Sparse Channel Estimation for DCO-OFDM VLC Systems in the Presence of Clipping Noise

Bektas, Ekin Basak; Panayırcı, Erdal

doi:10.21203/rs.3.rs-380210/v1

“…In [E. B. Bektas and E. Panayirci 2021], channel sparsity is exploited to VLC and DCO-OFDM in attendance of the noise clipping. The simulation results illustate the converges of algorithm in the top of two iterations and that leads to enhance MSE and BER performance, outperforming CE algorithms, having no clipping noise mitigation capability.…”

Section: Introductionmentioning

confidence: 99%

Enhanced deep learning based channel estimation for indoor VLC systems

Salama

¹

,

Aly

²

,

Amer

³

2022

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This paper aims to improve the channel estimation (CE) in the indoor visible light communication (VLC) system. We propose a system that depends on a comparison between Deep Neural Networks (DNN) and Kalman Filter (KF) algorithm for two optical modulation techniques; asymmetrically clipped optical-orthogonal frequency-division multiplexing (ACO-OFDM) and direct current optical-orthogonal frequency division multiplexing (DCO-OFDM). The channel estimation can be evaluated by changing the rate of errors in the received bits, where increased error means a performance decrease of the system and vice versa. Receiving less errors at the receiver indicates improved channel estimation and system performance. Thus, the main aim of our proposal is decreasing the error rate by using different estimators. Using the simulation results with the metric parameter of bit error rate (BER) aims to determine the improvement ratio between different systems. The proposed model is trained with OFDM signal samples with labels, where the labels represent the received signal after applying OFDM travelling across the medium. At a BER = 10–3 with DCO-OFDM, the DNN outperforms KF with 1.6 dB (7.6%) at the bit energy per noise $$({{\varvec{E}}}_{{\varvec{b}}}/{{\varvec{N}}}_{{\varvec{o}}})$$ ( E b / N o ) axis. Also, for ACO-OFDM at BER = 10–3, the DNN achieves better results than KF by about 1.3 dB (8.12%) at the $$({{\varvec{E}}}_{{\varvec{b}}}/{{\varvec{N}}}_{{\varvec{o}}}).$$ ( E b / N o ) . At different values of M in QAM, the DNN outperforms KF for ACO-OFDM by average improvement of ~ 1 dB (~ 11.5%).

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“…Also, using ACO-OFDM decresed the BER than that done in DCO-OFDM. In Bektas and Panayirci (2021), channel sparsity exploited the DCO-OFDM for indoor VLC systems with a clipping noise. The simulation results showed that the algorithm converges in a maximum of two iterations and that gets excellent MSE and BER performance, outperforming CE algorithms, having no clipping noise mitigation capability.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based channel estimation optimization in VLC systems

Salama

¹

,

Aly

²

,

Amer

³

2022

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This paper aims to improve the channel estimation (CE) in the indoor visible light communication system. The proposal of this paper deals with a system that depends on a comparison between Deep Neural Network (DNN), Yolo v3, and Kalman filter (KF) algorithm, for two optical modulation techniques; asymmetrically clipped optical-orthogonal frequency-division multiplexing (ACO-OFDM) and direct current optical-orthogonal frequency division multiplexing (DCO-OFDM). The CE can be evaluated by the error rates in the received bits, where increased error means a performance decrease of the system and vice versa. Receiving less errors at the receiver indicates improved CE and system performance. Hence, the main aim of our work is to decrease the error rate by using different estimators. Furthermore, we apply automatic hyper-parameter approach and Bayesian optimization, to Yolo v3 model to improve the system performance and reduce the positioning error. The metric parameter of bit error rate (BER) aims to determine the improvement ratio in different systems. The model in this paper is based on training with OFDM samples of signal with labels which are received and are corresponding to the signals of OFDM. At a BER = 10−3 with DCO-OFDM, the DNN outperforms KF with 1.7 dB (8.09%) at the bit energy per noise $$(E_{b} {/}N_{o} )$$ ( E b / N o ) axis. Also, for ACO-OFDM at BER = 10−3, the DNN achieves better results than KF by about 1.9 dB (11.8%) at the $$(E_{b} {/}N_{o} ){ }$$ ( E b / N o ) axis. For different values of M in QAM, the DNN outperforms KF for ACO-OFDM by average improvement of ~ 1.2 dB (~ 13%).

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OFDM-Based VLC Systems: A Systematic Review

Ali,

Abdulkafi

2024

BIO Web Conf.

0

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Visible Light Communications (VLC) represents a new technology of wireless communications allowing high data rate, high-speed internet access, green and friendly communication system, especially for indoor users, where the use of Light Emitting Diodes (LEDs) is growing as a viable alternative to traditional illumination. Orthogonal frequency division multiplexing (OFDM) is a promising modulation format for optical wireless communication (OWC); however, precise channel estimation is required for synchronization and equalization. Moreover, one of the challenging issues for OFDM is its high Peak-to-Average Power Ratio (PAPR) due to the superposition of all subcarriers; therefore, the system requires a high linearity and dynamic range. This paper provides a systematic overview of OFDM-based VLC systems, highlighting the essential aspects of PAPR reduction and channel estimation techniques. Several technical challenges have been addressed to realize the full potential of OFDM-based VLC technology. Moreover, we provide new insights into the over-explored and under-explored areas, which lead us to identify open research problems of VLC based on OFDM. Concisely, this paper serves as a guide and a starting point for researchers willing to research VLC using OFDM.

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Sparse Channel Estimation for DCO-OFDM VLC Systems in the Presence of Clipping Noise

Cited by 3 publications

References 17 publications

Enhanced deep learning based channel estimation for indoor VLC systems

Enhanced deep learning based channel estimation for indoor VLC systems

Deep learning based channel estimation optimization in VLC systems

OFDM-Based VLC Systems: A Systematic Review

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