The Research Of Node Selection In Wirelesscooperative Communication

Communication nodes cooperate and forward the information to improve the reliability and effectiveness in the cooperative communication network,which becomes the research hot spot.In multi-users cooperative communication,the selection of nodes and the method of forward are the key problem which affect the performance of the network.In this paper,we focus on the node selection and the methods of forward to reduce the transmission relay and increase the system capacity.In this thesis,we apply the coalition games theory into the cooperative communication.The node selection problem of the task perform process in the Unmanned Aerial Vehicles(UAVs)network is researched.In UAVs network,the mobility of tasks leads to that the information of tasks is uncertain.So the belief update mechanism of uncertain tasks is used to calculate the probability that the task is in the range of potential coalition in each environment.On this basis,the Bayesian coalition games model is employed to solve the node selection problem when the information of the tasks is uncertain.According to the achieved payoff and cost in terms of the throughput and delay,members in the coalition take distributed decisions to leave or join in the coalition.Moreover,the algorithm of the Bayesian coalition games is proposed.The Simulation results demonstrate the Bayesian core is stability and the utility of the corresponding coalition is maximum.In this thesis,the compressed sensing(CS)theory is applied into the cooperative communication.The compressed quantize-and-forward(QF)protocol based on compressed sensing of discrete memory-less half duplex channel model is proposed.The coalition formation game is used as the relay selection strategy to choose the optimal relay nodes.And that,the system model use the difference between channel capacity and communication overhead as the system utility to analyze the performance.It is proved that the quantize-and-forward based on compressed sensing(CSQF)is more efficient than the amplify-and-forward based on compressed sensing(CSAF)in low Signal-to-Noise-Ratio(SNR)case.