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关联与功率控制本身就是两个重要问题：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、增强期望信号来提升系统性能。与以往主要分别处理这两个问题的研究不同，本文通过解决联合AP-UE关联与功率控制问题，在满足每个UE服务质量（QoS）需求的前提下，最大化分布式mMIMO网络的上行总吞吐量。为提升可扩展性，我们引入了一种l1惩罚函数，能够精细地平衡频谱效率（SE）与前传信令负载之间的权衡。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，提供了一种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所提技术在联合考虑AP-UE关联与功率控制问题下最大化总吞吐量的有效性，结果表明其优于分别处理这些问题的现有方法。此外，结果还表明所引入的惩罚函数可帮助我们有效控制最大前传负载。