Journal Paper

Centralized access point (AP) selection approaches in sequential fronthaul cell-free (SF-CF) networks, such as centralized maximum ratio combining (CMRC) or centralized optimal sequence linear processing (COSLP), often hinder its scalability by increasing fronthaul load and computational complexity as the number of APs and user equipment (UEs) grows. To address this, we propose a novel AP selection scheme that optimizes the uplink (UL) spectral efficiency (SE) while reducing computational and signaling overhead. This scheme dynamically optimizes the AP-UE association matrix and integrates two equalization strategies: sequential MRC (SMRC), where APs coordinate decisions sequentially, and parallel MRC (PMRC), where APs independently optimize their associations without any inter-AP coordination. The optimization problem is solved using a customized genetic algorithm (GA). Our evaluation across various network scenarios demonstrates that although COSLP achieves the highest SE, it does so at the cost of increased complexity and fronthaul signaling, while SMRC and PMRC provide scalable, high-speed alternatives to CMRC with reduced computational overhead. Additionally, we assess the adaptability of MRC-based selection schemes under network changes, such as the addition or removal of UEs, showing that reusing a prior allocation matrix can significantly enhance GA optimization particularly when the network undergoes small changes. The results confirm that SMRC and PMRC offer a computationally efficient and scalable alternative to traditional CF mMIMO AP selection strategies.