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

Akshay Vayal Parambath, Jose Flordelis, Venkatesh Tentu, Charitha Madapatha, Fredrik Rusek, Erik Bengtsson, Tommy Svensson

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.

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

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.
Paper Structure (24 sections, 15 equations, 5 figures, 1 table)

This paper contains 24 sections, 15 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Topology and Associations
  4. Channel Model
  5. Coherent Case
  6. Non-Coherent Case
  7. Signal Model
  8. Power-Consumption Model
  9. Optimization Framework
  10. Effective Channel-Based Constants
  11. Problem Formulation
  12. Joint Alternating Optimization Algorithm
  13. MM-Based Power Allocation (fixed $\mathbf{\Phi}_{m_k}$)
  14. RIS Phase Optimization (fixed powers)
  15. Outer Loop and Stopping Rule
  16. ...and 9 more sections

Figures (5)

  • Figure 1: Illustration of an RIS-assisted D-MIMO downlink system.
  • Figure 2: Sum SE and global EE vs. $P_{\mathrm{T}}$ for a D-MIMO system, comparing benchmarks of RIS integration, and global EE optimization.
  • Figure 3: Global EE vs. sum SE for a RIS-assisted D-MIMO system ($L$ = 9, $N_{\text{RIS}}$ = 256) under C and NC reception, comparing RIS-Opt and RIS-Rand with OPA across $P_{\text{T}}\!\in\![20,40]$ dBm.
  • Figure 4: Global EE of a RIS-aided D-MIMO system ($L=9$, $N_{\text{RIS}}=256$) under C and NC combining for centralized and per-RIS control.
  • Figure 5: Global EE vs. $P_{\mathrm{T}}$ for a RIS-assisted D-MIMO system with $L=9$ APs, under C and NC reception using OPA and RIS-Opt.