figure 2: WSR ($\omega_i = 1$$\forall i$) w.r.t. the number of spatial elements: the number of CN antennas $K$ (solid) and RIS elements $L$ (dotted).