Cooperative Relay Selection In Cognitive Radio Networks

Recently Cognitive Radio Networking has attracted a great attention from both academia and industry due to its remarkable improvement in spectrum utilization efficiency. We notice that in a Cognitive Radio Network the direct transmissions from a transmitter to the intended receiver may be severely damaged due to the unstable environment in wireless communications such as multipath fading and shadowing. In order to mitigate these fading effects, users can use the cooperative relaying technic which can obtain the spatial diversity gain. In particular, we consider the problem of cooperative relay selection in Cognitive Radio Networks, in which the PUs actively select appropriate SUs as relay nodes to enhance their transmission performance.In this paper we firstly introduce the concept of Cognitive Radio Networks and cooperative relaying. The most critical concern for cooperative relay selection is efficiency, As the number of secondary users could be large due to the rapidly growing number of mobile communication devices, it is impossible to observe all the candidate relays for a primary transmitter. Thus we propose the cooperative relay selection based on stopping theory, with an objective of stopping early enough to avoid scanning all the candidate relays. We address this problem by using the optimal stopping theory and derive the optimal stopping rule. We also discuss the optimal observation order of the SUs and analyze the collision probability. To investigate the problem of selecting multiple relays for each primary transmitter, we also propose the cooperative relay selection for multiple relays. We apply the multiple stopping theory to address this problem and derive the optimal multiple stopping rule. We prove that corresponding threshold stopping times derived by optimal multiple stopping rule are optimal m-stopping times. To evaluate the performance of the two proposed schemes, we compare the two cooperative relay selections with the random selection policy through simulation study. The results show the superiority of our policies. Extensive simulation study is also conducted to investigate the impact of different parameters on the system performance.