Robust Energy-Efficient Transmission for Cell-Free Massive MIMO Systems with Imperfect CSI

Abstract

In this paper, we investigate a long-term power minimization problem of cell-free massive multiple-input multiple-output (MIMO) systems. To address this issue and to ensure the system queue stability, we formulate a dynamic optimization problem aiming to minimize the average total power cost in a time-varying system under imperfect channel conditions. The problem is then converted into a real-time weighted sum rate maximization problem for each time slot using the Lyapunov optimization technique. We employ approximation techniques to design robust sparse beamforming, which enables energy savings of the network and mitigates channel uncertainty. By applying direct fractional programming (DFP) and alternating optimization, we can obtain a locally optimal solution. Our DFP-based algorithm minimizes the average total power consumption of the network while satisfying the quality of service requirements for each user. Simulation results demonstrate the rapid convergence of the proposed algorithm and illustrate the tradeoff between average network power consumption and queue latency.

Variational Optimization for Sustainable Massive MIMO Base Station Switching

“…Other algorithms aimed at enhancing energy efficiency in MIMO systems include the use of statistical channel state indicators [ 11 ], load adaptive energy efficiency strategies [ 12 ], network planning and optimal deployment [ 13 , 14 , 15 ], and SINR constraints and power adaptation techniques [ 16 , 17 ]. The authors in [ 18 ] focused on dynamic optimization for minimizing average total power costs in time-varying systems under imperfect channel conditions, employing Lyapunov optimization and fractional programming for robust sparse beamforming. This approach demonstrates rapid convergence and balances network power consumption against queue latency.…”