Transmit Antenna Selection for Millimeter-Wave MIMO System Based on GBDT

In millimeter-wave multiple-input multipleoutput(MIMO)systems,transmit antenna selection(TAS)can be employed to reduce hardware complexity and energy consumption when the number of antennas becomes very large.However,the traditional exhaustive search TAS tries all possible antenna combinations which causes high computational complexity.It may limit its application in practice.The main advantage of machine learning(ML)lies in the capability of establishing underlying relations between system parameters and objective,hence being able to shift the computation burden of real-time processing to the offline training phase.Based on this advantage,introducing ML to TAS is a promising way to tackle the high computational complexity problem.Although the existing ML-based algorithms try to approach the optimal performance,there is still a large room for improvement.In this paper,considering the secure transmission of the system,we model the TAS problem as a multi-class classification problem and propose an efficient antenna selection algorithm based on gradient boosting decision tree(GBDT),in which we consider the system security capacity and computational complexity as the optimization objectives.On the one hand,the system security performance is improved because its achievable security capacity is close to the traditional exhaustive search algorithm.On the other hand,compared with the exhaustive search algorithm and existing ML-based algorithms,the training efficiency is significantly improved with the complexity O(N),where N is the number of transmitting antenna.In addition,the performance of the proposed algorithm is evaluated in mmWave MIMO system by employing New York University simulator(NYUSIM)model,which is based on the real channel measurement.Performance analysis show that the proposed GBDT-based scheme can effectively improve the system secrecy capacity and significantly reduce the computational complexity.