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（mMIMO）网络的上行链路总吞吐量主要取决于接入点（AP）-用户设备（UE）关联与功率控制。在分布式mMIMO网络中，AP-UE关联与功率控制各自均是重要问题：前者旨在提升网络可扩展性并降低前传负载，后者则通过抑制干扰与增强期望信号来提升系统性能。与以往主要分别研究这两个问题的文献不同，本工作通过联合求解AP-UE关联与功率控制问题来处理分布式mMIMO网络中的上行链路总吞吐量最大化问题，同时保障各UE的服务质量（QoS）要求。为提升可扩展性，我们提出了一种l1-惩罚函数，可精细平衡频谱效率（SE）与前传信令负载之间的权衡关系。所提出的方法融合分式规划、拉格朗日对偶构造与惩罚函数，构建出具有收敛保证的优雅高效迭代解。大量数值仿真验证了所提技术在联合考虑AP-UE关联与功率控制问题时最大化总吞吐量的有效性，证明了其相较于单独处理这两个问题的方法的优越性。此外，结果表明引入的惩罚函数能有效控制最大前传负载。