An Alternating Variable Step-Size Adaptive Long-Range Prediction of LMS Fading Signals
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 predic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2015-02-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/483937 |
| _version_ | 1797722408327577600 |
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| author | Xi Liao Rui Xue Dan-feng Zhao Yang Wang |
| author_facet | Xi Liao Rui Xue Dan-feng Zhao Yang Wang |
| author_sort | Xi Liao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T09:46:56Z |
| format | Article |
| id | doaj.art-3a9c5850f48e451f812e286ec4e1823d |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T09:46:56Z |
| publishDate | 2015-02-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-3a9c5850f48e451f812e286ec4e1823d2023-09-02T12:46:56ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772015-02-011110.1155/2015/483937483937An Alternating Variable Step-Size Adaptive Long-Range Prediction of LMS Fading SignalsXi LiaoRui XueDan-feng ZhaoYang WangWe 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.https://doi.org/10.1155/2015/483937 |
| spellingShingle | Xi Liao Rui Xue Dan-feng Zhao Yang Wang An Alternating Variable Step-Size Adaptive Long-Range Prediction of LMS Fading Signals International Journal of Distributed Sensor Networks |
| title | An Alternating Variable Step-Size Adaptive Long-Range Prediction of LMS Fading Signals |
| title_full | An Alternating Variable Step-Size Adaptive Long-Range Prediction of LMS Fading Signals |
| title_fullStr | An Alternating Variable Step-Size Adaptive Long-Range Prediction of LMS Fading Signals |
| title_full_unstemmed | An Alternating Variable Step-Size Adaptive Long-Range Prediction of LMS Fading Signals |
| title_short | An Alternating Variable Step-Size Adaptive Long-Range Prediction of LMS Fading Signals |
| title_sort | alternating variable step size adaptive long range prediction of lms fading signals |
| url | https://doi.org/10.1155/2015/483937 |
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