Low spatial complexity adaptive artificial neural network post-equalization algorithms in MIMO visible light communication systems | Zendy

Yiheng Zhao | Zendy; Peng Zou | Zendy; Zhixue He | Zendy; Ziwei Li | Zendy; Nan Chi | Zendy

Open Access

Low spatial complexity adaptive artificial neural network post-equalization algorithms in MIMO visible light communication systems

Author(s) -

Yiheng Zhao,

Peng Zou,

Zhixue He,

Ziwei Li,

Nan Chi

Publication year - 2021

Publication title -

optics express

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.394

H-Index - 271

ISSN - 1094-4087

DOI - 10.1364/oe.440155

Subject(s) - mimo , visible light communication , quadrature amplitude modulation , artificial neural network , equalization (audio) , computer science , bit error rate , algorithm , superposition principle , electronic engineering , telecommunications , decoding methods , artificial intelligence , mathematics , optics , engineering , physics , beamforming , mathematical analysis , light emitting diode

In this paper, we experimentally propose a feasible and low spatial complexity adaptive artificial neural network (AANN) post-equalization algorithm in MIMO visible light communication (VLC) systems. By introducing the power ratio and the MIMO least mean square (MIMO-LMS) post-equalization algorithm into the structure design process of the artificial neural network (ANN) post-equalization algorithm, we reduced the spatial complexity of the post-ANN equalization algorithm to less than 10%. At the same time, the bit error rate (BER) performance of AANNs did not decrease. Finally, we achieved a data rate of 2.1Gbps in the AANN equalized 16QAM superposition coding modulation (SCM) and carrier-less amplitude-phase (CAP) single-receiver MIMO (SR-MIMO) VLC system.

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