基于密度聚类的HMM协作频谱预测算法

doi:10.11896／j.issn.1002-137X.2018.09.020

摘要/Abstract

摘要： 对传统隐马尔科夫频谱预测中的时延长、预测准确度低的问题,提出了一种基于密度聚类(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)的HMM协作频谱预测算法。该算法采用DBSCAN算法将具有强相关性的频域信道聚为一簇,并以簇为单位对信道状态进行预测,通过减少预测次数来降低频谱预测时延;同时在时域利用多个次级用户协作预测的方法,通过融合各次级用户的初始预测结果来降低预测的不确定度。仿真实验表明,相比于传统的隐马尔科夫频谱预测算法,所提算法的频谱预测时延更短,准确度更高。

关键词: DBSCAN, 时延, 协作频谱预测, 信道相关性, 隐马尔科夫模型

Abstract: Aiming at the problems of long time delay and low prediction accuracy in traditional hidden Markov spectrum prediction,this paper proposed an HMM cooperative spectrum prediction algorithm based on density-based spatial clustering of applications with noise (DBSCAN).BSCAN algorithm is used to cluster the frequency domain channels with strong correlation and predict the channel state in units of clusters,and the prediction delay is reduced by reducing the number of predicted times.At the same time,the method of multiple sub-users cooperative prediction is used in the time domain,and the forecast uncertainty is reduced by fusing the initial prediction results of each subordinate user.Simulation results show thatthe proposed algorithm has shorter spectral delay and higher accuracy compared with the traditional HMM-based local spectrum prediction algorithm and HMM-based packet fusion prediction algorithm.

Key words: Channel correlation, Cooperative spectrum prediction, DBSCAN, Hidden Markov model, Time delay

中图分类号:

P922

吴建伟, 李艳玲, 张辉, 臧翰林. 基于密度聚类的HMM协作频谱预测算法[J]. 计算机科学, 2018, 45(9): 129-134. https://doi.org/10.11896／j.issn.1002-137X.2018.09.020

WU Jian-wei, LI Yan-ling, ZHANG Hui, ZANG Han-lin. HMM Cooperative Spectrum Prediction Algorithm Based on Density Clustering[J]. Computer Science, 2018, 45(9): 129-134. https://doi.org/10.11896／j.issn.1002-137X.2018.09.020

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