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.

翻译：我们提出了一种交替优化框架，旨在最大化可重构智能表面辅助分布式MIMO系统在相干与非相干两种接收模式下的能效。该框架联合优化接入点功率分配与RIS相位配置，在每AP功率及信干噪比约束下提升能效。通过采用针对功率分配的主成分最小化方法结合逐单元RIS自适应，该框架实现了对该非凸问题的可解优化。在采用实际功耗模型的室内部署场景下，仿真结果表明所提方案优于等功率和随机散射体基线方法，具有显著的能效增益。我们评估了两种接收模式的性能，并量化了RIS相位优化、RIS控制器架构（集中式与每RIS控制）及RIS尺寸的影响，为未来6G网络中实用化RIS辅助分布式MIMO部署提供了设计指导。