Smart Detection Using the Cascaded Artificial Neural Network for OFDM with Subcarrier Number Modulation

Li, Jun; Dang, Shuping; Wen, Miaowen; Zhang, Zhenrong; Li, Qiang

Smart Detection Using the Cascaded Artificial Neural Network for OFDM with Subcarrier Number Modulation

Files

Double (1).pdf (588.74 KB)

Type

Article

Authors

Li, Jun
Dang, Shuping
Wen, Miaowen
Zhang, Zhenrong
Li, Qiang

KAUST Department

Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

Online Publication Date

2021-03-01

Print Publication Date

2021-06

Date

2021-03-01

Abstract

Since the proposal of orthogonal frequency-division multiplexing with subcarrier number modulation (OFDM-SNM), the high detection complexity of this novel modulation scheme becomes a major issue degrading its applicability in practice. To provide an easy-to-implement and low-complexity solution to the detection of the OFDM-SNM signal, we design a cascaded artificial neural network (CANN) at the OFDM-SNM receiver to decouple the detection of subcarrier number pattern (SNP) and constellation symbols on the active subcarriers. This is the first time that the CANN design is introduced to assist in detecting parallel signals. Numerical simulations verify the effectiveness and efficiency of the proposed smart detection scheme and show that the CANN based smart detection with adequate training can yield comparable error performance to the optimal maximum-likelihood detection at much lower complexity.

Citation

Li, J., Dang, S., Wen, M., Zhang, Z., & Li, Q. (2021). Smart Detection Using the Cascaded Artificial Neural Network for OFDM with Subcarrier Number Modulation. IEEE Wireless Communications Letters, 1–1. doi:10.1109/lwc.2021.3062686

Acknowledgements

This work was supported in part by National Natural Science Foundation of China under Grants 61872102 and 61661004, in part by the International Collaborative Research Program of Guangdong Science and Technology Department under Grant 2020A0505100061, in part by the Pearl River Nova Program of Guangzhou under Grant 201806010171, in part by the Fundamental Research Funds for the Central Universities under Grant 2019SJ02, and in part by the Guangxi Science Key Research and Development Project under Grant AB1850043.