Asymmetric CNN-based equalization for clipping distortion compensation in LED-based VLC systems
Tipos de publicação: Artigo
Visible Light Communication (VLC) systems utilizing Direct Current-biased Optical Orthogonal Frequency Division Multiplexing (DCO-OFDM) are inherently limited by a high Peak-to-Average Power Ratio (PAPR), which leads to non-linear distortions due to the restricted dynamic range of LEDs. Conventional PAPR reduction techniques often impose significant computational overhead on the transmitter or severely degrade the Error Vector Magnitude (EVM). This paper proposes an asymmetric deep learning-based framework to mitigate clipping noise. We introduce a low-complexity clipping approach at the transmitter ((𝑁)) and shift the compensation burden to a Convolutional Neural Network (CNN) at the receiver. Two clipping ratios were evaluated: CR = 0.9 (moderate, 2.9 dB PAPR reduction) and CR = 0.7 (aggressive, 5.8 dB PAPR reduction). Experimental results from 20 spatial positions within a 2.0 × 1.0 m2 test area demonstrate that the proposed CNN effectively recovers the signal under both clipping conditions. For 64-QAM modulation with CR = 0.7, the CNN achieves a mean EVM of 7.07%, below the 8% reference threshold defined by 3GPP standards, and a mean BER of 8.98 × 10−4, while the conventional MMSE equalizer reaches 15.05% EVM and 5.37 × 10−2 BER. The architecture shows consistent generalization and spatial robustness compared to standard Deep Neural Networks (DNNs), suggesting its potential for LED-based lighting applications.