Resource Allocation and User Scheduling Design for User-Centric Cell-Free Massive MIMO Systems

This paper proposes a novel resource allocation scheme for optimizing the downlink of a user-centric cellfree massive multiple-input multiple-output (MIMO) system. The proposed approach aims to optimize the number of users served by each access point based on channel conditions while adapting to variable packet error rate and modulation and coding schemes. To enhance the received signal-to-noise plus interference ratio, the authors use a precoding design approach called the local protective partial zero-forcing that categorizes users based on their channel gain. The problem is formulated as a joint optimization of user assignment, resource allocation, and the precoding design. Closed-form expressions for the data rate are derived, and a new algorithm for resource allocation is introduced that outperforms several different scenarios while keeping the computational complexity reasonable. Compared to fixed parameter schemes, the proposed approach provides an optimal selection of the number of users for each access point and has the potential to significantly improve the system throughput, making it a novel and impactful solution for the practical implementation of user-centric cell-free massive MIMO systems.