Research On Power Allocation In Cognitive Radio Networks

With the rapid development of wireless communications technology,the future wireless mobilecommunication systems require a higher data rate,higher spectrum efficiency,higher reliability andlower communication transmit power.The cognitive radio(CR)technology,thereby becomes thedevelopment trend of wireless communications technology,and becomes the preferred technology ofthe next generation wireless communication because of its high spectral efficiency,sufficientflexibility to support high data rates and to ensure system reliability. This paper focus on the researchof power allocation in cognitive radio networks.There are three topics as follows:First,we propose a scheme called cognitive cooperative relaying. The key feature of theproposed scheme is that cognitive relay can sense the spectrum activity , serve the spectrum policyand relay secondary signal so that the transmission rate of the secondary link will be maximized. Thecognitive relay uses cooperative relay so that spectrum diversity can be obtained.Second,we reserch the cognitive relay power allocation based on convex optimization.Considering under the interference temperature constraints, how to maximize the system capacitythrough reasonably sub-carriers power distribution. Article based on convex optimization theorygives the optimal iterative algorithm of the problem and improve the algorithm from the perspectiveof the average time complexity, and then propose a suboptimal algorithm. Simulation results showthat the suboptimal algorithm compared to optimal algorithm has some performance cost, but thecomputational complexity is greatly reduced.Third, we consider the question of how to improve the power efficiency of sub-user network inresource-constrained wireless cognitive networks. In this paper,we study optimal power allocationbased on the power efficiency of the sub-users in cognitive radio networks. The paper not only givesthe optimal power allocation algorithm, but also propose a low complexity suboptimal fast selectionalgorithm. Finally, the simulation shows that compared to optimal algorithm the sub-optimalalgorithm brings only limited performance loss. In addition, when close to the source node, relaynode is more conducive to the optimization of system performance, and the link of source node tothe destination than the relay link has a greater impact on system performance.