  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.
