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

Xi Liao, Rui Xue, Dan-feng Zhao and Yang Wang

International Journal of Distributed Sensor Networks, 2015, vol. 11, issue 2, 483937

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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:11:y:2015:i:2:p:483937

DOI: 10.1155/2015/483937

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