Research On Resource Allocation Based On Cooperative Relay In Cognitive Radio Networks

Cognitive radio (CR) is a new promising technology to improve the utilization of the limited spectral resources, which allows the primary users (PUs) and secondary users (SUs) to share the same spectrum. Spectrum availability of SUs is heterogeneous due to the location difference and opportunistic spectrum access nature of SUs, therefore the resource unbalance in cognitive radio networks (CRNs) is much more severe than traditional wireless networks. Cooperative relay can solve this problem by using relay nodes to bridge the transmitter and receiver exploiting channels common with them respectively. With the introduction of cooperative relay, the allocation of wireless resources, such as power and spectrum, becomes a key problem need to be solved, which is the issue this paper focuses on.First, the problem of power control and allocation over cognitive communication links is investigated. Two methods are proposed to maximize the throughput under the QoS requirement of the primary system. Parameter assisted power allocation firstly allocates the total power between direct and relay channels by a parameter, and then allocates separately using iterative water-filling (IWF) algorithm. Joint power allocation allows allocating power simultaneously over the two kinds of channel by using joint water-filling (JWF) algorithm. This method performs better than the first one at the price of a little complexity, thus it is more effective and valuable.The problem of joint relay selection and channel allocation in a centralized CRN is also investigated. Two algorithms—parallel algorithm and greedy algorithm—are proposed based on maximum flow theory, and the complexity of both algorithms is analyzed. Simulation results show that both algorithms are able to allocate the resources effectively, so as to improve the spectrum efficiency and the throughput of the network. Parallel algorithm can achieve the optimal solution, but its complexity grows rapidly with the channels'common available degree. Greedy algorithm may not necessarily be the optimal solution, but has low complexity and is closer to the optimum solution when there are more common channels.