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 conventional LSFD, by allowing each AP calculates interference-suppressing local weights independently and applies them to its data estimates before transmission. Furthermore, two generalized local zero-forcing (ZF) framework, generalized partial full-pilot ZF (G-PFZF) and generalized protected weak PFZF (G-PWPFZF), are introduced, where each access point (AP) adaptively and independently determines its combining strategy through a local sum spectral efficiency optimization that classifies user equipments (UEs) as strong or weak using only local information, eliminating the fixed thresholds used in PFZF and PWPFZF. To further enhance scalability, pilot-dependent combining vectors instead of user-dependent ones are introduced and are shared among users with the same pilot. The corresponding closed-form spectral efficiency expressions are derived. Numerical results show that the proposed generalized schemes consistently outperform fixed-threshold counterparts, while the introduction of local weights yields lower overhead and computation costs with minimal performance penalty compared to them.

翻译：上行分布式大规模多输入多输出网络的主要瓶颈在于本地合并方案的性能欠佳，同时集中式大规模衰落解码架构固有的高前传负载和计算成本问题突出。本文提出一种分散式解码架构，该架构从根本上突破了传统LSFD的局限：允许每个接入点独立计算干扰抑制的本地权重，并在数据传输前将其应用于数据估计。进一步地，本文引入了两种广义本地迫零框架——广义部分全导频迫零与广义保护型弱用户部分全导频迫零，其中每个接入点通过仅利用本地信息将用户设备分类为强用户或弱用户的本地总频谱效率优化，自适应且独立地确定其合并策略，从而消除了PFZF和PWPFZF中使用的固定阈值。为提升可扩展性，本文采用与导频相关而非用户相关的合并向量，并在使用相同导频的用户间共享。研究推导了相应的闭式频谱效率表达式。数值结果表明：所提出的广义方案始终优于固定阈值方案，而本地权重的引入在性能损失最小化的前提下，相比传统方案显著降低了系统开销与计算成本。