Joint dynamic clustering and user scheduling for downlink cloud radio access network with limited feedback

In limited feedback-based Cloud-RAN (C-RAN) systems, the inter-cluster and intra-cluster interference together with the quantification error can seriously deteriorates the system spectral efficiency. We, in this paper, propose an efficient three-phase framework and corresponding algorithms for dealing with this problem. Firstly, a greedy scheduling algorithm based on the lower bound of the ergodic rate is performed for generating an elementary cluster in the first phase. And then the elementary cluster is divided into many small clusters according to the following proposed algorithms based on the short term instantaneous information in the second phase. In the end, based on the limited feedback two zero-forcing (ZF) precoding strategies are adopted for reducing the intra-cluster interference in the third phase. The provided Monte Carlo simulations show the effectiveness of our proposed algorithms in the respect of system spectral efficiency and average user rate.