An improved resource allocation architecture utilising swarm intelligence for mm-wave MIMO communication architecture

Abstract

The recent years have witnessed the utilisation of machine learning architecture adopted in the recent frames of mm-wave-based MIMO communication system. Despite the fruitful outcomes, several challenges including allocation of resources and channel remain hot areas of research. In a similar context, the paper proposes a load utilisation-oriented Swarm-based Artificial Bee Colony algorithm for better utilisation of the resources. In order to attain maximum utilisation with minimum power consumption, a reward mechanism has been generated. The proposed work uses the Levenberg principle for layer propagation which is also a Machine Learning mechanism that is utilised for better allocation of channels to deliver enhanced quality in the communication process. The improved strength of the proposed allocation mechanism is evaluated in terms of throughput and Bit Error Rate (BER) by comparing against different scenarios. Further, comparative analysis against existing swarm-based optimisation architectures is also conducted to demonstrate −2% reduction in the BER using proposed allocation work.