An Alternating Variable Step-Size Adaptive Long-Range Prediction of LMS Fading Signals. (23rd February 2015)

• Record Type: Journal Article
• Authors: Liao, Xi; Xue, Rui; Zhao, Dan-feng; Wang, Yang
• Is Part Of: International journal of distributed sensor networks. Volume 11:Number 2(2015)
• Publication Date: 2015-02-23
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2015/483937
• Languages: English
• ISSNs: 1550-1329

Abstract

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.