DHNN_Equal_01_15.pdf (4.17 MB)
Download file

Deep Hybrid Neural Network-Based Channel Equalization in Visible Light Communication

Download (4.17 MB)
journal contribution
posted on 21.06.2022, 10:17 authored by P Miao, G Chen, K Cumanan, Y Yao, JA Chambers

In this letter, the channel impairments compensation of visible light communication is formulated as a time sequence with memory prediction. Then we propose efficient nonlinear post equalization, using a combined long-short term memory (LSTM) and deep neural network (DNN), to learn the complicated channel characteristics and recover the original transmitted signal. We leverage the long-term memory parameters of LSTM to represent the sequence causality within the memory channel and refine the results by DNN to improve the reconstruction accuracy. Results demonstrate that the proposed scheme can robustly address the overall channel impairments and accurately recover the original transmitted signal with fairly fast convergence speed. Besides, it can achieve better balance between performance and complexity than that of the conventional competitive approaches, which demonstrates the potential and validity of the proposed methodology for channel equalization.

Funding

Key Project of Science and Technology of Hainan (Grant Number: ZDKJ2019003)

10.13039/501100007129-Natural Science Foundation of Shandong Province (Grant Number: ZR2019BF001)

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61801257 and 61901241)

History

Citation

IEEE Communications Letters, 2022, DOI: 10.1109/LCOMM.2022.3172219

Author affiliation

School of Engineering, University of Leicester

Version

AM (Accepted Manuscript)

Published in

IEEE Communications Letters

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

1089-7798

eissn

1558-2558

Copyright date

2022

Available date

21/06/2022

Language

en