Machine Learning Approach for Improved Downlink Coordinated Multipoint in Heterogeneous Networks

We propose a method for practical downlink coordinated multipoint (DL CoMP) implementation in 4G LTE/LTE-A systems using supervised machine learning. Contributions of this paper are: 1) demonstrating that a support vector machine classifier can learn the optimal conditions at which DL CoMP can be dynamically triggered, 2) improving user throughput in DL CoMP as a result of learning the optimal triggering conditions of DL CoMP, and 3) showing that the machine learning approach is scalable to more than a single macro. The simulation results show an improvement in the pico cell average and edge throughputs and a reduction in the downlink block error rate due to the informed triggering of the multiple radio streams as part of DL CoMP as learned from the support vector machine.