Optimizing spectral efficiency based on poisson queuing model for procuring cognitive intelligence in vehicular communication

In vehicular networks, accidents and congestions constitute a significant risk due to numerous cause such as short link lifetime, spectrum competence and high mobility. Further, the shortage of spectrum is one of the significant concerns in vehicular communication. Hence, the competent supervision of spectrum utilization in vehicular networks is predominant. In vehicular communication, cognitive radio plays a crucial role to emphasize the implications of spectral proficiency. Henceforth, Poisson Queuing Model for procuring Cognitive Intelligence in Vehicular Communication (CIVC) is proposed to emphasize the purpose of spectrum in vehicular networks. The Poisson based queuing model deals with problems which involve waiting for specifics services (i.e., spectrum). Functional architecture is proposed to reduce both the bandwidth scarcity and spectrum inefficiency complication. Further, the proposed system aims to evaluate spectral competence for queuing model based on a single server and queuing model based on multiple servers. In addition to that, the proposed system model contemplates three main scenarios and (i.e.,) vehicle waiting time distribution, vehicle loss probability and vehicle time dissemination of server busy period, and the mathematical analysis evaluates the purpose of Cognitive Intelligence in vehicular communication using Deep learning.