Innovative approaches to beam forming antenna array systems with adaptive Partial Update NLMS algorithms
Improvements in interference cancellation, energy economy, spectrum efficiency, and system security are among the most pressing needs for today's wireless networks. One effective technique for this is the use of a Beamforming Array Antennas (BAA). However, the complexity of the Beamforming (BF)...
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Elsevier
2024-12-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671124004340 |
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| author | Zahraa A. Shubber Thamer M. Jamel Ali.K. Nahar |
| author_facet | Zahraa A. Shubber Thamer M. Jamel Ali.K. Nahar |
| author_sort | Zahraa A. Shubber |
| collection | DOAJ |
| description | Improvements in interference cancellation, energy economy, spectrum efficiency, and system security are among the most pressing needs for today's wireless networks. One effective technique for this is the use of a Beamforming Array Antennas (BAA). However, the complexity of the Beamforming (BF) network, the long convergence time, and the large number of adjustable weight coefficients, all work against full band BAA. The key innovation and contribution of this research was to use Partial Update (PU) instead of full band adaptive algorithms, as no previous attempt had been made to do so. A subset of the array's elements, rather than all of them, will be connected to by PU methods. This allows the system to reduce the number of active antennas across all cells while maintaining high efficiency, and low cost. In this research, a new architectural model was proposed that makes use of PU adaptive algorithms, to reduce the required number of phase shifters (PSs), and hence base station antennas. For the most part, we will be discussing PU Normalized Least Mean Square (PU NLMS) algorithms like M-max NLMS, Periodic-NLMS, and Stochastic-NLMS. Using a Uniform Linear Array (ULA) Antennas in a simulation environment, we find that the, in terms of Mean Square Error (MSE), convergence rate, and steady-state error, it is evident that all PU NLMS algorithms (with the exception of Periodic-NLMS) had performed close, and approximately equivalent performance to the full band NLMS algorithms. In other hands, these PU algorithms maintain the radiation pattern with as little change from the original (distortion-free) as possible and symmetrical of the array, while. fewer elements are needed to produce the same amount of radiation. Reducing the number of required coefficients N to M (M = 5; M: Number of coefficients to be update per iteration) compared to the full update method (N = 8), to obtain the Reduction ratio 38 %. |
| format | Article |
| id | doaj-art-69ca4e806c8a4ca6ad9a172f598ec7bb |
| institution | OA Journals |
| issn | 2772-6711 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj-art-69ca4e806c8a4ca6ad9a172f598ec7bb2025-08-20T01:56:41ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112024-12-011010085510.1016/j.prime.2024.100855Innovative approaches to beam forming antenna array systems with adaptive Partial Update NLMS algorithmsZahraa A. Shubber0Thamer M. Jamel1Ali.K. Nahar2University of Technology, Department of Electrical and Electronics Engineering, Baghdad, IraqUniversity of Technology, Department of Communications Engineering, Baghdad, Iraq; Corresponding author.University of Technology, Department of Electrical and Electronics Engineering, Baghdad, IraqImprovements in interference cancellation, energy economy, spectrum efficiency, and system security are among the most pressing needs for today's wireless networks. One effective technique for this is the use of a Beamforming Array Antennas (BAA). However, the complexity of the Beamforming (BF) network, the long convergence time, and the large number of adjustable weight coefficients, all work against full band BAA. The key innovation and contribution of this research was to use Partial Update (PU) instead of full band adaptive algorithms, as no previous attempt had been made to do so. A subset of the array's elements, rather than all of them, will be connected to by PU methods. This allows the system to reduce the number of active antennas across all cells while maintaining high efficiency, and low cost. In this research, a new architectural model was proposed that makes use of PU adaptive algorithms, to reduce the required number of phase shifters (PSs), and hence base station antennas. For the most part, we will be discussing PU Normalized Least Mean Square (PU NLMS) algorithms like M-max NLMS, Periodic-NLMS, and Stochastic-NLMS. Using a Uniform Linear Array (ULA) Antennas in a simulation environment, we find that the, in terms of Mean Square Error (MSE), convergence rate, and steady-state error, it is evident that all PU NLMS algorithms (with the exception of Periodic-NLMS) had performed close, and approximately equivalent performance to the full band NLMS algorithms. In other hands, these PU algorithms maintain the radiation pattern with as little change from the original (distortion-free) as possible and symmetrical of the array, while. fewer elements are needed to produce the same amount of radiation. Reducing the number of required coefficients N to M (M = 5; M: Number of coefficients to be update per iteration) compared to the full update method (N = 8), to obtain the Reduction ratio 38 %.http://www.sciencedirect.com/science/article/pii/S2772671124004340Beamforming array antennasPartial update adaptive algorithmFull band NLMSM-max PU NLMSPeriodic PU NLMS, and Stochastic PU NLMS algorithms |
| spellingShingle | Zahraa A. Shubber Thamer M. Jamel Ali.K. Nahar Innovative approaches to beam forming antenna array systems with adaptive Partial Update NLMS algorithms e-Prime: Advances in Electrical Engineering, Electronics and Energy Beamforming array antennas Partial update adaptive algorithm Full band NLMS M-max PU NLMS Periodic PU NLMS, and Stochastic PU NLMS algorithms |
| title | Innovative approaches to beam forming antenna array systems with adaptive Partial Update NLMS algorithms |
| title_full | Innovative approaches to beam forming antenna array systems with adaptive Partial Update NLMS algorithms |
| title_fullStr | Innovative approaches to beam forming antenna array systems with adaptive Partial Update NLMS algorithms |
| title_full_unstemmed | Innovative approaches to beam forming antenna array systems with adaptive Partial Update NLMS algorithms |
| title_short | Innovative approaches to beam forming antenna array systems with adaptive Partial Update NLMS algorithms |
| title_sort | innovative approaches to beam forming antenna array systems with adaptive partial update nlms algorithms |
| topic | Beamforming array antennas Partial update adaptive algorithm Full band NLMS M-max PU NLMS Periodic PU NLMS, and Stochastic PU NLMS algorithms |
| url | http://www.sciencedirect.com/science/article/pii/S2772671124004340 |
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