Efficient uplink processing in distributed massive multiple-input multiple-output (D-mMIMO) systems requires both effective local combining and scalable decoding to significantly mitigate inter-user interference. Recent zero-forcing (ZF)-based combining schemes, such as partial full-pilot ZF (PFZF) and protected weak PFZF (PWPFZF), rely on heuristic threshold-based user grouping that may lead to inefficient utilization of spatial degrees of freedom across access points (APs). To address this limitation, we propose adaptive pilot-aware local combining strategies, generalized PFZF (G-PFZF) and generalized PWPFZF (G-PWPFZF), that dynamically allocate spatial degrees of freedom based on local channel conditions and replace heuristic grouping with a decentralized pilot-level optimization framework. Thus providing substantial performance gains over conventional PFZF and PWPFZF. Further, centralized decoding has recently emerged as a promising technique for interference suppression in D-mMIMO systems. However, it incurs substantial fronthaul overhead and computational costs. We develop a decentralized large-scale fading decoding (d-LSFD) scheme in which each AP computes LSFD weights using only locally available channel statistics. We derive a lower bound on the signal-to-interference-plus-noise ratio that explicitly quantifies the performance gap between the proposed d-LSFD scheme and centralized LSFD (c-LSFD), and identifies conditions under which the proposed decentralized solution approaches the centralized optimum. Numerical results demonstrate that the proposed generalized combining and the d-LSFD scheme together achieve significantly higher sum spectral efficiency in comparison to any combination of existing local combining and decoding schemes, while also substantially reducing the computational cost and fronthaul overhead.

翻译：分布式大规模多输入多输出（D-mMIMO）系统中的高效上行链路处理需要有效的本地合并和可扩展的解码机制，以显著抑制用户间干扰。现有的基于迫零（ZF）的合并方案，如部分全导频ZF（PFZF）和保护型弱PFZF（PWPFZF），依赖于基于启发式阈值的用户分组，可能导致接入点（AP）间空间自由度的低效利用。为解决这一局限性，我们提出了自适应导频感知的本地合并策略——广义PFZF（G-PFZF）和广义PWPFZF（G-PWPFZF），这些策略基于本地信道条件动态分配空间自由度，并以去中心化的导频级优化框架取代启发式分组，从而相比传统PFZF和PWPFZF实现了显著的性能提升。此外，集中式解码近年来已成为D-mMIMO系统中一种有前景的干扰抑制技术，但其带来了巨大的前传开销和计算成本。我们开发了一种去中心化的大尺度衰落解码（d-LSFD）方案，其中每个AP仅使用本地可用的信道统计信息计算LSFD权重。我们推导了信干噪比的下界，该下界明确量化了所提出的d-LSFD方案与集中式LSFD（c-LSFD）之间的性能差距，并确定了所提出的去中心化方案逼近集中式最优解的条件。数值结果表明，与现有本地合并和解码方案的任何组合相比，所提出的广义合并方案与d-LSFD方案共同实现了显著更高的总频谱效率，同时大幅降低了计算成本和前传开销。