Adaptive Local Combining with Decentralized Decoding for Distributed Massive MIMO

Abstract

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.