基于PCN的水下认知网络动态频谱接入算法

Dynamic Spectrum Access Algorithm Based on PCN in Underwater Cognitive Network

水下网络可用频谱范围比较窄,且部分被水下生物占用,导致了水下传感器网络可用的频谱资源更为稀缺.针对上述问题,提出一种基于累积干扰预测(Predicted Cumulative Noise,PCN)的水下认知网络动态频谱接入算法.该算法把水下生物作为认知网络的主节点,水下传感器节点作为次节点;通过建立水下生物业务行为的马尔科夫模型预测累积干扰,次节点根据预测结果,采用合作的方式动态地接入授权频谱.仿真结果表明,该算法能够保护水下生物正常通信的同时,实现最优化的频谱共享,网络容量增益达到6.3d B.

The range of available spectrum in underw ater sensor netw ork is narrow; part of it is occupied by underw ater creatures. Therefore,the available spectrum resources become scarcer. In view of this,the algorithm based on predicting cumulative noise in underw ater cognitive netw orks to achieve dynamic spectrum accessing is proposed. The primary node is underw ater biology; the secondary node is sensor node. The M arkov model is established to predict noise in spectrum. The secondary nodes dynamically access the spectrum by cooperative method under the predicted states. The experiments verify that the algorithm realizes the optimization of spectrum sharing under protecting underw ater creatures and improves the capacity gain 6. 3d B.