Abstract
Recent trends in the field of communication science show that most devices are built and governed by Visible Light Communication (VLC) and Light Emitting Diodes (LED). The communication process depends on source characteristics and bandwidth restriction, and effective methods of modulation and demodulation can enhance data rate. Attractive and practical Carrierless Amplitude-Phase (CAP) modulation is used for higher efficiency and easier implementation. However, the performance of the CAP-VLC system gets adversely affected by factors like jamming, scattering, and low sensitivity to receiver signal. Hence, this paper examines the mitigation of available system degrading factors, based on a high-speed, extreme learning machine algorithm (ELM). Prediction analysis and simulation are processed using trained raw dataset for CAP demodulation, to prove the level of performance in terms of accuracy as 92%, under various parametric conditions. This system that is proposed can achieve bit error rate (BER) of 40% to 50% of noise ratio and can be effectively used in sectors like medical health care, banking, and finance.
Highlights
T He rapidly emerging, huge volumes of mobile and edge devices have lead to the shortage of bandwidth and we are faced with a bottleneck in our attempts to enhance network capacity
Implementation of C-RAN can reduce cost and complexity. In another way the indoor Visible Light Communication (VLC) system can be combined with the conventional RF system via reconfigurable intelligent surface (RIS) in order to show the performance of outage probability and bit error rate (BER)
[13] Carrierless Amplitude-Phase (CAP)-VLC systems are implemented with algorithms such as Support Vector Machines [14], K-Nearest Neighbourhood (KNN) [15], Artificial Neural Networks (ANN) [16] [17] and even Machine Learning Algorithms [18] [19]
Summary
T He rapidly emerging, huge volumes of mobile and edge devices have lead to the shortage of bandwidth and we are faced with a bottleneck in our attempts to enhance network capacity. Implementation of C-RAN can reduce cost and complexity In another way the indoor VLC system can be combined with the conventional RF system via reconfigurable intelligent surface (RIS) in order to show the performance of outage probability and BER. [13] CAP-VLC systems are implemented with algorithms such as Support Vector Machines [14], K-Nearest Neighbourhood (KNN) [15], Artificial Neural Networks (ANN) [16] [17] and even Machine Learning Algorithms [18] [19]. This paper proposes the new data-driven CAP-VLC simulated environment to collect available data sets that are used to study the characteristics of the receiver end of the CAPVLC demodulation technique.
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