LED Nonlinearity Estimation and Compensation in VLC Systems Using Probabilistic Bayesian Learning

Abstract

In this paper, we propose and evaluate a novel light-emitting diode (LED) nonlinearity estimation and compensation scheme using probabilistic Bayesian learning (PBL) for spectral-efficient visible light communication (VLC) systems. The nonlinear power-current curve of the LED transmitter can be accurately estimated by exploiting PBL regression and hence the adverse effect of LED nonlinearity can be efficiently compensated. Simulation results show that, in a 80-Mbit/s orthogonal frequency division multiplexing (OFDM)-based nonlinear VLC system, comparable bit-error rate (BER) performance can be achieved by the conventional time domain averaging (TDA)-based LED nonlinearity mitigation scheme with totally 20 training symbols (TSs) and the proposed PBL-based scheme with only a single TS. Therefore, compared with the conventional TDA scheme, the proposed PBL-based scheme can substantially reduce the required training overhead and hence greatly improve the overall spectral efficiency of bandlimited VLC systems. It is also shown that the PBL-based LED nonlinearity estimation and compensation scheme is computational efficient for the implementation in practical VLC systems.

A survey of applied machine learning techniques for optical orthogonal frequency division multiplexing based networks

“…Various supervised regression algorithms are used in optical communication systems, such as SVR [55,85], IVSTF [32,55,96,98], probabilistic Bayesian learning (PBL) [89] and FS-DBP [55]. Table 3 exhibits supervised ML algorithms for regression in OFDM based optical communication systems in terms of complexity for prediction, advantages, disadvantages, applications, signal type and reachability.…”

“…PBL is employed in LED nonlinearity estimation and compensation configuration for OFDM-based nonlinear visible light communication (VLC) systems [89]. Also, PBL is characterized with a high complexity (i.e., O((N + 1) ) where N is the number of basis function) and can improve the spectral efficiency of the system.…”

Special Issue on “Optics for AI and AI for Optics”

“…Generating, transmitting, and recovering such high-volume data requires advanced signal processing and networking technologies with high performance and cost-and-power efficiency. AI is especially useful for optimization and performance prediction for systems that exhibit complex behaviors [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. In this aspect, traditional signal processing algorithms may not be as efficient as AI algorithms.…”

“…The Special Issue is launched to bring optics and AI together to address the challenges that each face, which are difficult to address alone. There are 12 selected contributions for the special session, representing the fascinating progress in the combined area of optics and AI, ranging from photonic neural network (NN) architecture [5] to AI-enabled advances in optical communications including both physical layer transceiver signal processing [10][11][12][13][14][15][16][17] and network layer performance monitoring [18,19], as well as the potential role of AI in quantum communications [20].…”

“…AI for short-reach optical communications: For short visible light communications, Chen Chen et al introduced a probabilistic Bayesian learning algorithm to compensate the light-emitting diode (LED) nonlinearity [10]. Maximilian Schaedler and his colleagues investigated a deep NN-based nonlinear equalizer in a single lambda 600Gbps coherent short-reach link and show its superior performance compared with the conventional Volterra nonlinear equalizer [11].…”