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.

翻译：上行分布式大规模多输入多输出网络中的一个主要瓶颈在于本地合并方案的性能次优，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，从根本上摆脱了LSFD框架，允许每个接入点独立计算抑制干扰的本地权重，并在数据传输前将其应用于数据估计。此外，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本地总频谱效率优化自适应且独立地确定其合并策略，该策略将用户设备分类为强用户或弱用户，从而消除了PFZF和PWPFZF方案中使用的固定阈值。为提升可扩展性，引入了基于导频而非用户的合并向量，并在使用相同导频的用户间共享。文中推导了所提方案对应的闭式频谱效率表达式。数值结果表明，所提方案在性能上持续优于固定阈值方案，而本地权重的引入在性能损失较小的情况下实现了更低的开销和计算成本。