返回
Deep Learning-Based Codebook Designs for Generalized Space Shift Keying Systems
DOI:10.1109/TVT.2021.3128693.png)
摘要
En 中文
In this paper, we propose a novel deep learning (DL)-based codebook design method for generalized space shift keying (GSSK) systems. In this DL-based method, the transmitter and receiver of GSSK systems are designed based on deep neural network (DNN). By training the DNN in an end-to-end manner, the DL-based method can adaptively generate suitable binary codewords and combine them into a codebook for GSSK systems. Simulation results show that the proposed DL-based method obtains better performance compared to conventional approaches.
Keyword:
Transmitting antennas
Training
Receiving antennas
Design methodology
Deep learning
Neurons
Simulation
Generalized space shift keying
deep learning
codebook
multiple-input multiple-output
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
引用论文
A Survey on Spatial Modulation in Emerging Wireless Systems: Research Progresses and Applications新兴无线系统空间调制研究进展与应用综述
Deep Learning for Wireless Physical Layer: Opportunities and Challenges无线物理层深度学习: 机遇与挑战
CHINA COMMUNICATIONS
IF3.1

