Abstract: This paper proposes a single relay selection scheme in
cooperative communication. Decode-and-forward scheme is
considered when a source node wants to cooperate with a single relay
for data transmission. To use the proposed single relay selection
scheme, the source node makes a little different pattern signal which is
not complex pattern and broadcasts it. The proposed scheme does not
require the channel state information between the source node and
candidates of the relay during the relay selection. Therefore, it is able
to be used in many fields.
Abstract: In this paper, we study the cooperative communications where multiple cognitive radio (CR) transmit-receive pairs competitive maximize their own throughputs. In CR networks, the influences of primary users and the spectrum availability are usually different among CR users. Due to the existence of multiple relay nodes and the different spectrum availability, each CR transmit-receive pair should not only select the relay node but also choose the appropriate channel. For this distributed problem, we propose a game theoretic framework to formulate this problem and we apply a regret-matching learning algorithm which is leading to correlated equilibrium. We further formulate a modified regret-matching learning algorithm which is fully distributed and only use the local information of each CR transmit-receive pair. This modified algorithm is more practical and suitable for the cooperative communications in CR network. Simulation results show the algorithm convergence and the modified learning algorithm can achieve comparable performance to the original regretmatching learning algorithm.