Parallel Distributed Computational Microcontroller System for Adaptive Antenna Downlink Transmitter Power Optimization
This paper presents a tested research concept that
implements a complex evolutionary algorithm, genetic algorithm
(GA), in a multi-microcontroller environment. Parallel Distributed
Genetic Algorithm (PDGA) is employed in adaptive beam forming
technique to reduce power usage of adaptive antenna at WCDMA
base station. Adaptive antenna has dynamic beam that requires more
advanced beam forming algorithm such as genetic algorithm which
requires heavy computation and memory space. Microcontrollers are
low resource platforms that are normally not associated with GAs,
which are typically resource intensive. The aim of this project was to
design a cooperative multiprocessor system by expanding the role of
small scale PIC microcontrollers to optimize WCDMA base station
transmitter power. Implementation results have shown that PDGA
multi-microcontroller system returned optimal transmitted power
compared to conventional GA.
[1] R. L. Haupt, "Phase-Only Adaptive Nulling with a Genetic Algorithm",
IEEE Transactions on Antennas and Propagation, vol. 45, No. 6, June
1997. pp. 1009-1015.
[2] Y. Yashchyshyn and Piasecki M., "Improved Model of Smart Antenna
Controlled by Genetic Algorithm", VI-th Intemational Conference on
The Experience of Designing and Application of CAD Systems in
Microelectronics. Ukraine, 2001. pp. 147-150.
[3] S. K. Tiong, M. Ismail and A. Hassan. "Dynamic Characterized Genetic
Algorithm for Adaptive Beam Forming in WCDMA System", IEEE
International Conference on Communication, Nov 2005, pp.219-220.
[4] Takuma Jumonji, Goutam Chakraborty, Hiroshi Mabuchi and Masafumi
Matsuhara, "A novel distributed genetic algorithm implementation with
variable number of islands", Proc. IEEE Congress on Evolutionary
Computation, Sept 2007, pp. 4698.
[5] Erick Cant`u-Paz, "A survey of parallel genetic algorithms",
Calculateurs Paralleles, Reseaux et Systems Repartis, Vol.10, No.2,
pp.141-171, 1998.
[6] M. Miki, T. Hiroyasu, M. Kaneko, K. Hatanaka, "A Parallel Genetic
Algorithm with Distributed Environment Scheme", GECCO -00,
pp.376-376, 2000.
[7] Erick Cant`u-Paz, David E. Goldberg, "Are Multiple Runs of Genetic
Algorithms Better than One?", GECCO -02, pp.801-812, 2002.
[8] Weili Yi, Qizhen Liu and Yongbao He, "Dynamic distributed genetic
algorithms", Proc. IEEE Congress on Evolutionary Computation, July
2000, pp.1132.
[9] www.microchip.com.
[10] "PIC18F4550 Datasheet", [Online]. Available: www.microchip.com.
[1] R. L. Haupt, "Phase-Only Adaptive Nulling with a Genetic Algorithm",
IEEE Transactions on Antennas and Propagation, vol. 45, No. 6, June
1997. pp. 1009-1015.
[2] Y. Yashchyshyn and Piasecki M., "Improved Model of Smart Antenna
Controlled by Genetic Algorithm", VI-th Intemational Conference on
The Experience of Designing and Application of CAD Systems in
Microelectronics. Ukraine, 2001. pp. 147-150.
[3] S. K. Tiong, M. Ismail and A. Hassan. "Dynamic Characterized Genetic
Algorithm for Adaptive Beam Forming in WCDMA System", IEEE
International Conference on Communication, Nov 2005, pp.219-220.
[4] Takuma Jumonji, Goutam Chakraborty, Hiroshi Mabuchi and Masafumi
Matsuhara, "A novel distributed genetic algorithm implementation with
variable number of islands", Proc. IEEE Congress on Evolutionary
Computation, Sept 2007, pp. 4698.
[5] Erick Cant`u-Paz, "A survey of parallel genetic algorithms",
Calculateurs Paralleles, Reseaux et Systems Repartis, Vol.10, No.2,
pp.141-171, 1998.
[6] M. Miki, T. Hiroyasu, M. Kaneko, K. Hatanaka, "A Parallel Genetic
Algorithm with Distributed Environment Scheme", GECCO -00,
pp.376-376, 2000.
[7] Erick Cant`u-Paz, David E. Goldberg, "Are Multiple Runs of Genetic
Algorithms Better than One?", GECCO -02, pp.801-812, 2002.
[8] Weili Yi, Qizhen Liu and Yongbao He, "Dynamic distributed genetic
algorithms", Proc. IEEE Congress on Evolutionary Computation, July
2000, pp.1132.
[9] www.microchip.com.
[10] "PIC18F4550 Datasheet", [Online]. Available: www.microchip.com.
@article{"International Journal of Electrical, Electronic and Communication Sciences:52322", author = "K. Prajindra Sankar and S.K. Tiong and S.P. Johnny Koh", title = "Parallel Distributed Computational Microcontroller System for Adaptive Antenna Downlink Transmitter Power Optimization", abstract = "This paper presents a tested research concept that
implements a complex evolutionary algorithm, genetic algorithm
(GA), in a multi-microcontroller environment. Parallel Distributed
Genetic Algorithm (PDGA) is employed in adaptive beam forming
technique to reduce power usage of adaptive antenna at WCDMA
base station. Adaptive antenna has dynamic beam that requires more
advanced beam forming algorithm such as genetic algorithm which
requires heavy computation and memory space. Microcontrollers are
low resource platforms that are normally not associated with GAs,
which are typically resource intensive. The aim of this project was to
design a cooperative multiprocessor system by expanding the role of
small scale PIC microcontrollers to optimize WCDMA base station
transmitter power. Implementation results have shown that PDGA
multi-microcontroller system returned optimal transmitted power
compared to conventional GA.", keywords = "Microcontroller, Genetic Algorithm, Adaptiveantenna, Power optimization.", volume = "3", number = "2", pages = "203-5", }