Adaptive Local Combining with Decentralized Decoding for Distributed Massive MIMO

A major bottleneck in uplink distributed massive multiple-input multiple-output networks is the sub-optimal performance of local combining schemes, coupled with high fronthaul load and computational cost inherent in centralized large scale fading decoding (LSFD) architectures. This paper introduces a decentralized decoding architecture that fundamentally breaks from the LSFD, by allowing each access point (AP) to calculate interference-suppressing local weights independently and apply them to its data estimates before transmission. Furthermore, two generalized local zero-forcing (ZF) frameworks, generalized partial full-pilot ZF (G-PFZF) and generalized protected weak PFZF (G-PWPFZF), are introduced, where each AP adaptively and independently determines its combining strategy through a local sum spectral efficiency (SE) optimization that classifies user equipments (UEs) as strong or weak, eliminating the fixed thresholds used in the PFZF and PWPFZF schemes. To enhance scalability, pilot-dependent combining vectors instead of user-dependent ones are introduced and are shared among users with the same pilot. The closed-form SE expressions corresponding to the proposed schemes are derived. Numerical results show that the proposed schemes consistently outperform fixed-threshold counterparts, while the introduction of local weights yields lower overheads and computation costs with lower performance penalty compared to them.