为了提升无线光通信系统接收灵敏度, 采用一种基于改进基分类器系数的AdaBoost弱光信号检测算法, 解决多像素光子计数器(MPPC)在弱光条件下的信号检测问题。该算法采用k最近邻(KNN)为基分类器组建强分类器, 针对传统AdaBoost算法基分类器系数仅与错误率有关而产生冗余的基分类器消耗系统资源的问题, 提出一种基于错误和正确分类样本权重的基分类器系数优化AdaBoost算法(W-AdaBoost), 将信号解调问题转换为分类问题; 并采用波长450 nm半导体激光器、MPPC光电转换器件搭建了无线光通信系统。结果表明, 系统在通信速率为2 Mbit/s、误比特率为3.8×10-3时, 改进的W-AdaBoost-KNN算法较传统AdaBoost-KNN和单一KNN算法,灵敏度分别提升了1.6 dB和4.8 dB左右。此研究结果说明W-AdaBoost-KNN算法可提高弱光条件下的信号检测效率, 提升无线光通信系统接收灵敏度。

In order to improve the receiving sensitivity of the wireless optical communication system, an AdaBoost weak-light signal detection algorithm based on the improved base classifier coefficient was adopted to solve the signal detection problem of multi-pixel photon counter (MPPC) under weak-light conditions. In this algorithm, k-nearest neighbor (KNN) was used as the base classifier to build a strong classifier. A W-AdaBoost algorithm based on the weights of incorrect and correct classification samples was proposed to solve the problem of that the traditional AdaBoost algorithm’s base classifier coefficients are only related to the error rate, which causes redundant base classifiers to consume system resources. The W-AdaBoost algorithm transforms the issue of signal demodulation into classification, a 450 nm semiconductor laser and MPPC photoelectric conversion device are used to build a wireless optical communication system. The experimental results show that the sensitivity of the improved W-AdaBoost-KNN algorithm is about 1.6 dB and 4.8 dB higher than that of the traditional AdaBoost-KNN algorithm and the single KNN algorithm respectively, when the communication rate of the system is 2 Mbit/s and the bit error rate is 3.8×10-3. The research results show that W-AdaBoost-KNN algorithm can improve the signal detection efficiency under weak-light conditions and improve the receiving sensitivity of the wireless optical communication systems.