WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm
WCDMA mobile communication systems provide enhanced high-speed data, multimedia, and voice services to mobile users. The integration of such heterogeneous traffic types implies that the network must provide differentiated Quality of Service (QoS). Beam forming techniques have been proposed to increa...
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Oficyna Wydawnicza Politechniki Wroclawskiej
2023
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my.uniten.dspace-308552023-12-29T15:54:43Z WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm Prajindra S.K. Tiong S.K. Johnny Koh S.P. Yap D.F.W. 36053261400 15128307800 22951210700 22952562500 Adaptive Beam Forming Genetic Algorithm Microcontroller WCDMA Algorithms Antennas Beamforming Controllers Medical applications Microcontrollers Mobile telecommunication systems Multimedia services Outages Quality of service Radio broadcasting Resource allocation Adaptive beam-forming Beamforming technique Differentiated quality of services Microcontroller boards Microcontroller systems Parallel genetic algorithms Power resource managements WCDMA Genetic algorithms WCDMA mobile communication systems provide enhanced high-speed data, multimedia, and voice services to mobile users. The integration of such heterogeneous traffic types implies that the network must provide differentiated Quality of Service (QoS). Beam forming techniques have been proposed to increase the spectral efficiency of the wireless channel. In this work, a new microcontroller board was designed and developed with Genetic Algorithm (GA) embedded on board. The embedded GA optimization scheme was based on minimum downlink power consumption tailored for a WCDMA network that employs adaptive antennas in support of a heterogeneous user mix. The algorithm works by finding the minimum transmitter power with the help of Distributed Parallel Genetic Algorithm (DPGA) employed on an offload microcontroller system to form optimal beam coverage to reduce power usage of adaptive antenna at WCDMA base station. The power usage at Node B is used as fitness function to compare the performance of DPGA and conventional GA (cGA). Simulation results show that DPGA converges faster and is superior in power resource management and returns better teletraffic performance in system outage and capacity. Final 2023-12-29T07:54:43Z 2023-12-29T07:54:43Z 2009 Conference paper 2-s2.0-84923413356 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923413356&partnerID=40&md5=feecda48993ab11322a36f6e9583e5f3 https://irepository.uniten.edu.my/handle/123456789/30855 277 282 Oficyna Wydawnicza Politechniki Wroclawskiej Scopus |
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Adaptive Beam Forming Genetic Algorithm Microcontroller WCDMA Algorithms Antennas Beamforming Controllers Medical applications Microcontrollers Mobile telecommunication systems Multimedia services Outages Quality of service Radio broadcasting Resource allocation Adaptive beam-forming Beamforming technique Differentiated quality of services Microcontroller boards Microcontroller systems Parallel genetic algorithms Power resource managements WCDMA Genetic algorithms |
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Adaptive Beam Forming Genetic Algorithm Microcontroller WCDMA Algorithms Antennas Beamforming Controllers Medical applications Microcontrollers Mobile telecommunication systems Multimedia services Outages Quality of service Radio broadcasting Resource allocation Adaptive beam-forming Beamforming technique Differentiated quality of services Microcontroller boards Microcontroller systems Parallel genetic algorithms Power resource managements WCDMA Genetic algorithms Prajindra S.K. Tiong S.K. Johnny Koh S.P. Yap D.F.W. WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm |
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WCDMA mobile communication systems provide enhanced high-speed data, multimedia, and voice services to mobile users. The integration of such heterogeneous traffic types implies that the network must provide differentiated Quality of Service (QoS). Beam forming techniques have been proposed to increase the spectral efficiency of the wireless channel. In this work, a new microcontroller board was designed and developed with Genetic Algorithm (GA) embedded on board. The embedded GA optimization scheme was based on minimum downlink power consumption tailored for a WCDMA network that employs adaptive antennas in support of a heterogeneous user mix. The algorithm works by finding the minimum transmitter power with the help of Distributed Parallel Genetic Algorithm (DPGA) employed on an offload microcontroller system to form optimal beam coverage to reduce power usage of adaptive antenna at WCDMA base station. The power usage at Node B is used as fitness function to compare the performance of DPGA and conventional GA (cGA). Simulation results show that DPGA converges faster and is superior in power resource management and returns better teletraffic performance in system outage and capacity. |
author2 |
36053261400 |
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36053261400 Prajindra S.K. Tiong S.K. Johnny Koh S.P. Yap D.F.W. |
format |
Conference paper |
author |
Prajindra S.K. Tiong S.K. Johnny Koh S.P. Yap D.F.W. |
author_sort |
Prajindra S.K. |
title |
WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm |
title_short |
WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm |
title_full |
WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm |
title_fullStr |
WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm |
title_full_unstemmed |
WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm |
title_sort |
wcdma teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm |
publisher |
Oficyna Wydawnicza Politechniki Wroclawskiej |
publishDate |
2023 |
_version_ |
1806425682407849984 |
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13.222552 |