The proliferation of the Internet of Things (IoTs) and pervasive use of many different types of mobile computing devices make wireless communication spectrum a precious resource. In order to accommodate the still fast increasing number of devices requesting wireless connection, more efficient, fine-grained spectrum allocation and sharing schemes are badly in need. Cognitive radio networks (CRNs) have been widely recognized as one promising solution, in which the secondary users (SUs) are allowed to share channels with licensed primary users (PUs) as long as bringing no interference to the normal operations of the PUs. However, malicious attackers or selfish SUs may mimic the behavior of PUs to occupy the channels illegally. It is nontrivial to accurately, timely detect such kind of primary user emulation (PUE) attacks. In this paper, an efficient PUE attacked detection method is introduced leveraging the recurrent neural network (RNN). After a fundamental algorithm using basic RNN, an advanced version taking advantage of long-short-term-memory (LSTM) is proposed, which is more efficient on processing time series with long term memory. The experimental study has provided deeper insights about the different performances the RNNs achieved and validated the effectiveness of the proposed detectors.
It is proven by many spectrum measurement campaigns around the world that the spectrum occupancy of PU channels shows noticeable statistic features, which can be described by some statistical models @cite_30 . Figure shows a rational and practical way for spectrum sensing, which allows CRs measure a channel's state during a short observation time @math and wait for a revisiting time @math until next spectrum sensing action. Based on such an intermittent spectrum sensing manner, there are two ways to improve the efficiency of CRNs. The CRs can either save energy during @math period of sleep, or they are able to perform wideband spectrum sensing by sensing other bands during @math period of scanning. However, because CRs only collect intermittent channel information, many signal patterns become untraceable. It is more advantageous for malicious parties to apply various attacks, especially the PUE attacks.
@cite_30
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Spectrum occupancy models are very useful in cognitive radio designs. They can be used to increase spectrum sensing accuracy for more reliable operation, to remove spectrum sensing for higher resource usage efficiency, or to select channels for better opportunistic access, among other applications. In this survey, various spectrum occupancy models from measurement campaigns taken around the world are investigated. These models extract different statistical properties of the spectrum occupancy from the measured data. In addition to these models, spectrum occupancy prediction is also discussed, where autoregressive and or moving-average models are used to predict the channel status at future time instants. After comparing these different methods and models, several challenges are also summarized based on this survey.