To retrieve the information from the serious distorted received signal is the key challenge of communication signal processing. The chaotic baseband communication promises theoretically to eliminate the inter-symbol interference (ISI), however, it needs complicated calculation, if it is not impossible. In this paper, a genetic algorithm support vector machine (GA-SVM) based symbol detection method is proposed for chaotic baseband wireless communication system (CBWCS), by this way, treating the problem from a different viewpoint, the symbol decoding process is converted to be a binary classification through GA-SVM model. A trained GA-SVM model is used to decode the symbols directly at the receiver, so as to improve the bit error rate (BER) performance of the CBWCS and simplify the symbol detection process by removing the channel identification and the threshold calculation process as compared to that using the calculated threshold to decode symbol in the traditional methods. The simulation results show that the proposed method has better BER performance in both the static and time-varying wireless channels. The experimental results, based on the wireless open-access research platform, indicate that the BER of the proposed GA-SVM based symbol detection approach is superior to the other counterparts under a practical wireless multipath channel.

从严重失真的接收信号中提取信息是通信信号处理的关键挑战。混沌基带通信理论上有望消除符号间干扰（ISI），但如果不是不可能的话，它需要复杂的计算。本文针对混沌基带无线通信系统（CBWCS）提出了一种基于遗传算法支持向量机（GA-SVM）的符号检测方法，通过这种方式，从不同的角度看待问题，符号解码过程转化为通过 GA-SVM 模型进行二元分类。经过训练的 GA-SVM 模型用于直接在接收器处解码符号，与传统方法中使用计算的阈值解码符号相比，通过去除信道标识和阈值计算过程来提高CBWCS的误码率(BER)性能并简化符号检测过程。仿真结果表明，所提出的方法在静态和时变无线信道中均具有更好的误码率性能。基于无线开放接入研究平台的实验结果表明，在实际无线多径信道下，所提出的基于 GA-SVM 的符号检测方法的 BER 优于其他同行。仿真结果表明，所提出的方法在静态和时变无线信道中均具有更好的误码率性能。基于无线开放接入研究平台的实验结果表明，在实际无线多径信道下，所提出的基于 GA-SVM 的符号检测方法的 BER 优于其他同行。仿真结果表明，所提出的方法在静态和时变无线信道中均具有更好的误码率性能。基于无线开放接入研究平台的实验结果表明，在实际无线多径信道下，所提出的基于 GA-SVM 的符号检测方法的 BER 优于其他同行。