An Alternating Variable Step-Size Adaptive Long-Range Prediction of LMS Fading Signals

• Published in: International journal of distributed sensor networks Vol. 2015; no. 2; p. 483937
• Main Authors: Liao, Xi, Xue, Rui, Zhao, Dan-feng, Wang, Yang
• Format: Journal Article
• Language: English
• Published: London, England Hindawi Publishing Corporation 01.01.2015; SAGE Publications; Sage Publications Ltd. (UK); John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Channels; Computer simulation; Eigenvalues; Engineering research; Errors; Fading; Fading channels; Least mean squares algorithm; Mathematical models; Networks; Prediction theory; Satellite communications
• ISSN: 1550-1329; 1550-1477; 1550-1477
• DOI: 10.1155/2015/483937

We propose a linear alternating variable step-size adaptive long-range prediction (AVSS-ALRP) scheme to predict fading signals which is especially suitable for a versatile two-state land mobile satellite (LMS) channel model at S-band. A three-step design procedure is presented to optimize the prediction performance. Firstly, we establish the Gilbert-Elliot channel model based on first-order Markov chain for satellite communication downlink and take advantage of smoothing average to obtain channel observed values. At a second stage, eigenvalue decomposition method is applied to predict future long-range channel state instead of weighted prediction. Finally, combining variable step-size least mean squares and adaptive long-range prediction, we introduce the VSS-ALRP algorithm to predict LMS channel fading signals in the case of “good” state, and the obtained prediction results would be revised based on the linear prediction of error when shadowing condition is in the “bad” state. Simulation results show that the proposed scheme can not only offer an accurate prediction for long-range channel state and fading signals over the two-state Gilbert-Elliot channel model and greatly enhance the fading signals’ autocorrelation, but also have considerably better performance than long-range prediction (LRP) algorithm from the results of mean square error (MSE) and correlation coefficient.