Deep Learning-Based Codebook Designs for Generalized Space Shift Keying Systems

초록

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.

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