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：本文针对下行用户-centric无蜂窝大规模多输入多输出系统中，为降低前传信令与计算复杂度，用户仅由一组接入点服务的情形，提出了联合设计用户设备关联与功率控制的新颖方案。为最大化用户总频谱效率，我们在单接入点发射功率、服务质量速率需求、最大前传信令负载及每接入点最大服务用户数等约束下，构建了一个混合整数非凸优化问题。为高效求解该问题，我们根据无蜂窝大规模MIMO系统的规模差异提出两种方案：针对小规模系统，采用连续凸逼近方法获得稳态解，并开发基于学习的JointCFNet以降低计算复杂度；针对大规模系统，提出基于加速投影梯度的低复杂度次优算法。数值结果表明，在小规模系统中，JointCFNet可获得与连续凸逼近算法相近的性能并显著缩短运行时间；在大规模系统中，加速投影梯度法验证其运行速度远快于连续凸逼近算法，且所获频谱效率性能接近连续凸逼近方法。此外，加速投影梯度法的中值总频谱效率可达启发式基线方案的约2.8倍。