Efficient Selection on Spatial Modulation Antennas: Learning or Boosting

被引：14
|
作者：
Zhang, Yue [1 ,2 ]
Wang, Jintao [1 ,2 ]
Wang, Xuesi [1 ,2 ]
Xue, Yonglin [1 ,2 ]
Song, Jian [1 ,2 ]
机构：
[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Beijing 100084, Peoples R China
来源：
IEEE WIRELESS COMMUNICATIONS LETTERS | 2020年 / 9卷 / 08期
关键词：
Transmitting antennas; Modulation; Boosting; MIMO communication; Artificial neural networks; Decision trees; Receiving antennas; Spatial modulation (SM); transmit antenna selection (TAS); deep learning; neural network; gradient boosting decision tree (GBDT);
D O I：
10.1109/LWC.2020.2986974
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this letter, a novel deep learning-based transmit antenna selection (TAS) scheme for the multiple-input multiple-output (MIMO) with spatial modulation (SM) system is proposed. We formulate the generalized TAS pipeline in both neural networks (NN) and gradient boosting decision trees (GBDT), in which the importance of different features reflecting the different elements from channel state information (CSI) is analyzed regarding to the empirical data as well. Furthermore, the bit error rate (BER) performance and the complexity comparison of two structures is investigated. Simulation results confirm that GBDT can be efficiently implemented for real-time application with near-optimal performance.
引用
收藏
页码：1249 / 1252
页数：4
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