Efficient Recovery of Linear Predicted Coefficients Based on Adaptive Steepest Descent Algorithm in Signal Compression for End-to-End Communications
The efficiency of recovery and signal decoding efficacy at the receiver in end-to-end communications using linearly predicted coefficients are susceptible to errors, especially for highly compressed signals. In this paper, we propose a method to efficiently recover linearly predicted coefficients fo...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/jece/6570183 |
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| author | Abel Kamagara Abbas Kagudde Baris Atakan |
| author_facet | Abel Kamagara Abbas Kagudde Baris Atakan |
| author_sort | Abel Kamagara |
| collection | DOAJ |
| description | The efficiency of recovery and signal decoding efficacy at the receiver in end-to-end communications using linearly predicted coefficients are susceptible to errors, especially for highly compressed signals. In this paper, we propose a method to efficiently recover linearly predicted coefficients for high signal compression for end-to-end communications. Herein, the steepest descent algorithm is applied at the receiver to decode the affected linear predicted coefficients. This algorithm is used to estimate the unknown frequency, time, and phase. Subsequently, the algorithm facilitates down-conversion, time and carrier recovery, equalization, and correlation processes. To evaluate the feasibility of the proposed method, parameters such as multipath interference, additive white Gaussian noise, timing, and phase noise are modeled as channel errors in signal compression using the software-defined receiver. Our results show substantial recovery efficiency with noise variance between 0 and y × 10E − 3, where y lies between 0 and 10 using the modeled performance metrics of bit error rate, symbol error rate, and mean square error. This is promising for modeling software-defined networks using highly compressed signals in end-to-end communications. |
| format | Article |
| id | doaj-art-d2edda69762b48179329bd55cb37eb21 |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-d2edda69762b48179329bd55cb37eb212025-01-25T05:00:01ZengWileyJournal of Electrical and Computer Engineering2090-01552025-01-01202510.1155/jece/6570183Efficient Recovery of Linear Predicted Coefficients Based on Adaptive Steepest Descent Algorithm in Signal Compression for End-to-End CommunicationsAbel Kamagara0Abbas Kagudde1Baris Atakan2Department of Electrical and Electronics EngineeringDepartment of Electrical and Energy EngineeringDepartment of Electrical and Electronics EngineeringThe efficiency of recovery and signal decoding efficacy at the receiver in end-to-end communications using linearly predicted coefficients are susceptible to errors, especially for highly compressed signals. In this paper, we propose a method to efficiently recover linearly predicted coefficients for high signal compression for end-to-end communications. Herein, the steepest descent algorithm is applied at the receiver to decode the affected linear predicted coefficients. This algorithm is used to estimate the unknown frequency, time, and phase. Subsequently, the algorithm facilitates down-conversion, time and carrier recovery, equalization, and correlation processes. To evaluate the feasibility of the proposed method, parameters such as multipath interference, additive white Gaussian noise, timing, and phase noise are modeled as channel errors in signal compression using the software-defined receiver. Our results show substantial recovery efficiency with noise variance between 0 and y × 10E − 3, where y lies between 0 and 10 using the modeled performance metrics of bit error rate, symbol error rate, and mean square error. This is promising for modeling software-defined networks using highly compressed signals in end-to-end communications.http://dx.doi.org/10.1155/jece/6570183 |
| spellingShingle | Abel Kamagara Abbas Kagudde Baris Atakan Efficient Recovery of Linear Predicted Coefficients Based on Adaptive Steepest Descent Algorithm in Signal Compression for End-to-End Communications Journal of Electrical and Computer Engineering |
| title | Efficient Recovery of Linear Predicted Coefficients Based on Adaptive Steepest Descent Algorithm in Signal Compression for End-to-End Communications |
| title_full | Efficient Recovery of Linear Predicted Coefficients Based on Adaptive Steepest Descent Algorithm in Signal Compression for End-to-End Communications |
| title_fullStr | Efficient Recovery of Linear Predicted Coefficients Based on Adaptive Steepest Descent Algorithm in Signal Compression for End-to-End Communications |
| title_full_unstemmed | Efficient Recovery of Linear Predicted Coefficients Based on Adaptive Steepest Descent Algorithm in Signal Compression for End-to-End Communications |
| title_short | Efficient Recovery of Linear Predicted Coefficients Based on Adaptive Steepest Descent Algorithm in Signal Compression for End-to-End Communications |
| title_sort | efficient recovery of linear predicted coefficients based on adaptive steepest descent algorithm in signal compression for end to end communications |
| url | http://dx.doi.org/10.1155/jece/6570183 |
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