分布式大规模 MIMO 的联合 AP-UE 关联与功率因子优化
Joint AP-UE Association and Power Factor Optimization for Distributed Massive MIMO
AI总结：
本文针对分布式大规模 MIMO 网络，提出联合优化 AP-UE 关联与功率控制的迭代方法，通过 l1 惩罚函数平衡频谱效率与前传负载，在满足 QoS 约束下最大化上行总吞吐量。
摘要：
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）关联和功率控制。在分布式 mMIMO 网络中，AP-UE 关联和功率控制各自都是重要问题：前者用于提高可扩展性并减少网络的前传负载，后者通过抑制干扰和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，本工作通过求解联合 AP-UE 关联与功率控制问题，在满足每个 UE 的服务质量（QoS）要求的同时，解决分布式 mMIMO 网络中的上行链路总吞吐量最大化问题。为了提高可扩展性，我们提出了一种 l1 惩罚函数，该函数精细地平衡了频谱效率（SE）与前传信令负载之间的权衡。我们提出的方法利用分数规划、拉格朗日对偶构造和惩罚函数，提供了一种优雅且有效的迭代解决方案，并具有收敛保证。大量数值仿真验证了所提技术在考虑联合 AP-UE 关联与功率控制问题时最大化总吞吐量的有效性，证明了其优于分别处理这些问题的现有方法。此外，结果表明，所引入的惩罚函数能够帮助我们有效地控制最大前传负载。
英文摘要：
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.
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