Full text

Turn on search term navigation

© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

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.

Details

Title
Signal Detection for Enhanced Spatial Modulation-Based Communication: A Block Deep Neural Network Approach
Author
Jin, Shaopeng 1 ; Yuyang Peng 1 ; AL-Hazemi, Fawaz 2   VIAFID ORCID Logo  ; Mohammad Meraj Mirza 3   VIAFID ORCID Logo 

 The School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China; [email protected] 
 Department of Computer and Networking Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia; [email protected] 
 Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia; [email protected] 
First page
596
Publication year
2025
Publication date
2025
Publisher
MDPI AG
e-ISSN
22277390
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3171091830
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.