基于拉丁方的认知差分跳频网络自适应频率集方法

基于拉丁方的认知差分跳频网络自适应频率集方法 A Frequency Set Adaptive Optimization Algorithm for Differential Frequency Hopping Network Based on Cognitive Radio and Latin Squares 程卓 CHENG Zhuo 1 2 first-author 王殊 WANG Shu 2 王典洪 WANG Dianhong 1 鄢舒 YAN Shu 2 中国地质大学机械与电子信息学院 中国地质大学机械与电子信息学院 School of Machinery and Electronic Information,China University of Geosciences School of Machinery and Electronic Information,China University of Geosciences 华中科技大学电子与信息工程系 华中科技大学电子与信息工程系 Department of Electronics and Information Engineering,Huazhong University of Science and Technology Department of Electronics and Information Engineering,Huazhong University of Science and Technology 将方法无线电和应用数学的原理引入多用户差分跳频网络,提出基于拉丁方的认知差分跳频自适应频率集方法.该方法在认知后得到可用频谱信息的基础上,将跳频频率集划分为频率子集,按照动态生成的拉丁方将频率子集动态分配给网内用户,使同一时隙内用户的频率子集正交,从而抑制多用户网络的多址干扰,提高抗部分频带干扰以及多音干扰性能.仿真结果显示该方法较传统多用户差分跳频网络可以显著提高多址性能与抗干扰性能. Differential frequency hopping（DFH）is believed to be an effective FH technique for increasing the data rate of FH communication system.Cognitive radio（CR）users can use the CR technology to sense the surrounding RF environment,search for available spectrum resources and access spectrum dynamically.The cognitive radio technology and applied mathematics are introduced in DFH.And the Latin squares based frequency set adaptive optimization（LSFSAO）algorithm is proposed.In short,the frequency set is divided into many frequency micro-sets after the spectrum sensing,and the micro-sets are assigned to DFH users with dynamic Latin squares.The users’ frequency micro-sets are dissimilar during the same time slot to cut down the Multiple Access Interference（MAI）,Partial Band Jamming（PBJ）and Multi-Tone Jamming（MTJ）.The scenario simulations of the LS-FSAO based DFH network and the traditional DFH are performed,respectively.The results show that the LS-FSAO DFH can obtain favorable network performance. 认知无线电（CR） 差分跳频（DFH） 拉丁方 cognitive radio（CR） differential frequency hopping（DFH） Latin squares 10.14188/j.1671-8836.2014.03.013 TN914.41 中央高校基本科研业务基金（CUG120866）;湖北省自然科学基金（2012FFB6402）资助项目 2014-03-01 2021-04-01 3 60