Downlink Radio Resource Allocation With Mu-Mimo And Carrier Aggregation In 5g Networks

Taking plenty benefit of the new frequency bands in 5G networks requires the design of efficient carrier aggregation (CA) methods compliant with the expected large resource pool dimensions, especially when realized jointly with Multi-User (MU)-MEMO feature. In this paper, we propose a MU-MIMO aware CA (MMCA) resource allocation solution for SG systems that assigns the available resources in time, frequency and MEMO dimensions under realistic traffic and wireless system architecture constraints. We design a novel user grouping approach that accounts for available input constraints defined by the system design. These constraints are generated by higher layers such as user priorities based on their requests and previously allocated rates as well as lower layers such as coupling factors between users based on the used beamformers and physical layer signal processing. In addition, a hierarchical resource scheme is proposed in order to cope with the high computational complexity through optimization space reduction and parallelization. Obtained simulation results under 5G assumptions and realistic constraints clearly assess the large gains achieved by our solution in terms of user throughput and fairness comparing to a set of baseline schemes.