RIS-Assisted D-MIMO for Energy-Efficient 6G Indoor Networks

2026-03-25

AI Summary

This work addresses energy efficiency maximization in indoor 6G reconfigurable intelligent surface (RIS)-assisted distributed MIMO systems by jointly optimizing access point power allocation and RIS phase shifts, subject to transmit power and signal-to-interference-plus-noise ratio (SINR) constraints. An alternating optimization framework based on the majorization-minimization (MM) method is proposed to tackle the non-convex problem under both coherent and non-coherent receiver configurations. The study further compares centralized and distributed RIS control architectures. Simulation results under a realistic power consumption model demonstrate that the proposed approach significantly outperforms baseline schemes employing equal power allocation and random phase shifts. The findings provide critical insights into the impact of RIS phase design, control architecture, and scale on system energy efficiency, offering valuable guidance for practical 6G deployments.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements

Abstract

We propose an alternating optimization framework for maximizing energy efficiency (EE) in reconfigurable intelligent surface (RIS) assisted distributed MIMO (D-MIMO) systems under both coherent and non-coherent reception modes. The framework jointly optimizes access point (AP) power allocation and RIS phase configurations to improve EE under per-AP power and signal-to-interference-plus-noise ratio (SINR) constraints. Using majorization-minimization for power allocation together with per-element RIS adaptation, the framework achieves tractable optimization of this non-convex problem. Simulation results for indoor deployments with realistic power-consumption models show that the proposed scheme outperforms equal-power and random-scatterer baselines, with clear EE gains. We evaluate the performance of both reception modes and quantify the impact of RIS phase-shift optimization, RIS controller architectures (centralized vs. per-RIS control), and RIS size, providing design insights for practical RIS-assisted D-MIMO deployments in future 6G networks.

Problem

• energy efficiency
• reconfigurable intelligent surface
• distributed MIMO
• 6G indoor networks
• SINR constraints

Innovation

• Reconfigurable Intelligent Surface (RIS)
• Distributed MIMO
• Energy Efficiency
• Alternating Optimization
• 6G Indoor Networks