Equalizer Design: HBOA-DE-trained radial basis function neural networks
Communication systems that rely on wireless technology require signal processing techniques to improve their channel performance. Wireless communications are susceptible to various signal distortions during transmission, including inter-symbol interference, adjacent channel interference, and co-chan...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866525000106 |
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| author | Santosh Kumar Das Satya Ranjan Pattanaik Pradyumna Kumar Mohapatra Saroja Kumar Rout Abdulaziz S. Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed |
| author_facet | Santosh Kumar Das Satya Ranjan Pattanaik Pradyumna Kumar Mohapatra Saroja Kumar Rout Abdulaziz S. Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed |
| author_sort | Santosh Kumar Das |
| collection | DOAJ |
| description | Communication systems that rely on wireless technology require signal processing techniques to improve their channel performance. Wireless communications are susceptible to various signal distortions during transmission, including inter-symbol interference, adjacent channel interference, and co-channel interference. As a result, achieving error-free signal transmission in wireless communication is often challenging. To make sure the signal is recovered with a minimum bit error rate, equalizers are needed at the front end of the receiver. As an optimization algorithm, a nature-inspired hybrid algorithm is applied, namely BOA/DE, which is a combination of the Butterfly optimization algorithm (BOA) and differential evolution (DE). Based on a suitable network topology and transfer function, the presented work proposes an algorithm for training radial basis function neural networks (RBFNNs) that is applied to the problem of channel equalization. Both BOA and DE are advantageous in the proposed algorithm, which permits it to produce efficient results by balancing exploration and exploitation. Several methods have also been discussed in the literature that use optimization techniques to deal with the problem of equalization. The same problem is treated in this article as a classification issue. As a further step in the evaluation of the HBOA-DE-based RBFNN equalizer, three non-linear channels and adding different nonlinearities have been simulated. The proposed algorithm is compared with well-known algorithms in terms of Mean Square Error (MSE) and Bit Error Rate (BER). Additionally, the algorithm has been tested against a situation in burst error and evaluated via bit error probability (BEP) to establish its robustness and performance. Results showed that the method performed better in handling burst errors compared to others. It has been shown that the projected method outclasses other methods even in poor signal-to-noise ratio conditions, which is borne out by extensive simulation studies. |
| format | Article |
| id | doaj-art-ebd9a0e87a82431d94bd67b087834fee |
| institution | Kabale University |
| issn | 1110-8665 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-ebd9a0e87a82431d94bd67b087834fee2025-01-28T04:14:27ZengElsevierEgyptian Informatics Journal1110-86652025-03-0129100617Equalizer Design: HBOA-DE-trained radial basis function neural networksSantosh Kumar Das0Satya Ranjan Pattanaik1Pradyumna Kumar Mohapatra2Saroja Kumar Rout3Abdulaziz S. Almazyad4Muhammed Basheer Jasser5Guojiang Xiong6Ali Wagdy Mohamed7Department of Computer Science & Engineering, Gandhi Institute For Technology (Autonomous), BPUT, Bhubaneswar, Odisha, IndiaDepartment of Computer Science & Engineering, Gandhi Institute For Technology (Autonomous), BPUT, Bhubaneswar, Odisha, IndiaDepartment of Electronics & Communication Engineering, Vedang Institute of Technology, BPUT, Bhubaneswar, Odisha, IndiaDepartment of Information Technology, Vardhaman College of Engineering (Autonomous), Hyderabad, Telangana, IndiaDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi ArabiaDepartment of Data Science and Artificial Intelligence, School of Engineering and Technology, Sunway University, No. 5 Jalan Universiti, Bandar Sunway, 47500, Petaling Jaya, Selangor, Malaysia; Research Centre for Human-Machine Collaboration (HUMAC), School of Engineering and Technology, Sunway University, No. 5 Jalan Universiti, Bandar Sunway, 47500, Petaling Jaya, Selangor, Malaysia; Corresponding author.Guizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaOperations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt; Applied Science Research Center, Applied Science Private University, Amman 11931, JordanCommunication systems that rely on wireless technology require signal processing techniques to improve their channel performance. Wireless communications are susceptible to various signal distortions during transmission, including inter-symbol interference, adjacent channel interference, and co-channel interference. As a result, achieving error-free signal transmission in wireless communication is often challenging. To make sure the signal is recovered with a minimum bit error rate, equalizers are needed at the front end of the receiver. As an optimization algorithm, a nature-inspired hybrid algorithm is applied, namely BOA/DE, which is a combination of the Butterfly optimization algorithm (BOA) and differential evolution (DE). Based on a suitable network topology and transfer function, the presented work proposes an algorithm for training radial basis function neural networks (RBFNNs) that is applied to the problem of channel equalization. Both BOA and DE are advantageous in the proposed algorithm, which permits it to produce efficient results by balancing exploration and exploitation. Several methods have also been discussed in the literature that use optimization techniques to deal with the problem of equalization. The same problem is treated in this article as a classification issue. As a further step in the evaluation of the HBOA-DE-based RBFNN equalizer, three non-linear channels and adding different nonlinearities have been simulated. The proposed algorithm is compared with well-known algorithms in terms of Mean Square Error (MSE) and Bit Error Rate (BER). Additionally, the algorithm has been tested against a situation in burst error and evaluated via bit error probability (BEP) to establish its robustness and performance. Results showed that the method performed better in handling burst errors compared to others. It has been shown that the projected method outclasses other methods even in poor signal-to-noise ratio conditions, which is borne out by extensive simulation studies.http://www.sciencedirect.com/science/article/pii/S1110866525000106Channel EqualizationRBFNNBOADifferential algorithm |
| spellingShingle | Santosh Kumar Das Satya Ranjan Pattanaik Pradyumna Kumar Mohapatra Saroja Kumar Rout Abdulaziz S. Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Equalizer Design: HBOA-DE-trained radial basis function neural networks Egyptian Informatics Journal Channel Equalization RBFNN BOA Differential algorithm |
| title | Equalizer Design: HBOA-DE-trained radial basis function neural networks |
| title_full | Equalizer Design: HBOA-DE-trained radial basis function neural networks |
| title_fullStr | Equalizer Design: HBOA-DE-trained radial basis function neural networks |
| title_full_unstemmed | Equalizer Design: HBOA-DE-trained radial basis function neural networks |
| title_short | Equalizer Design: HBOA-DE-trained radial basis function neural networks |
| title_sort | equalizer design hboa de trained radial basis function neural networks |
| topic | Channel Equalization RBFNN BOA Differential algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S1110866525000106 |
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