We study the problem of user-scheduling and resource allocation in distributed multi-user, multiple-input multiple-output (MIMO) networks implementing user-centric clustering and non-coherent transmission. We formulate a weighted sum-rate maximization problem which can provide user proportional fairness. As in this setup, users can be served by many transmitters, user scheduling is particularly difficult. To solve this issue, we use block coordinate descent, fractional programming, and compressive sensing to construct an algorithm that performs user-scheduling and beamforming. Our results show that the proposed framework provides an 8- to 10-fold gain in the long-term user spectral efficiency compared to benchmark schemes such as round-robin scheduling. Furthermore, we quantify the performance loss due to imperfect channel state information and pilot training overhead using a defined area-based pilot-reuse factor.

翻译：我们研究分布式多用户、多投入多产出(MIMO)网络中的用户排期和资源分配问题,这些网络实施以用户为中心的集群和非连贯传输;我们提出加权总和最大化问题,可以提供用户比例公平性;正如在这种设置中,许多发射机可以为用户服务,用户排期特别困难;为解决这一问题,我们使用块式协调下限、分数编程和压缩遥感来构建一种算法,进行用户排期和编程。我们的结果显示,拟议框架与圆杆排期等基准计划相比,在长期用户光谱效率方面提供了8至10倍的收益。此外,我们用一个明确的地区试点使用系数来量化由于不完善的国家信息和试点培训管理导致的业绩损失。