Design of RIS-Aided mMTC+ Networks for Rate Maximization Under the Finite Blocklength Regime With Imperfect Channel Knowledge

2025-11-01

IEEE Communications Letters

AI Summary

This work addresses the challenges posed by finite blocklength and channel estimation errors in short-packet communications for massive machine-type communications (mMTC+). Focusing on a scenario where single-antenna sensors transmit to a multi-antenna access point, the paper proposes a robust reconfigurable intelligent surface (RIS) optimization method. Under imperfect channel state information, the non-convex weighted sum-rate maximization problem is efficiently solved by leveraging successive convex optimization (SCO) combined with concave lower-bound approximation techniques. Simulation results demonstrate that the proposed scheme significantly enhances system throughput while maintaining manageable computational complexity, thereby achieving a favorable trade-off between performance and efficiency.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements

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.

Problem

• RIS
• mMTC+
• finite blocklength
• imperfect channel knowledge
• rate maximization

Innovation

• Reconfigurable Intelligent Surface (RIS)
• Finite Blocklength Regime
• Robust Optimization
• Successive Convex Optimization (SCO)
• Massive Machine-Type Communications (mMTC)