中文摘要：

协作感知技术可提高认知无线电网络中的频谱资源利用率,但网络节点在形成协作感知联盟的同时也不可避免地引入了额外开销,联盟内节点总希望用较少的额外能量开销达到较大的吞吐量期望.为此,文中提出了协作感知系统的多目标非线性优化问题,然后基于联盟博弈理论为该问题构建了一个不可转移支付的联盟构造博弈模型,在其核心的支付函数的设计中,采用线性加权和的方法同时考虑了节点吞吐量期望和能量消耗两个优化目标.基于该函数,提出了一种分布式多目标联盟构造算法DMCF,其核心是根据优超算子所定义的联盟的帕累托顺序,循环地对联盟进行合并和分裂操作.此外,还证明了DMCF的收敛性和最终联盟划分的稳定性.仿真实验的结果表明,DMCF可有效解决提出的多目标优化问题,与一种分布式随机联盟构造算法DRCF相比,DMCF总能使节点消耗较少能量却达到相对较大的吞吐量期望.在不同网络规模下,DMCF可获得的节点平均吞吐量期望可提升约7.5%,而节点平均能量消耗却可降低约70%.

英文摘要：

Cooperative sensing technology can greatly improve the spectrum utilization in cognitive radio networks.However,with the formation of cooperative sensing coalition,it inevitably introduces extra cost.All the nodes within a coalition expect to achieve a higher throughput with less extra energy cost.In this paper,we address the multi-objective non-linear programming problem for cooperative sensing.Based on coalition game theory,we construct a non-transferable utility coalition formation game for the problem.In the design of its payoff function,we assign the throughput expectation and the energy cost with different weights,so that these two objectives are jointly considered.After that,we propose a distributed multi-objective coalition formation algorithm DMCF,in which coalition are iterated merged and split according to Pareto order.In addition,we show the convergence of the proposed algorithm and the stability of the final coalition partition.Extensive experiments by simulations demonstrate that,compared with the result delivered by a distributed random coalition formation algorithm DRCF,algorithm DMCF can increase the node＇s throughput expectation by 7.5%,however decrease the energy cost significantly by 70%,which shows great effectiveness to deal with the proposed multi-objective optimization problem.