Embedded Parallel Distributed Artificial Intelligent Processors for Adaptive Beam Forming in WCDMA System

Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. One application would be in WCDMA adaptive beam forming technique. Adaptive antenna has dynamic beam to cater for users' needs and provides better capacity for mobile communication but requires more intelligent and advance beam forming algorithm such as genetic algorithm. Compared to Standard GAs, Parallel Distributed GAs promise substantial gain in terms of convergence performance. In this paper, an embedded parallel and distributed genetic algorithm (EPDGA) with dynamic parameter setting on a multiprocessor system is proposed. The proposed scheme applies a master-slave architecture where the total active unit equipments (UE) are distributed to subpopulations (slaves) that evolve separately and exchange individuals occasionally. The power usage at Node B is used as fitness function to compare the performance of EPDGA and standard GA. Simulation results show that EPDGA converges faster and is better in adaptive antenna beam forming in the aspect of power usage at Node B as compared to standard GA.