A Neural Network Based Spectrum Prediction Scheme for Cognitive Radio
A Neural Network Based Spectrum Prediction Scheme for Cognitive Radio
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DOI：
10.1109/icc.2010.5502348
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发表时间：
2010-05
期刊：
影响因子：
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通讯作者：
Vamsi Krishna Tumuluru;Ping Wang;D. Niyato
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文献类型：
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作者：
Vamsi Krishna Tumuluru;Ping Wang;D. Niyato
摘要：认知无线电（CR）技术使未授权用户能够在无干扰的基础上与授权用户共享频谱。频谱感知是非授权用户确定授权用户频谱中信道可用性的重要功能。然而，频谱感测消耗相当大的能量，这可以通过采用用于发现频谱空洞的预测方法来减少。使用可靠的预测方案，未授权用户将仅感测被预测为空闲的那些信道。通过在预测空闲信道中实现低的错误概率，还可以提高频谱利用率。由于在真实的生活中遇到的大多数授权用户系统的流量特性是不知道的先验，我们设计的频谱预测器使用的神经网络模型，多层感知器（MLP），它不需要一个先验知识的授权用户系统的流量特性。通过大量的仿真分析了谱预测器的性能。
Abstract-The Cognitive Radio (CR) technology enables the unlicensed users to share the spectrum with the licensed users on a non-interfering basis. Spectrum sensing is an important function for the unlicensed users to determine availability of a channel in the licensed user's spectrum. However, spectrum sensing consumes considerable energy which can be reduced by employing predictive methods for discovering spectrum holes. Using a reliable prediction scheme, the unlicensed users will sense only those channels which are predicted to be idle. By achieving a low probability of error in predicting the idle channels, the spectrum utilization can also be improved. Since the traffic characteristics of most licensed user systems encountered in real life are not known a priori, we design the spectrum predictor using the neural network model, multilayer perceptron (MLP), which does not require a prior knowledge of the traffic characteristics of the licensed user systems. The performance of the spectrum predictor is analyzed through extensive simulations.