摘要
ad hoc认知网络(CRAHNs)比起传统ad hoc网络有很多优势,如分布式多跳结构及动态变化的网络拓扑等。频谱感知是认知无线电技术的基础,采用压缩感知方法以低于奈奎斯特采样率的速率来获得较好的重构信号,同时可减轻数字处理设备的压力。本文使用多跳模型,每个认知用户感知的频谱不仅包括主用户的主频谱,也包括认知用户自身收到其他认知用户干扰的更新频谱。由于具有动态特性的ad hoc网络缺少融合中心,本文将基于梯度的分布式协作感知算法应用于CRAHNs。该算法基于能量检测,具有可靠的感知性能和较少的能量消耗,并能快速收敛。仿真实验验证了算法的有效性。
Cognitive radio ad hoc networks（CRAHNs） have several advantages over traditional ad hoc networks,like distributed multi-hop architecture,variant network topology and so on.Spectrum sensing is a foundation in the cognitive radio technology.We adopt the compressed sensing approach to attain high-resolution signal recovery at lower than Nyquist sampling rate.In this paper,we use the multi-hop architecture.Each CR sense its individual spectral map that consists of both common spectral components from primary users and individualized spectral innovations arising from interference of other CRs.CRAHNs are dynamic and have no central entity for data fusion.Therefore in this paper,we adopt a gradient-based distributed cooperative scheme in CRAHNs.The proposed scheme is based on energy detection.These distributed algorithms converge fast to the globally optimal solutions and have little energy consumption.Simulation results show the effectiveness of the proposed scheme.
出处
《信号处理》
CSCD
北大核心
2012年第10期1402-1407,共6页
Journal of Signal Processing
基金
国家自然科学基金项目(60972039)
江苏自然科学基金(BK2010077)
关键词
认知无线电
ad
HOC网络
压缩感知
分布式协作感知
基于梯度法
cognitive radios
ad hoc networks
compressed sensing
distributed collaborative sensing
gradient-based scheme
