WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm

Abstract

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.