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Deep Learning-Based Codebook Designs for Generalized Space Shift Keying Systems
- Authors
- Huang, Di; Jiang, Xue-Qin; Lee, Inkyu; Hai, Han
- Issue Date
- 1월-2022
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Transmitting antennas; Training; Receiving antennas; Design methodology; Deep learning; Neurons; Simulation; Generalized space shift keying; deep learning; codebook; multiple-input multiple-output
- Citation
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.71, no.1, pp.1038 - 1042
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- Volume
- 71
- Number
- 1
- Start Page
- 1038
- End Page
- 1042
- ISSN
- 0018-9545
- Abstract
- 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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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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