We exploit a general cluster-based network architecture for a fronthaul-limited user-centric cell-free massive multiple-input multiple-output (CF-mMIMO) system under different degrees of cooperation among the access points (APs) to achieve scalable implementation. In particular, we consider a CF-mMIMO system wherein the available APs are grouped into multiple processing clusters (PCs) to share channel state information (CSI), ensuring that they have knowledge of the CSI for all users assigned to the given cluster for the purposes of designing resource allocation and precoding. We utilize the sum pseudo-SE metric, which accounts for intra-cluster interference and intercluster-leakage, providing a close approximation to the true sum achievable SE. For a given PC, we formulate two optimization problems to maximize the cluster-wise weighted sum pseudo-SE under fronthaul constraints, relying solely on local CSI. These optimization problems are associated with different computational complexity requirements. The first optimization problem jointly designs precoding, user association, and power allocation, and is performed at the small-scale fading time scale. The second optimization problem optimizes user association and power allocation at the large-scale fading time scale. Accordingly, we develop a novel application of modified weighted minimum mean square error (WMMSE)-based approach to solve the challenging formulated non-convex mixed-integer problems.

翻译：本文针对前传受限的用户中心型无蜂窝大规模多输入多输出（CF-mMIMO）系统，利用一种通用的基于簇的网络架构，在不同接入点（AP）协作程度下实现可扩展部署。具体而言，我们考虑一种CF-mMIMO系统，其中可用AP被分组为多个处理簇（PCs）以共享信道状态信息（CSI），确保每个簇内的AP知晓分配给该簇的所有用户的CSI，从而进行资源分配与预编码设计。我们采用能够计及簇内干扰与簇间泄漏的和伪频谱效率（pseudo-SE）度量，该度量为实际可达和频谱效率提供了紧密近似。对于给定PC，我们基于局部CSI，在前传约束下构建了两个优化问题以最大化簇级加权和伪频谱效率。这两个优化问题对应不同的计算复杂度要求：第一个优化问题在小尺度衰落时间尺度上联合设计预编码、用户关联与功率分配；第二个优化问题在大尺度衰落时间尺度上优化用户关联与功率分配。为此，我们提出了一种改进的加权最小均方误差（WMMSE）方法的新颖应用，以求解所构建的具有挑战性的非凸混合整数优化问题。