论文部分内容阅读
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。本文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两种思想,重新定义了新的感知帧结构,并通过结合基于DBSCAN的隐马尔科夫协作频谱预测算法,提高了频谱预测的准确率和降低了协作预测带宽的消耗,进而达到提高系统吞吐量的效果。仿真实验结果表明,该方案可以有效提高系统的吞吐量。
In cognitive radio networks, the performance of spectrum sensing is often reflected by the system throughput. Based on the traditional perceptual frame structure, this paper redefines the new perceptual frame structure through the introduction of collaborative spectrum prediction and spectrum segmentation. Combined with the hidden-Markov collaborative spectrum prediction algorithm based on DBSCAN, this paper improves the performance of spectral prediction Accuracy and reduce the consumption of collaborative prediction bandwidth, and then to achieve the effect of improving system throughput. Simulation results show that the scheme can effectively improve the system throughput.