Genetic Algorithm Based BER Aware Channel Selection Using Break Point Technique For Next Generation Milli-Meter (mm) Wave Communication Systems

Abstract

Due to exponential increase in communication speed when shifting from 4th Generation 4G to 5G networks, there is a requirement to redesign equipment to support spectrum ranges from 450 MHz to 52.6 GHz, which makes them operate at very high speeds. In order to maintain good communication performance while operating at this bandwidth, millimeter waves (mmWaves) are used. As communication radius increases, the BER also increases linearly, which limits range of these equipment’s, thereby incurring higher deployment costs. In order to reduce these costs, and design mmWave communication components to work for larger areas, this text proposes a Genetic optimization architecture that uses intelligent channel modelling and selection. The architecture is designed in order to reduce BER during communication when threshold breakpoint occurs, thereby improving communication speed, and overall throughput. It exploits long-ranged loopback communications in order to automatically tune internal transmission parameters for supporting larger areas with minimum packet loss. The underlying model is tested on various channel types, different network scenarios, and under different noise conditions. It is observed that the proposed model outperforms original mmWave communication models in terms of BER reduction by 8% and in terms of communication coverage by 6%, thereby making it applicable for wider geographical areas. This results in reduced deployment costs, and better communication quality of service (QoS), thereby assisting in better network design.