PROJECT TITLE :

Traffic-Aware Optimal Spectral Access in Wireless Powered Cognitive Radio Networks - 2018

ABSTRACT:

Traffic patterns related to completely different primary users (PUs) may provide completely different spectral access and energy harvesting opportunities to secondary users (SUs) in wireless powered cognitive radio networks (WP-CRNs). Since the traffic applications have their own distinctive patterns, spectral access and energy harvesting opportunities also are expected to be distinctive. During this Project, we tend to propose a novel approach to identify the PU traffic patterns and estimate the energy harvested from each traffic pattern therefore that SU can maximize its capability accordingly. More specifically, we tend to propose a theoretical framework primarily based on a variational inference algorithm to cluster numerous traffic patterns and style a threshold-based mostly SU transmission strategy by taking into consideration the spectral access and energy harvesting opportunities for every traffic pattern, thus as to optimize SU transmission. Through simulations, we demonstrate the effectiveness of the proposed scheme in terms of throughput gains and show the transmission thresholds under numerous traffic applications (patterns). Any, we tend to illustrate the consequences of different collision costs on throughput for different traffic applications using real wireless traces.