This work proposes novel approaches that jointly design user equipment (UE) association and power control (PC) in a downlink user-centric cell-free massive multiple-input multiple-output (CFmMIMO) network, where each UE is only served by a set of access points (APs) for reducing the fronthaul signalling and computational complexity. In order to maximize the sum spectral efficiency (SE) of the UEs, we formulate a mixed-integer nonconvex optimization problem under constraints on the per-AP transmit power, quality-of-service rate requirements, maximum fronthaul signalling load, and maximum number of UEs served by each AP. In order to solve the formulated problem efficiently, we propose two different schemes according to the different sizes of the CFmMIMO systems. For small-scale CFmMIMO systems, we present a successive convex approximation (SCA) method to obtain a stationary solution and also develop a learning-based method (JointCFNet) to reduce the computational complexity. For large-scale CFmMIMO systems, we propose a low-complexity suboptimal algorithm using accelerated projected gradient (APG) techniques. Numerical results show that our JointCFNet can yield similar performance and significantly decrease the run time compared with the SCA algorithm in small-scale systems. The presented APG approach is confirmed to run much faster than the SCA algorithm in the large-scale system while obtaining an SE performance close to that of the SCA approach. Moreover, the median sum SE of the APG method is up to about 2.8 fold higher than that of the heuristic baseline scheme.

翻译：本文提出了新颖的方法，在下行以用户为中心的无蜂窝大规模多输入多输出（CFmMIMO）网络中联合设计用户设备（UE）关联和功率控制（PC），其中每个UE仅由一组接入点（AP）服务，以减少前传信令和计算复杂度。为了最大化UE的频谱效率（SE）总和，我们在每个AP的发射功率、服务质量速率要求、最大前传信令负载以及每个AP服务的最大UE数量的约束下，制定了一个混合整数非凸优化问题。为了高效解决所提出的问题，我们根据CFmMIMO系统的不同规模提出了两种方案。对于小型CFmMIMO系统，我们提出了一种逐次凸近似（SCA）方法来获得平稳解，并开发了一种基于学习的方法（JointCFNet）以降低计算复杂度。对于大规模CFmMIMO系统，我们提出了一种使用加速投影梯度（APG）技术的低复杂度次优算法。数值结果表明，在小型系统中，我们的JointCFNet能够获得与SCA算法相似的性能，并显著缩短运行时间。所提出的APG方法在大规模系统中被证实比SCA算法运行快得多，同时获得接近SCA方法的SE性能。此外，APG方法的中位总SE比启发式基线方案高出约2.8倍。