基于隐马尔可夫模型的联合概率信道预测动态认知无线电频谱接入

在基于固定检测周期的认知无线电频谱接入技术中，为了在降低认知用户对授权用户干扰的同时，尽可能地提高认知用户的频谱利用率，提出一种基于隐马尔可夫模型(hidden Markov model,HMM)的联合概率信道预测(joint probability channel prediction,JPCP)的动态认知无线电频谱接入技术.该技术以固定检测周期(fixed sensing period,FSP)为基础，若认知用户已占用授权频段，则利用JPCP值与设定阈值的比较来确定是否提前退出信道的机制，以降低认知用户对授权用户的干扰；若认知用户未能占用授权频段，则利用在二次固定检测周期中增加临时的频谱空穴检测机制来提高认知用户的频谱利用率.仿真结果显示，与传统的固定检测周期技术相比，该技术方案能在降低认知用户对授权用户干扰的同时，有效提高认知用户的频谱利用率.

Hidden Markov Model-Based Joint Probability Channel Prediction Dynamic Spectrum Access in Cognitive Radio

To reduce interference from cognitive users with licensed users,and raise spectrum efficiency as much as possible in cognitive radio spectrum access based on fixed sensing period(FSP),a new technique called dynamic spectrum access using joint probability channel prediction(JPCP) based on hidden Markov model(HMM) is proposed.After cognitive users have occupied a licensed spectrum,whether it should quit the channel can be determined by comparing the JPCP value with the threshold defined to reduce interference...