Multi-Label Learning Based Antenna Selection in Massive MIMO Systems

Antenna selection (AS) is a signal processing technology that can greatly reduce the hardware complexity of multi-antenna systems. Specifically, AS can decrease the number of required radio frequency chains by activating only a subset of the available antennas in each transmission slot. However, optimal AS suffers from a high computational complexity that increases exponentially with the scale of the antenna array. In this paper, we propose a low-complexity AS algorithm based on multi-label learning (MLL), where a deep neural network is employed to determine the set of selected antennas for a given channel matrix. Specifically, the MLL network combines deep canonical correlation analysis and an autoencoder in a unified network structure, which can extract the low dimensional features of channel matrix as well as the interdependency among selected antennas, so as to achieve an accurate prediction of the set of selected antennas with a relatively small-scale learning model. Simulation results show that, in comparison with the convex relaxation based method, our proposed MLL-based method can achieve comparable capacity with significantly reduced computation time.

Automated summary

This paper proposes a low-complexity antenna selection algorithm based on multi-label learning, using a deep neural network to extract low-dimensional features of the channel matrix for accurate prediction of selected antenna sets. Compared to traditional methods, the new algorithm achieves comparable capacity while reducing computation time.