Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation
Type
ArticleKAUST Department
Communication Theory LabComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Date
2020Permanent link to this record
http://hdl.handle.net/10754/664608
Metadata
Show full item recordAbstract
Generalized Spatial Modulation (GSM) is being considered for high capacity and energy-efficient networks of the future. However, signal detection due to inter channel interference among the active antennas is a challenge in GSM systems and is the focus of this paper. Specifically, we explore the feasibility of using deep neural networks (DNN) for signal detection in GSM. In particular, we propose a block DNN (BDNN) based architecture, where the active antennas and their transmitted constellation symbols are detected by smaller sub- DNNs. After N-ordinary DNN detection, the Euclidean distancebased soft constellation algorithm is implemented. The proposed B-DNN detector achieves a BER performance that is superior to traditional block zero-forcing (B-ZF) and block minimum mean-squared error (B-MMSE) detection schemes and similar to that of classical maximum likelihood (ML) detector. Further, the proposed method requires less computation time and is more accurate than alternative conventional numerical methods.Citation
Albinsaid, H., Singh, K., Biswas, S., Li, C.-P., & Alouini, M.-S. (2020). Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation. IEEE Communications Letters, 1–1. doi:10.1109/lcomm.2020.3015810Journal
IEEE Communications LettersarXiv
2008.03612Additional Links
https://ieeexplore.ieee.org/document/9165095/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9165095
http://arxiv.org/pdf/2008.03612
Relations
Is Supplemented By:- [Software]
Albinsaid, H., Keshav Singh, Sudip Biswas, Chih-Peng Li, & Mohamed-Slim Alouini. (2020). Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation (Version 2.0) [Computer software]. Code Ocean. https://doi.org/10.24433/CO.3589818.V2. DOI: 10.24433/co.3589818.v2 Handle: 10754/667586
ae974a485f413a2113503eed53cd6c53
10.1109/LCOMM.2020.3015810