认知网络基于跨层感知的接入选择机制  

Access point selection mechanism based on cross-layer awareness for cognitive networks

作　　者：王慧强[1] 陈晓明[1,2] 吕宏武[1] 冯光升[1] 朱强[1] 

机构地区：[1]哈尔滨工程大学计算机科学与技术学院,黑龙江哈尔滨150001 [2]哈尔滨理工大学管理学院,黑龙江哈尔滨150080

出　　处：《通信学报》2015年第10期188-199,共12页Journal on Communications

基　　金：国家自然科学基金资助项目(61370212;61402127;61502118);教育部博士点基金优先发展领域基金资助项目(20122304130002);黑龙江省自然科学重点基金资助项目(ZD201102);中央高校基本科研业务费专项基金资助项目(HEUCF100601;HEUCFZ1213);黑龙江省博士后基金资助项目(LBH-210204);黑龙江省自然科学基金资助项目(F2015029)~~

摘　　要：选择最优接入网络是确保网络资源有效分配和提高网络整体性能的有效手段。提出一种基于跨层感知的接入选择方法 CN_CLA,构建跨层感知框架模型以获取影响接入网性能的主要评估参数,采用模糊理论对可接入网络的性能进行综合评测,应用量子遗传算法对评判权值进行优化,最终实现接入网络的智能选择。实验结果表明,CN_CLA能够在无用户干涉情况下合理地选择接入网络,并在吞吐量、时延、会话完成率、分组丢失率等方面优于链路容量法、H_RSSI_S及AS_FTM接入选择算法。Accessing to the optimal network was an effective way of ensuring the efficiency of network resources utilization and improving network performance. An access point selection mechanism based on cross-layer awareness for cognitive networks（CN_CLA） was proposed. Firstly, a cross-layer cognitive framework was constructed for obtaining the primary evaluation parameters that influence the performance of network access. Secondly, the fuzzy theory was applied for evaluating the access network performance comprehensively, and the weights in each layer were optimized using the quantum genetic algorithm, and then the access point was selected intelligently. Simulation results show that the proposed method chooses reasonable access networks without intervention of users. Furthermore, it is superior to the traditional methods, including the link capacity scheme, H_RSSI_S and AS_FTM, in terms of throughput, delay, session completion rate, packet loss and other performance indicators.

关 键 词：认知网络 跨层感知 模糊综合评判 量子遗传算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]