A Metaheuristec Approach for Feature Extraction of mmWave based 5G Communication Network

With the increase in subscribers day-by-day, the popularity of mmWaves is also increasing due to its ability to increase the bandwidth over smaller and densely populated areas. In the context of 5G networks, resource allocation is a major issue which can be achieved by increasing the hardware which in turn lead to increase the cost of project. In contrast to it, another way to experience efficient resource allocation is to optimize the current system architecture. Thus along with mmWave based communication architecture, the use of swarm based algorithms can also be utilized to customize data for training the system. In this paper we have designed a network consisting of random number of nodes. Using the AODV Route Discovery process, we have discovered path from source to destination node. Further by applying channel estimation we have obtained QoS parameters. To analyze the QoS parameters Metaheuristic based ABC algorithm is used. The labeling of Good, Moderate and Bad QoS parameters is performed using ABC algorithm.