ABSTRACT
The proliferation of linked mobile communication devices worldwide has inspired researchers to devise a creative mode switching paradigm. Reciprocated Bayesian-recurrent neural network classifier (RB-RNN-C) is used in this work to develop a unique mode-switching model. Quality characteristics such as link usage, bandwidth, latency, energy usage, and signal strength are used to switch modes. Whenever the network moves from cellular mode to user mode, the quality characteristics must be maintained. To enhance the capacity of network mobility management, a methodology for calculating user mobility has been developed. The presented method is critical to enhancing user mobility while communicating. In terms of latency, energy usage, link usage ratio, and throughput, RB-RNN-C mode switching is excellent. These factors are depicted as findings in the graphical formation using the Origin tool.
