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Deep Learning-Based Codebook Designs for Generalized Space Shift Keying Systems
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DOI：10.1109/TVT.2021.3128693.png)
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Abstract
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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.
Keywords:
Transmitting antennas
Training
Receiving antennas
Design methodology
Deep learning
Neurons
Simulation
Generalized space shift keying
deep learning
codebook
multiple-input multiple-output
Journal
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7.1
Papers:
1.8W
Citations:
6.6W
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