Compressed Sensing Based 3D MU-MIMO Scheduling in Cognitive HetNets

Most of the already existing spectrum detection algorithms are based on each channel sensing, which lowers the spectrum detection efficiency and makes detecting the spectrum holes promptly very difficult. Traditional MIMO systems are based on 2D MIMO spatial channel models, but in fact the real channel is 3D characterized. This paper proposes a novel scheduling scheme for cylindrical antenna based on compressed sensing algorithm, where higher-order codebook is employed and inter-cell interference appears more spatially white along with a less performance loss. The proposed scheme can effectively sense the vacant spectrum and users based on 3D angular scan and schedule the users with optimized 3D codebook. Simulation results show that our proposed scheme can achieve a considerable gain in terms of throughput and users data rate, with significantly reduced system complexity and increased efficiency.