摘要
针对认知网络的学习环节和行动环节,提出了一种新的接入模型.根据同构型环境下的主节点存在区域,通过构造次级节点接入以获得主网络节点最大化吞吐量的目的.在此基础上,对同构型的认知网络建立了拥塞控制算法TCP-Vegas.最后,以实际数据对网络性能进行仿真评估,结果表明该算法能够较好地指导认知网络的决策与行动.
A novel access model is proposed for the learning and action of cognitive network in this paper. With homogeneous environment master node region, the maximize throughput of main node in network is got by secondary node access. Based on this model, the congestion control algorithm of TCP-Vegas is presented by cognitive network homogeneous network. Finally, a simulation with actual data is estimated for network performance, and result shows that, it is better guidance of cognitive network decision and action.
出处
《微电子学与计算机》
CSCD
北大核心
2013年第10期13-16,共4页
Microelectronics & Computer
基金
国家自然科学基金项目(10662002)
