计算机应用 ›› 2016, Vol. 36 ›› Issue (5): 1179-1182.DOI: 10.11772/j.issn.1001-9081.2016.05.1179

• 网络与通信 •    下一篇

基于隐马尔可夫模型的短波认知频率选择方法

王董礼1, 曹鹏1, 黄国策1, 孙启禄1, 李连宝2   

  1. 1. 空军工程大学 信息与导航学院, 西安 710077;
    2. 93756部队 电子教研室, 天津 300270
  • 收稿日期:2015-11-05 修回日期:2016-01-10 出版日期:2016-05-10 发布日期:2016-05-09
  • 通讯作者: 王董礼
  • 作者简介:王董礼(1992-),男,河南项城人,硕士研究生,主要研究方向:短波认知无线电;曹鹏(1982-),男,湖南衡阳人,讲师,博士,主要研究方向:短波IP网络、短波宽带通信、短波认知无线电;黄国策(1962-),男,陕西高陵人,教授,博士生导师,主要研究方向:短波通信、卫星通信、军事通信组网;孙启禄(1977-),男,山东泰安人,讲师,博士,主要研究方向:短波通信组网、卫星通信;李连宝(1983-),男,天津大港人,讲师,博士,主要研究方向:短波装备运用。
  • 基金资助:
    中国博士后科学基金资助项目(2013M532220)。

High frequency cognitive frequency selection mechanism based on hidden Markov model

WANG Dongli1, CAO Peng1, HUANG Guoce1, SUN Qilu1, LI Lianbao2   

  1. 1. School of Information and Navigation, Air Force Engineering University, Xi'an Shaanxi 710077, China;
    2. Department of Electronics, Unit 93756 of PLA, Tianjin 300270, China
  • Received:2015-11-05 Revised:2016-01-10 Online:2016-05-10 Published:2016-05-09
  • Supported by:
    This work is supported by the China Postdoctoral Science Foundation (2013M532220).

摘要: 针对短波频谱利用率低下及频率选择不够智能的局限性,提出一种基于隐马尔可夫模型(HMM)的短波认知频率选择方法。应用认知无线电原理,将短波传统用户作为主用户,将采用认知无线电技术的短波电台作为认知用户。首先,建立隐马尔可夫模型,结合频谱感知历史数据预测主用户信道状态;其次,在预测空闲的基础上估计信道参数;最后,根据估计的信道参数选择最优频率。仿真结果表明,所提方法能够准确预测传统短波用户信道状态,快速估计信道参数。在设定的仿真条件下,所提方法的成功传输率分别较HMM预测和能量感知随机信道选择方法有5.54%和10.56%的提升,能够选择最优信道。

关键词: 短波认知通信, 隐马尔可夫模型, 信道状态预测, 参数估计, 频率选择

Abstract: Since the limitation of inefficient use and unintelligent frequency selection of the HF (High Frequency) band, a method of HF cognitive frequency selection using Hidden Markov Model (HMM) was proposed. Applying cognitive radio principles to HF communications, HF legacy users were considered as primary users, and the HF radio using cognitive technologies were seen as the secondary user. Firstly, the HMM was established to predict channel states of HF legacy users based on the history data of spectrum sensing; secondly, channel parameters were estimated if the predicted state was idle; finally, the optimal frequency was selected among the channels whose predicted states were idle according to the estimated channel parameters. Simulation results show that the proposed method can be used to actually predict HF legacy users' channel states and quickly estimate channel parameters. Under the given simulation conditions, the successful transmission ratio of the proposed method is 5.54% and 10.56% higher than the methods of random channel selection using HMM prediction and energy detection, therefore the proposed method can select the optimal channel.

Key words: High Frequency (HF) cognitive communication, Hidden Markov Model (HMM), channel state prediction, parameter estimation, frequency selection

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