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Signal Detection for Enhanced Spatial Modulation-Based Communication: A Block Deep Neural Network Approach

Author

Listed:
  • Shaopeng Jin

    (The School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China)

  • Yuyang Peng

    (The School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China)

  • Fawaz AL-Hazemi

    (Department of Computer and Networking Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia)

  • Mohammad Meraj Mirza

    (Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

Abstract

As a novel variant of spatial modulation (SM), enhanced SM (ESM) provides higher spectral efficiency and improved bit error rate (BER) performance compared to SM. In ESM, traditional signal detection methods such as maximum likelihood (ML) have the drawback of high complexity. Therefore, in this paper, we try to solve this problem using a deep neural network (DNN). Specifically, we propose a block DNN (B-DNN) structure, in which smaller B-DNNs are utilized to identify the active antennas along with the constellation symbols they transmit. Simulation outcomes indicate that the BER performance related to the introduced B-DNN method outperforms both the minimum mean-square error (MMSE) and the zero-forcing (ZF) methods, approaching that of the ML method. Furthermore, the proposed method requires less computation time compared to the ML method.

Suggested Citation

  • Shaopeng Jin & Yuyang Peng & Fawaz AL-Hazemi & Mohammad Meraj Mirza, 2025. "Signal Detection for Enhanced Spatial Modulation-Based Communication: A Block Deep Neural Network Approach," Mathematics, MDPI, vol. 13(4), pages 1-12, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:4:p:596-:d:1588832
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