Dynamic spectrum allocation in cognitive radio using hidden Markov models: Poisson distributed case
Dynamic spectrum allocation in cognitive radio using hidden Markov models: Poisson distributed case
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DOI：
10.1109/secon.2007.342884
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发表时间：
2007-03
期刊：
影响因子：
--
通讯作者：
I. Akbar;W. Tranter
中科院分区：
文献类型：
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作者：
I. Akbar;W. Tranter
认知无线电网络可以通过利用主用户授权频带中的频谱空洞来更有效地管理无线电频谱。最近的研究表明，即使在城市地理区域，无线电频谱也没有被许可用户充分利用。通过使次级用户（其不被主系统服务）能够接入频谱空洞（spectrum hole），即，未被授权用户使用的频段。在这项新的工作中，我们使用隐马尔可夫模型（HALGOT）来建模和预测许可无线电频段的频谱占用。所提出的技术可以动态地选择不同的授权频带供其自己使用，而来自授权用户的干扰和对授权用户的干扰明显更少。据发现，通过预测主要用户的频谱空洞的持续时间，CR可以更有效地利用它们离开频带，它目前占用，从该频带的主要用户的业务开始之前。我们提出了一个简单的算法，称为马尔可夫信道预测算法（MCPA），在认知无线电网络中的动态频谱分配。在这项工作中，我们提出了我们提出的动态频谱分配算法的性能时，主用户的信道状态占用假设为泊松分布。CR传输对授权用户的影响也被提出。结果表明，显着的SIR改善，可以实现基于HMM的动态频谱分配相比，传统的基于CSMA的方法。使用隐马尔可夫模型得到的结果是非常有前途的，隐马尔可夫模型可以提供一个新的范式预测认知无线电，一个领域，最近一直有很多研究兴趣的信道行为。
Cognitive radio networks can be designed to manage the radio spectrum more efficiently by utilizing the spectrum holes in primary users' licensed frequency bands. Recent studies have shown that the radio spectrum is poorly utilized by the licensed users even in urban geographical areas. This spectrum utilization can be improved significantly by making it possible for secondary users (who are not being served by the primary system) to access spectrum holes, i.e., frequency bands not used by licensed users. In this novel work, we use hidden Markov models (HMMs) to model and predict the spectrum occupancy of licensed radio bands. The proposed technique can dynamically select different licensed bands for its own use with significantly less interference from and to the licensed users. It is found that by predicting the duration of spectrum holes of primary users, the CR can utilize them more efficiently by leaving the band, that it currently occupies, before the start of traffic from the primary user of that band. We propose a simple algorithm, called the Markov-based channel prediction algorithm (MCPA), for dynamic spectrum allocation in cognitive radio networks. In this work, we present the performance of our proposed dynamic spectrum allocation algorithm when the channel state occupancy of primary users are assumed to be Poisson distributed. The impact of CR transmission on the licensed users is also presented. It is shown that significant SIR improvements can be achieved using HMM based dynamic spectrum allocation as compared to the traditional CSMA based approach. The results obtained using HMM are very promising and HMM can offer a new paradigm for predicting channel behavior in cognitive radio, an area that has been of much research interest lately.