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This paper investigates the relay selection and power allocation problem in multi-user based cooperative networks,where intermediate relay nodes help source forward information to destination using decode-and-forward (DF) relaying protocol. Specifically,we propose a novel multi-relay nodes selection strategy taking both instantaneous channel state information (I-CSI) and residual energy into consideration,by which ’emergence’ diversity gain can be achieved and the imbalance of resource utilization can be overcome. Besides,using Largangian dual-primal decomposition and subgradient projection approach,an optimal power allocation algorithm at source and cooperative relay nodes is presented with the constraints of each user’s individual quality of service (QoS) requirements and system total transmit power. Theoretical analysis and simulation results demonstrate that the proposed scheme can significantly improve energy efficiency,while guaranteeing a good balance between achievable data rate and average network lifetime with relatively low implementation complexity.
This paper investigates the relay selection and power allocation problem in multi-user based cooperative networks, where intermediate relay nodes help source forward information to destination using decode-and-forward (DF) relaying protocol. Specifically, we propose a novel multi-relay nodes selection strategy taking both instantaneous channel state information (I-CSI) and residual energy into consideration, by which ’emergence’ diversity gain can be achieved and the imbalance of resource utilization can be overcome. Besides, using Larguian dual-primal decomposition and subgradient projection approach, an optimal power allocation algorithm at source and cooperative relay nodes is presented with the constraint of each user’s individual quality of service (QoS) requirements and system total transmit power. Theoretical analysis and simulation results demonstrate that the proposed scheme can significantly improve energy efficiency while guaranteeing a good balance between achievable data rat e and average network lifetime with relatively low implementation complexity.