In cognitive radio networks, Secondary Users (SUs) have opportunities to access the spectrum channel when primary user would not use it, which will enhance the resource utilization. In order to avoid interference to p...In cognitive radio networks, Secondary Users (SUs) have opportunities to access the spectrum channel when primary user would not use it, which will enhance the resource utilization. In order to avoid interference to primary users, it is very important and essential for SUs to sense the idle spectrum channels, but also it is very hard to detect all the channels in a short time due to the hardware restriction. This paper proposes a novel spectrum prediction scheme based on Support Vector Machines (SVM), to save the time and energy consumed by spectrum sensing via predicting the channels' state before detecting. Besides, spectrum utilization is further improved by using the cooperative mechanism, in which SUs could share spectrum channels' history state information and prediction results with neighbor nodes. The simulation results show that the algorithm has high prediction accuracy under the condition of small training sample case, and can obviously reduce the detecting energy, which also leads to the improvement of spectrum utilization.展开更多
针对在认知无线电网络(Cognitive Radio Network,CRN)中进行协作频谱感知(Cooperative Spectrum Sensing,CSS)时,随着次级用户(Secondary User,SU)数量N增加导致能量向量维度随之提高造成的数据处理时间增加,设计开发了使用恒定二维向...针对在认知无线电网络(Cognitive Radio Network,CRN)中进行协作频谱感知(Cooperative Spectrum Sensing,CSS)时,随着次级用户(Secondary User,SU)数量N增加导致能量向量维度随之提高造成的数据处理时间增加,设计开发了使用恒定二维向量的机器学习算法。首先在由N个SU组成的CRN中进行频谱感知,获取每个SU感知得到的能量数值并组成N维能量向量。然后对N维能量向量进行数据处理,将其变换为恒定的二维特征向量——概率向量,并利用K-Mediods和模糊支持向量机(Fuzzy Support Vector Machine,FSVM)算法对此向量进行训练和分类。针对2个SU和16个SU建立仿真场景,分别基于N维能量向量与概率向量进行研究,仿真结果表明,在SU数量增加的场景下,使用概率向量的训练时间降低了至少31%,分类延迟略有减少。展开更多
基金Sponsored by the Youth Foundation of Beijing Univesity of Postsand Telecommunications(Grant No.2011RC0110)Director Foundation of Key Lab of Universal Wirelsess Communication of Ministry of Education(Grant No.ZRJJ-2010-3)Ministry of Industry and Information Technology of China(Grant No.2011ZX03001-007-03)
文摘In cognitive radio networks, Secondary Users (SUs) have opportunities to access the spectrum channel when primary user would not use it, which will enhance the resource utilization. In order to avoid interference to primary users, it is very important and essential for SUs to sense the idle spectrum channels, but also it is very hard to detect all the channels in a short time due to the hardware restriction. This paper proposes a novel spectrum prediction scheme based on Support Vector Machines (SVM), to save the time and energy consumed by spectrum sensing via predicting the channels' state before detecting. Besides, spectrum utilization is further improved by using the cooperative mechanism, in which SUs could share spectrum channels' history state information and prediction results with neighbor nodes. The simulation results show that the algorithm has high prediction accuracy under the condition of small training sample case, and can obviously reduce the detecting energy, which also leads to the improvement of spectrum utilization.
文摘针对在认知无线电网络(Cognitive Radio Network,CRN)中进行协作频谱感知(Cooperative Spectrum Sensing,CSS)时,随着次级用户(Secondary User,SU)数量N增加导致能量向量维度随之提高造成的数据处理时间增加,设计开发了使用恒定二维向量的机器学习算法。首先在由N个SU组成的CRN中进行频谱感知,获取每个SU感知得到的能量数值并组成N维能量向量。然后对N维能量向量进行数据处理,将其变换为恒定的二维特征向量——概率向量,并利用K-Mediods和模糊支持向量机(Fuzzy Support Vector Machine,FSVM)算法对此向量进行训练和分类。针对2个SU和16个SU建立仿真场景,分别基于N维能量向量与概率向量进行研究,仿真结果表明,在SU数量增加的场景下,使用概率向量的训练时间降低了至少31%,分类延迟略有减少。
