摘要：

在认知无线电网络中,频谱感知的效能往往通过系统的吞吐量进行体现。为此,在传统感知帧结构的基础上,通过引入协作频谱预测和频谱分割定义一种新的感知帧结构,并结合基于DBSCAN的隐马尔科夫模型协作频谱预测算法,提高频谱预测的准确率,降低协作预测带宽的消耗,从而增加系统吞吐量。仿真结果表明,与不含协作模块的频谱分割帧结构和含有协作模块但未进行频谱分割的帧结构相比,改进的帧结构可有效提高系统吞吐量。

关键词: 协作频谱预测, 频谱分割, 频谱感知, 隐马尔科夫模型, 系统吞吐量

Abstract:

In cognitive radio network,spectrum sensing performance tends to be reflected by the system throughput.Based on the traditional perceptual frame structure,this paper redefines a new perceptual frame structure by introducing the cooperative spectrum prediction and spectrum segmentation.By combining the Hidden Markov Model(HMM) cooperative spectrum prediction algorithm based on DBSCAN,the accuracy of spectrum prediction is improved,the consumption of cooperative prediction bandwidth is reduced,and the effect of improving system throughput is achieved.Simulation results show that the system throughput under the improved frame structure is improved compared with that of the frame structure without the cooperation module and the frame structure with the cooperation module but without spectrum segmentation.

Key words: cooperative spectrum prediction, spectrum segmentation, spectrum sensing, Hidden Markov Model(HMM), system throughput

中图分类号: 

• TN92