基于隐马尔可夫模型和状态持久性的动态频谱检测

本文针对复杂无线通信环境中的动态频谱接入进行了研究，提出了一种基于隐马尔可夫模型和状态持久性的动态频谱检测方案。具体来说，提出的方案在能量窗口检测的基础上，首先将每个主用户随时间变化的信号能量表示为一个随机过程，然后利用隐马尔可夫模型和状态持久性的概念设计出了2种检测器来检测这些主用户和二级用户之间的差异，并尝试根据它们的统计特征来区分信号，从而提高可用空白频谱的检测精度和它们的动态频谱接入能力；仿真实验结果表明，本文提出的方案不仅可区分复杂无线通信环境中的传输源，而且还可提高动态频谱检测的性能。

Dynamic Spectrum Detection Based on Hidden Markov Model and State Persistence

In this paper, dynamic spectrum access in complex wireless communication environment is studied, and a dynamic spectrum detection scheme based on HMM and state persistence is proposed. Specifically, on the basis of energy window detection, the proposed scheme first represents the signal energy of each primary user changing with time as a random process. Secondly, two detectors are designed to detect the differences between the primary and secondary users by using HMM and the concept of state persistence,and try to distinguish the signals according to their statistical characteristics, so as to improve the detection accuracy of available white spaces and their dynamic spectrum access ability. The simulation results show that the proposed scheme can not only distinguish transmission sources in complex wireless communication environment, but also improve the performance of dynamic spectrum detection.