基于隐马尔可夫模型的短波认知频率选择方法

High frequency cognitive frequency selection mechanism based on hidden Markov model

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。

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.