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 the AP-UE association or power control 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 while meeting strict QoS criteria. 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 for uplink distributed mMIMO systems.

翻译：分布式大规模多输入多输出网络的上下行链路总吞吐量主要取决于接入点-用户设备关联和功率控制。AP-UE关联与功率控制均是分布式mMIMO网络中的重要问题：前者用于提升网络可扩展性并降低前传负载，后者通过抑制干扰和增强期望信号来改善系统性能。与以往主要单独处理AP-UE关联或功率控制问题的研究不同，本文通过联合解决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网络上行总吞吐量最大化。为提升可扩展性，我们引入一种l1惩罚函数，该函数能精细平衡频谱效率与前传信令负载之间的权衡关系。所提出的方法利用分数规划、拉格朗日对偶形式及惩罚函数，构建了一个优雅且高效的迭代求解方案，该方案在严格满足QoS准则的同时保证收敛性。大量数值仿真验证了所提技术在联合处理AP-UE关联与功率控制问题时最大化总吞吐量的有效性，并表明其优于单独处理这些问题的方案。此外，结果表明引入的惩罚函数可有效控制上行分布式mMIMO系统的最大前传负载。