The Analysis Of Performance And Study Of Energy-Harvesting In Cognitive Radio Networks

In Cognitive radio networks, cognitive users need to sense before transmitting. Then, it decides whether to access according sensing result. However, licensed users can access channels any time, which contributes to QoS(Quality of Service). Therefore, how to maximize the throughput while strictly guaranteeing the QoS of cognitive users still faces serious challenge. Moreover, the process of sensing, transmitting and data processing will consume much energy, which is a problem. It can be solved by energy harvesting technology. In energy-harvesting cognitive radio networks, the studies mostly focused on energy harvesting of transmitter However, the consumption in the receive side is not a negligible issue. Considering this problem, how to guarantee the throughput not decreasing is challenging.To solve the problem that strictly guaranteeing the QoS of cognitive users, we proposed a new strategy based on dynamic admission and handoff control. Our purpose is to maximize the throughput strictly guaranteeing the QoS of cognitive users. Firstly, we analyze the performance of the system by developing a Markov model. Then, by solve a non-linear programming problem we get the optimal admission and eviction control probability. Lastly, compared with traditional admission control, we find the proposed strategy is the best. Although the above proposed system improved spectrum efficiency as much as possible, it didn't consider energy efficiency. Therefore, this paper proposed a cognitive radio networks working in slots, in which the transmitter and receiver supplied by no-fixed power can harvest energy from surrounding environment, which becomes a self-sustained system. Every slot is divided three parts: harvesting, sensing and transmitting. Considering balancing of the three parts, we need to find the optimal time allocation to maximize the throughput. In addition, we found that the energy harvesting rate of transmitter and receiver affect the throughput. Then, the formulation is formed a constrained nonlinear programming problem,by solving which we can find the best time allocation. The numerical results confirm the effectiveness of the proposed system.