逆向调制无线光通信在无人机、小型化平台等领域具有广阔的应用前景, 但是易受大气湍流的影响, 接收信号会出现较大的低频随机性起伏,使用固定阈值的硬判决方法判决时误码率较高。为了提高信号判决的准确率, 提出一种基于K均值算法的信号判决方法, 通过学习接收到样值点的内在特征, 依据其相似性动态自适应地对该窗口内的样值点进行分类。研究结果表明: 湍流信道条件下, 与传统的硬判决方法相比, 基于K均值算法的信号判决方法误码性能更优, 误码率为10-6时, 信噪比需求降低约2 dB。

Reverse modulation wireless optical communication has broad application prospects in unmanned aerial vehicle, miniaturized platform and other fields, but it is easy to be affected by atmospheric turbulence, the received signal will have large low-frequency random fluctuation, and the bit error rate is high when using the hard decision method with fixed threshold. In order to improve the accuracy of signal decision, a signal decision method based on K-means algorithm is proposed. By learning the internal characteristics of the received sample points, the sample points in the window are dynamically and adaptively classified according to their similarity. The research results show that under the condition of turbulent channel, compared with the traditional hard decision method, the signal decision method based on K-means algorithm has better bit error performance. When the bit error rate is 10-6, the signal-to-noise ratio requirement is reduced by about 2 dB.