You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: 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.

[2]: 

[3]: Spectrum sensing in a large-scale heterogeneous network is very challenging as it usually requires a large number of static secondary users (SUs) to obtain the global spectrum states. To tackle this problem, in this paper, we propose a new framework based on Bayesian machine learning. We exploit the mobility of multiple SUs to simultaneously collect spectrum sensing data and cooperatively derive the global spectrum states. We first develop a novel non-parametric Bayesian learning model, referred to as beta process (BP) sticky hidden Markov model (SHMM), to capture the spatial–temporal correlation in the collected spectrum data, where the SHMM models the latent statistical correlation within each mobile SU's time series data, while the BP realizes the cooperation among multiple SUs. Bayesian inference is then carried out to automatically infer the heterogeneous spectrum states. Based on the inference results, we also develop a new algorithm with a refinement mechanism to predict the spectrum availability, which enables a newly joining SU to immediately access the unoccupied frequency band without sensing. Simulation results show that the proposed framework can significantly improve spectrum sensing performance compared with the existing spectrum sensing techniques.

[4]: 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.

[5]: This paper presents an adversarial machine learning approach to launch jamming attacks on wireless communications and introduces a defense strategy. In a cognitive radio network, a transmitter senses channels, identifies spectrum opportunities, and transmits data to its receiver in idle channels. On the other hand, an attacker may also sense channels, identify busy channels and aim to jam transmissions of legitimate users. In a dynamic system with complex channel, traffic and interference characteristics, the transmitter applies some pre-trained machine learning algorithm to classify a channel as idle or busy. This classifier is unknown to the attacker that senses a channel, captures the transmitter's decisions by tracking the acknowledgments and applies deep learning (in form of an exploratory attack, i.e., inference attack) to build a classifier that is functionally equivalent to the one at the transmitter. This approach is shown to support the attacker to reliably predict successful transmissions based on the sensing results and effectively jam these transmissions. Then, a defense scheme is developed against adversarial deep learning by exploiting the sensitivity of deep learning to training errors. The transmitter deliberately takes a small number of wrong actions (in form of a causative attack, i.e., poisoning attack, launched against the attacker) when it accesses the spectrum. The objective is to prevent the attacker from building a reliable classifier. For that purpose, the attacker systematically selects when to take wrong actions to balance the conflicting effects of deceiving the attacker and making correct transmission decisions. This defense scheme successfully fools the attacker into making prediction errors and allows the transmitter to sustain its performance against intelligent jamming attacks.
Machine learning technology has recently played an important role in improving spectrum sensing. The work in [5] 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 [1] . In [2] , the combination of infinite GMM and CSS was proposed to detect the primary user emulation attacks. In [4] , a convolutional neural network-based CSS scheme was developed to detect multiple bands simultaneously. A mobile CSS framework was proposed in [3] for large-scale heterogeneous cognitive networks.