Lower Energy Consumption in Multi-CPU Cell-Free Massive MIMO Systems

Under the ideal assumption of deploying only one central processing unit (CPU) in the entire system, cell-free (CF) systems can achieve significant macro-diversity gain, thereby providing uniformly reliable service to each user equipment (UE). However, due to limitations in system scalability and the feasibility of strict phase synchronization, CF systems require a multi-CPU setup and perform coherent transmission at a smaller scale. Moreover, conventional CF systems typically operate in time-division duplex (TDD) mode and utilize statistical channel state information (CSI) for downlink (DL) decoding, but the channel hardening effect is not significant. These factors reduce downlink spectral efficiency (SE) and increase DL transmission time, leading to higher energy consumption in CF systems. To address these issues, we introduce downlink channel estimation (DLCE) in multi-CPU CF systems and derive the approximate achievable DL SE. To reduce DL pilot overhead, we propose an uplink–pilot-reuse-constrained DL pilot allocation principle. Based on this principle, we develop a farthest distance pilot allocation (FDPA) algorithm to mitigate pilot contamination. In addition, leveraging the characteristics of the heuristic distributed power allocation algorithm, we propose two access point (AP) clustering algorithms: one based on CSI (BCSI) and the other based on coherent group size (BCGS). Simulation results indicate that the introduction of DLCE significantly improves DL SE in multi-CPU CF massive MIMO systems, while the proposed FDPA algorithm further enhances DL SE. The BCSI and BCGS algorithms also effectively improve DL SE and help reduce energy consumption. By combining DLCE, the FDPA algorithm, and the proposed AP clustering algorithms, the energy consumption of multi-CPU CF systems can be significantly reduced.