Multi-parameter cognition in a cognitive radio network (CRN) provides a more thorough understanding of the radio environments, and could potentially lead to far more intelligent and efficient spectrum usage for a secondary user. In this paper, we investigate the multi-parameter cognition problem for a CRN where the primary transmitter (PT) radiates multiple transmit power levels, and propose a learning-based two-stage spectrum sharing strategy. We first propose a data-driven machine learning based multi-level spectrum sensing scheme, including the spectrum learning (Stage I) and prediction (the first part in Stage II). This fully blind sensing scheme does not require any prior knowledge of the PT power characteristics. Then, based on a novel normalized power level alignment metric, we propose two prediction-transmission structures, namely periodic and non-periodic, for spectrum access (the second part in Stage II), which enable the secondary transmitter (ST) to closely follow the PT power level variation. The periodic structure features a fixed prediction interval, while the non-periodic one dynamically determines the interval with a proposed reinforcement learning algorithm to further improve the alignment metric. Finally, we extend the prediction-transmission structure to an online scenario, where the number of PT power levels might change as a consequence of PT adapting to the environment fluctuation or quality of service variation. The simulation results demonstrate the effectiveness of the proposed strategy in various scenarios.
Machine learning technology has recently played an important role in improving spectrum sensing. The work in @cite_10 presented an adversarial machine learning approach to launch jamming attacks on CR and introduces a defense strategy. Several supervised and unsupervised machine learning algorithms for cooperative spectrum sensing (CSS) were investigated in @cite_30 . In @cite_26 , the combination of infinite GMM and CSS was proposed to detect the primary user emulation attacks. In @cite_44 , a convolutional neural network-based CSS scheme was developed to detect multiple bands simultaneously. A mobile CSS framework was proposed in @cite_8 for large-scale heterogeneous cognitive networks.
@cite_30 @cite_26 @cite_8 @cite_44 @cite_10
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In this paper, we investigate cooperative spectrum sensing (CSS) in a cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in order to detect a primary user, which possibly occupies multiple bands simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS framework based on a convolutional neural network (CNN), is proposed. In DCS, instead of the explicit mathematical modeling of CSS, the strategy for combining the individual sensing results of the SUs is learned autonomously with a CNN using training sensing samples regardless of whether the individual sensing results are quantized or not. Moreover, both spectral and spatial correlation of individual sensing outcomes are taken into account such that an environment-specific CSS is enabled in DCS. Through simulations, we show that the performance of CSS can be greatly improved by the proposed DCS. We propose novel cooperative spectrum sensing (CSS) algorithms for cognitive radio (CR) networks based on machine learning techniques which are used for pattern classification. In this regard, unsupervised (e.g., K-means clustering and Gaussian mixture model (GMM)) and supervised (e.g., support vector machine (SVM) and weighted K-nearest-neighbor (KNN)) learning-based classification techniques are implemented for CSS. For a radio channel, the vector of the energy levels estimated at CR devices is treated as a feature vector and fed into a classifier to decide whether the channel is available or not. The classifier categorizes each feature vector into either of the two classes, namely, the "channel available class" and the "channel unavailable class". Prior to the online classification, the classifier needs to go through a training phase. For classification, the K-means clustering algorithm partitions the training feature vectors into K clusters, where each cluster corresponds to a combined state of primary users (PUs) and then the classifier determines the class the test energy vector belongs to. The GMM obtains a mixture of Gaussian density functions that well describes the training feature vectors. In the case of the SVM, the support vectors (i.e., a subset of training vectors which fully specify the decision function) are obtained by maximizing the margin between the separating hyperplane and the training feature vectors. Furthermore, the weighted KNN classification technique is proposed for CSS for which the weight of each feature vector is calculated by evaluating the area under the receiver operating characteristic (ROC) curve of that feature vector. The performance of each classification technique is quantified in terms of the average training time, the sample classification delay, and the ROC curve. Our comparative results clearly reveal that the proposed algorithms outperform the existing state-of-the-art CSS techniques. Abstract Cooperative spectrum sensing (CSS) is an effective approach to improve the detection performance in cognitive radio (CR) networks. The CSS process imposes some additional security threats to the CR networks. One of the common threats is primary user emulation attack (PUEA). In this particular type of attack, some malicious users try to imitate primary signal characteristics and defraud CR users to prevent them from accessing the idle frequency bands. Here, we propose a novel cooperative sensing scheme with attack-aware capability in the presence of a malicious PUEA. More precisely, the probabilities of the presence of a PUEA fake signal in both presence and absence of the licensed PU signal are estimated. Then, the obtained parameters are used to determine the optimal threshold that minimizes the total error probability. Simulation results are presented to indicate the performance improvement of the proposed method against PUEA compared with the conventional method. In this paper, we investigate cooperative spectrum sensing (CSS) in a cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in order to detect a primary user (PU) which possibly occupies multiple bands simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS framework based on a convolutional neural network (CNN), is proposed. In DCS, instead of the explicit mathematical modeling of CSS which is hard to compute and also hard to use in practice, the strategy for combining the individual sensing results of the SUs is learned with a CNN using training sensing samples. Accordingly, an environment-specific CSS which considers both spectral and spatial correlation of individual sensing outcomes, is found in an adaptive manner regardless of whether the individual sensing results are quantized or not. Through simulation, we show that the performance of CSS can be improved by the proposed DCS with low complexity even when the number of training samples is moderate.