Novel nonlinear prediction algorithm for fast fading channel
-
摘要: 快速衰落信道预测是实现快速资源配置和快速自适应调制等容量提升技术的重要途径.为解决快速衰落信道参数预测问题,对系统输出的低维标量时间序列,利用坐标延迟理论,重建系统的高维相空间,从而获得比标量时间序列更多的系统信息,进而采用递归最小均方支持向量机在这一高维空间中进行回归预测.具有局部可预测性的高斯带限过程可对快速衰落信道特性进行准确的描述是该预测算法的前提,另外从非线性动力学的角度讨论了快速衰落信道的可预测性.仿真结果表明该算法适于进行较大时间范围的预测,是进行衰落信道非线性预测的有效途径.Abstract: Prediction of the rapidly fading mobile radio channel enables a number of capacity improving techniques such as the fast adaptive resource allocation or fast adaptive modulation. To predict the fast fading channel parameters, the embedding phase space was reconstructed by utilizing times delay techniques since the high dimension space possessed more information of system than the scalar time series, and a new nonlinear regression method: recurrent least squares support vector machines (RLS-SVM) was used to resolve the prediction problem in a high space. The fading envelope was well modeled as a Gauss bandlimited process which possessed special predictability properties and the predictability was also analyzed from point of view of nonlinear dynamics. Performance evaluation of the prediction algorithm was carried out with varied signal to noise rate on Rayleigh fading channels. The simulation result shows that the proposed algorithm is a good candidate for long range prediction of fading channel.
-
Key words:
- fading channel /
- prediction /
- support vector machines /
- embedding phase space
-
[1] Jakes W C. Microwave mobile communications[M]. Piscataway, NJ:IEEE Press, 1994 [2] Lyman R J, Edmonson W W, Mc-Cullough S, et al. The predictability ofcontinuous-time, bandlimited processes[J]. IEEE Trans on Signal Processing, 2000, 48(2):311~316 [3] Eyceoz T, Duel-Hallen A, Hallen H. Prediction of fast fading parameters by resolving the interference pattern . Proceedings of the 31st ASILOMAR Conference on Signals, Systems and Computers . California :IEEE Press, 1997. 167~171 [4] Sternad M, Ekman T, Ahlen A. Power prediction on broadband channels . Vehicular Technology Conference . Greece:IEEE Press, 2001. 2328~2332 [5] Takens F. Detecting strange attractors in fluid turbulence . In:Rand D, Young L S. Dynamical Systems and Turbulence . Berlin:Springer, 1981.366~381 [6] Kaplan D, Glass L. Understanding nonlinear dynamics[M]. New York:Springer, 1995 [7] Vapnik V. The nature of statistical learning theory[M]. Translated by Zhang Xuegong. Beijing:Tsinghua University Press, 2000. 91~108 [8] Suykens J A K, Vandewalle J. Recurrent least squares support vector machines[J]. IEEE Trans on Circuits and System-I:Fundamental Theory and Applications, 2000, 47(7):1109~1114 [9] Kennel M B, Brown R, Abarbanel H D I. Determining embedding dimension for phase-space reconstruction using a geometrical construction[J]. Phys Rev A, 1992,45(6):3403~3411 [10] Fraser A M, Swinney H L. Independent coordinates for strange attractors from mutual information[J]. Phys Rev A, 1986, 33(2):1134~1140 -
点击查看大图
计量
- 文章访问数: 2469
- HTML全文浏览量: 147
- PDF下载量: 1159
- 被引次数: 0
下载:
