基于最小二乘支持向量机的衰落信道预测算法

该文探讨了利用相空间重构和支持向量机进行衰落信道非线性预测算法。该算法基于多径衰落信道具有混沌行为，利用坐标延迟理论，重建衰落信道系数的相空间，再根据混沌吸引子的稳定性和分形性，在相空间中通过递归最小二乘支持向量机（RLS-SVMM）进行预测。该算法对原始数据可以进行更平滑的处理，在噪声环境下预测的时间范围更长。对时间跨度为63．829ms的衰落系数进行了预测，仿真结果表明，在信噪比为15dB时，预测结果优于AR算法。

Prediction Algorithm for Fast Fading Channels Based on Recurrent Least Squares Support Vector Machines

An new method for fast fading channel prediction using Recurrent Least Squares Support Vector Machines (RLS-SVM) combined with reconstructed embedding phase space is investigated. This algorithm is based on the chaotic behavior of the mobile multipath fading channel.The phase space of these mobile multipath fading channel coefficients are reconstructed by the theory of time delays. Based on the stability and the fractal of the chaotic attractor, the fast fading channel coefficients are predicted in their phase space based on the RLS-SVM.The proposed algorithm is a better candidate for long range prediction of the fading channel in the noise context. The experiment is carried out by utilizing fading channel data which spanes 63.829 ms. The simulation results show that the better prediction performance is acquired than the AR method when the signal to noise ratio is 15dB.