JCSE, vol. 17, no. 2, pp.71-79, 2023

DOI: http://dx.doi.org/10.5626/JCSE.2023.17.2.71

Flip-OFDM Optical MIMO Based VLC System Using ML/DL Approach

Mahesh Kumar Jha, Rubini P, and Navin Kumar
School of Engineering and Technology, CMR University, Bengaluru, Karnataka, India; Department of ECE, CMR Institute of Technology, Bengaluru, Karnataka, India Department of CSE, School of Engineering and Technology, CMR University, Bengaluru, Karnataka, India Department of ECE, Amrita School of Engineering, Amrita University, Bengaluru, Karnataka, India

Abstract: Flip orthogonal frequency division multiplexing (OFDM) is a variation of OFDM which modifies the bipolar OFDM signal in the unipolar signal by flipping the negative sign of the subcarriers. Flip-OFDM in multiple input multiple output (MIMO) visible light communication (VLC) system improves the orthogonally of the subcarriers and reduces bit error rate, which results in a higher data rate and a more robust communication system. Machine learning (ML) and deep learning (DL) techniques are being used to improve various aspects of VLC systems such as modulation, channel estimation, and MIMO design, which can result in more robust and efficient communication systems. In this paper, deep neural network (DNN), convolution neural network (CNN) and long short-term memory (LSTM) algorithms are used to analyze flip-OFDM optical MIMO VLC system. The MIMO techniques, repetitive coding (RC), spatial modulation (SM), generalized spatial modulation (generalized-SM) and spatial multiplexing (SMP) are analyzed with and without flip- OFDM. Simulation results showed that generalized-SM outperformed SM, SMP and RC with and without flip-OFDM. In both scenarios, CNN improved performance and outperformed LSTM and DNN.

Keyword: Visible light communication; Flip-OFDM; MIMO techniques; CNN; DNN; LSTM

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