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Optimal resource allocation for cognitive radio networks with multi-group multicast
For the increasing shortage of radio spectral resource,a new multi-group multicast(MGMC) mechanism based on inter-group and inner-group cooperative transmission is proposed,which relates to multiple multicast groups and uses the same spectrum resource to transmit information in a cooperative way. In cognitive radio(CR) network based on this new transmission mechanism,the optimal resource allocation of system is presented. The power allocation scheme is derived from theoretical analysis,and then the optimization of system weighted overall rate is discussed. Moreover,the impact of the signal interference between primary user(PU) and CR users and the power constraint on the transmission rate is considered to optimize user performance.Simulation result indicates that the transmission rate of multi-group multicast rises with the increasing number of users,and optimal user quality of service(QoS) is achieved;when power is constrainted,the PU can be ensured to have good communication performance by setting weighted factors for every multicast group.
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A vertical handoff algorithm with optimal energy efficiency for heterogeneous networks
In view of the problem that traditional vertical handoff fails to take a crucial factor,energy efficiency,into consideration,an optimal energy efficiency vertical handoff algorithm is proposed. The algorithm’s core is that the mobile terminal tests and calculates the intensity of different received signals from different candidate networks,and system capacity of different candidate areas. Capacity provided by the energy of each unit in candidate areas is taken as decision criterion to realize handoff to the optimal network. Meanwhile,traditional network factors are combined to perform energy efficiency expansion on the multiple attributes decision algorithm. Simulation results show that the handoff algorithm based on unit energy capacity saves about 10% energy when compared with the traditional one,while the expanded multiple attributes decision algorithm saves about 7% energy. Meanwhile,“ping pang effect” is reduced,thereby improving users’communication experience quality.
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2024
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2024
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2024
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2024
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2023
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