Multi-CPU Dynamic Cooperation Clustering for Scalable Cell-Free Massive MIMO Systems

초록

Cell-free massive MIMO (CF-mMIMO) ensures uniform service across large areas by jointly serving user equipments (UEs) with densely distributed access points (APs). Dynamic cooperation clustering (DCC) provides a scalable framework for CF-mMIMO, but extending it to multiple central processing units (CPUs) increases complexity to the order of the total APs, leading to excessive signaling overhead. To address this, we propose multi-CPU DCC (MCC), a scalable approach that reduces large-scale fading coefficient (LSFC) exchanges from the total APs to a single LSFC. Using this shared LSFC, MCC estimates remaining AP-UE LSFCs to form a user-centric AP cluster while limiting signaling. Experimental results show that MCC achieves 99% of the performance of an idealized DCC while reducing UE-AP, AP-CPU, CPU-CPU, and total signaling by 74.06%, 51.72%, 65.56%, and 57.93%, respectively. Thus, MCC enhances scalability in multi-CPU environments while maintaining performance and minimizing signaling overhead. © 2025 IEEE.

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