基于平稳小波和重构相空间的快衰落信道预测算法

为了解决快速衰落信道的预测问题,提出了一种非线性预测算法.该算法基于多径快速衰落信道所具有的混沌行为,将快速衰落信道系数分解为与原序列等长的尺度系数和小波系数,并利用坐标延迟理论,重建尺度系数和各级小波系数的相空间,再根据混沌吸引子的稳定性和分形性,在相空间中对尺度系数和小波系数进行预测,进而通过平稳小波重构算法,实现了快速衰落信道的非线性预测.与无小波算法相比,该算法更适于对噪声环境下的较大时间范围进行预测.对时间跨度为65 9ms的衰落系数进行了预测,仿真结果表明,在信噪比为10dB时,预测结果明显优于无小波变换算法.

Prediction Algorithm for Fast Fading Channels Based on Stationary Wavelet Transform and Reconstructed Phase Space

To solve the problem of fast fading channel prediction, the mobile multipath fading channel coefficient was decomposed as the measurement coefficients and wavelet coefficients based on the discrete stationary wavelet transform algorithms. For the chaotic behavior of the mobile multipath fading channel, the phase space of these coefficients was reconstructed by the theory of time delays. Based on the stability and the fractal of the chaotic attractor, the measurement coefficients and wavelet coefficients were predicted in their phase space. Finally the prediction of the fading channel coefficients were acquired by the inverse discrete stationary wavelet transform.Compared with the algorithm without wavelet,the proposed algorithm is a better candidate for long range prediction of the fading channel in the noise context. The experiment was carried out by utilizing fading channel data which spaned 65.9 milliseconds. The simulation results shows that the better prediction performance is acquired than the method without wavelet transform when the signal to noise ratio is 10 dB.