正交频分复用
可见光通信
计算机科学
自编码
剪裁(形态学)
误码率
副载波
还原(数学)
稳健性(进化)
电子工程
算法
电信
人工神经网络
频道(广播)
人工智能
光学
数学
物理
工程类
生物化学
基因
哲学
语言学
发光二极管
化学
几何学
作者
Lili Hao,Dongyi Wang,Wenyong Cheng,Jing Li,Anfan Ma
标识
DOI:10.1016/j.optcom.2019.03.013
摘要
High peak-to-average-power ratio (PAPR) and the LED nonlinearity have significant impacts on the performance of indoor Visible Light Communication (VLC) orthogonal frequency division multiplexing (OFDM) systems. In this paper, we aim to improve the system performance by utilizing an end-to-end learning network. A novel PAPR reduction scheme is applied based on weighted autoencoder and amplitude clipping methods to address the high PAPR and LED nonlinearity problems. The constellation mapping and de-mapping of the transmitted symbols and phase factor of each subcarrier are adaptively acquired and optimized through the deep learning technique. How hyperparameters of network, network architecture and channel types affect the performance of bit error ratio (BER) and PAPR are firstly quantified in the asymmetrically clipped optical OFDM based VLC systems. Simulation results show that the hybrid autoencoder method achieves a distinct PAPR reduction of about 12 dB and is more robustness to LED nonlinearities leading to better BER performance compared to the standard methods.
科研通智能强力驱动
Strongly Powered by AbleSci AI