Research on Computational Intelligence Algorithm in LTE Channel Estimation
Because data traffic is growing at a rapid pace thanks to advancements in the Internet of Things, precise modelling and precisely anticipating Long-Term Evolution (LTE) Channel is critical for a variety of applications like as video streaming, effective bandwidth consumption, and power management. I...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Universidad Autónoma de Bucaramanga
2022-12-01
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| Series: | Revista Colombiana de Computación |
| Subjects: | |
| Online Access: | http://revistas.unab.edu.co/index.php/rcc/article/view/4308 |
| _version_ | 1797938600884568064 |
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| author | Siraj Pathan Sanjay Singh Mujib Tamboli Sunil Pathak |
| author_facet | Siraj Pathan Sanjay Singh Mujib Tamboli Sunil Pathak |
| author_sort | Siraj Pathan |
| collection | DOAJ |
| description | Because data traffic is growing at a rapid pace thanks to advancements in the Internet of Things, precise modelling and precisely anticipating Long-Term Evolution (LTE) Channel is critical for a variety of applications like as video streaming, effective bandwidth consumption, and power management. In this research, we propose a model based on a Computational Intelligence (CI) Algorithm that may enhance Channel Estimation based on received signal. Two Algorithms are considered. In contrast to previous work that focused solely on designing models to estimate channel using traditional Minimum Mean Square Error (MMSE) and Least Square (LS) algorithms, we used 1) GA (Genetic Algorithm) and 2) PSO (Particle Swarm Optimization Algorithm) to work on Discrete and Continuous Long-Term Evolution (LTE) drive test data. We're looking at LTE in the 5.8 GHz band in particular. By lowering the mean square error of LS and the complexity of MMSE, the design model attempts to improve channel estimation. Pilots are put at random and sent with data to gather channel information, which aids the receiver in decoding and estimating the channel using LS, MMSE, Taguchi GA, and PSO. The Bit Error Rate (BER), Signal to Noise Ratio, and Mean Square Error of a CI-based model have all been estimated. In comparison to the MMSE and LS algorithms, the proposed model BER achieves the target gain of 2.4 dB and 5.4 dB.
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| first_indexed | 2024-04-10T19:02:35Z |
| format | Article |
| id | doaj.art-a7b7dd23d5e447999d6097df8d72ddc8 |
| institution | Directory Open Access Journal |
| issn | 1657-2831 2539-2115 |
| language | English |
| last_indexed | 2024-04-10T19:02:35Z |
| publishDate | 2022-12-01 |
| publisher | Universidad Autónoma de Bucaramanga |
| record_format | Article |
| series | Revista Colombiana de Computación |
| spelling | doaj.art-a7b7dd23d5e447999d6097df8d72ddc82023-01-31T08:15:57ZengUniversidad Autónoma de BucaramangaRevista Colombiana de Computación1657-28312539-21152022-12-0123210.29375/25392115.4308Research on Computational Intelligence Algorithm in LTE Channel EstimationSiraj Pathan0Sanjay Singh1Mujib Tamboli2Sunil Pathak3Amity University, Rajasthan, Jaipur, IndiaAmity University, Rajasthan, Jaipur, IndiaKalsekar College of engineering, Mumbai, IndiaAmity University, Rajasthan, Jaipur, IndiaBecause data traffic is growing at a rapid pace thanks to advancements in the Internet of Things, precise modelling and precisely anticipating Long-Term Evolution (LTE) Channel is critical for a variety of applications like as video streaming, effective bandwidth consumption, and power management. In this research, we propose a model based on a Computational Intelligence (CI) Algorithm that may enhance Channel Estimation based on received signal. Two Algorithms are considered. In contrast to previous work that focused solely on designing models to estimate channel using traditional Minimum Mean Square Error (MMSE) and Least Square (LS) algorithms, we used 1) GA (Genetic Algorithm) and 2) PSO (Particle Swarm Optimization Algorithm) to work on Discrete and Continuous Long-Term Evolution (LTE) drive test data. We're looking at LTE in the 5.8 GHz band in particular. By lowering the mean square error of LS and the complexity of MMSE, the design model attempts to improve channel estimation. Pilots are put at random and sent with data to gather channel information, which aids the receiver in decoding and estimating the channel using LS, MMSE, Taguchi GA, and PSO. The Bit Error Rate (BER), Signal to Noise Ratio, and Mean Square Error of a CI-based model have all been estimated. In comparison to the MMSE and LS algorithms, the proposed model BER achieves the target gain of 2.4 dB and 5.4 dB. http://revistas.unab.edu.co/index.php/rcc/article/view/4308Genetic Algorithm (GA) ,Particle Swarm Intelligence(PSO) , Long Term Evolution (LTE) , Minimum Mean Square Error (MMSE) , Least Square (LS) |
| spellingShingle | Siraj Pathan Sanjay Singh Mujib Tamboli Sunil Pathak Research on Computational Intelligence Algorithm in LTE Channel Estimation Revista Colombiana de Computación Genetic Algorithm (GA) ,Particle Swarm Intelligence(PSO) , Long Term Evolution (LTE) , Minimum Mean Square Error (MMSE) , Least Square (LS) |
| title | Research on Computational Intelligence Algorithm in LTE Channel Estimation |
| title_full | Research on Computational Intelligence Algorithm in LTE Channel Estimation |
| title_fullStr | Research on Computational Intelligence Algorithm in LTE Channel Estimation |
| title_full_unstemmed | Research on Computational Intelligence Algorithm in LTE Channel Estimation |
| title_short | Research on Computational Intelligence Algorithm in LTE Channel Estimation |
| title_sort | research on computational intelligence algorithm in lte channel estimation |
| topic | Genetic Algorithm (GA) ,Particle Swarm Intelligence(PSO) , Long Term Evolution (LTE) , Minimum Mean Square Error (MMSE) , Least Square (LS) |
| url | http://revistas.unab.edu.co/index.php/rcc/article/view/4308 |
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