Deep Learning with Game Theory Assisted Vertical Handover Optimization in a Heterogeneous Network

Abstract

Problem: In next-generation networks, users can optimize or tune their preferences with a seamless transfer of diverse access methodologies for maximizing the Quality of Service (QoS) and cost savings. In these heterogeneous wireless environments, users are prepared with several multimode wireless devices for maximizing media services through several access networks. Such networks may vary regarding energy usage, available bandwidth, technology, coverage range, monetary cost, etc. In recent days, vertical handover has attained higher performance owing to the improvements in mobility models through adopting the Fourth Generation (4G) technologies. On the other hand, these improvements are restricted to some cases, so, it does not offer support for generic mobility. Consequently, diverse strategies were implemented by considering these mobility models. However, it suffers from improper network selection, late and too-early handovers, repeated handovers, high packet loss, etc. Aim: This paper tackles the problem of vertical handover problem in the heterogeneous network using deep learning with game theory. Methods: The proposed model develops a non-cooperative game approach, in which all base stations compete selfishly to transmit at higher power. The overall performance in terms of throughput, handover, energy consumption, and load balancing is attained by optimizing the transmission power by the game theory. For performing this model, the required data like path loss, SINR, data rate, load, etc are generated by the deep learning called Recurrent Neural Network (RNN). Results: From the simulation findings, the handoff probability of the recommended RNN+Game Theory is correspondingly secured at 6.9%, 22.6%, and 8.2% superior to TOPSIS, ABC-PSO, and game theory when taking the time like 5 secs for user velocity as 30 km/h. Conclusion: Results show that the proposed game theoretical approach with deep learning provides a throughput enhancement while reducing the power consumption, in addition, to minimizing the unnecessary handover and balancing the load between base stations.