The uplink sum-throughput of distributed massive multiple-input-multiple-output (mMIMO) networks depends majorly on Access point (AP)-User Equipment (UE) association and power control. The AP-UE association and power control both are important problems in their own right in distributed mMIMO networks to improve scalability and reduce front-haul load of the network, and to enhance the system performance by mitigating the interference and boosting the desired signals, respectively. Unlike previous studies, which focused primarily on addressing these two problems separately, this work addresses the uplink sum-throughput maximization problem in distributed mMIMO networks by solving the joint AP-UE association and power control problem, while maintaining Quality-of-Service (QoS) requirements for each UE. To improve scalability, we present an l1-penalty function that delicately balances the trade-off between spectral efficiency (SE) and front-haul signaling load. Our proposed methodology leverages fractional programming, Lagrangian dual formation, and penalty functions to provide an elegant and effective iterative solution with guaranteed convergence. Extensive numerical simulations validate the efficacy of the proposed technique for maximizing sum-throughput while considering the joint AP-UE association and power control problem, demonstrating its superiority over approaches that address these problems individually. Furthermore, the results show that the introduced penalty function can help us effectively control the maximum front-haul load.

翻译：分布式大规模多输入多输出网络的 uplink 总吞吐量主要取决于接入点与用户设备的关联及功率控制。AP-UE关联与功率控制在分布式mMIMO网络中各自具有重要性：前者可提升网络可扩展性并降低前传负载，后者则能通过抑制干扰和增强期望信号来优化系统性能。不同于以往侧重独立处理这两个问题的研究，本文通过求解联合AP-UE关联与功率控制问题，在满足每个UE服务质量要求的前提下，实现分布式mMIMO网络的上行总吞吐量最大化。为提升可扩展性，我们引入l1惩罚函数来精细平衡频谱效率与前传信令负载之间的权衡。所提方法结合分数规划、拉格朗日对偶公式及惩罚函数，构建了优雅且有效的迭代求解方案，并保证收敛性。大量数值仿真验证了该技术在联合优化AP-UE关联与功率控制时最大化总吞吐量的有效性，证明其优于独立处理这些问题的传统方法。此外，结果表明所引入的惩罚函数能有效控制最大前传负载。