Modular extremely large-scale MIMO (XL-MIMO) architectures combined with wireless fronthaul provide a scalable alternative to monolithic arrays, but their performance is sensitive to hardware impairments and resource allocation strategies. In this paper, we consider a distributed two-phase processing framework for modular XL-MIMO systems employing amplify-and-forward wireless fronthaul under practical hardware constraints. We jointly model access-side and fronthaul-side distortions and formulate a weighted minimum mean-square error (WMMSE)-based optimization problem that maximizes the uplink sum spectral efficiency (SE) by jointly adjusting UE transmit powers and fronthaul amplification levels. The resulting algorithm alternates between distortion-aware receiver design and convex power-control updates. Numerical results demonstrate that the proposed joint optimization significantly improves spectral efficiency compared to fixed transmission strategies, particularly when the CPU has a moderate number of antennas, while also quantifying the relative impact of access and fronthaul impairments.

翻译：模块化超大规模MIMO（XL-MIMO）架构结合无线前传为大规模阵列提供了可扩展替代方案，但其性能对硬件损伤和资源分配策略高度敏感。本文针对采用放大转发无线前传的模块化XL-MIMO系统，在实用硬件约束下提出分布式两阶段处理框架。我们联合建模接入侧与前传侧失真，并构建基于加权最小均方误差（WMMSE）的优化问题，通过联合调整用户发射功率与前传放大增益来最大化上行频谱效率（SE）。所提算法在失真感知接收机设计与凸功率控制更新之间交替迭代。数值结果表明，相较于固定传输策略，所提出的联合优化方案可显著提升频谱效率（特别是在中央处理器配置中等数量天线时），同时量化了接入侧与前传侧损伤的相对影响。