Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter

Cognitive radio technology addresses the problem of spectrum scarcity by allowing secondary users to use the vacant spectrum bands without causing interference to the primary users. However, several attacks could disturb the normal functioning of the cognitive radio network. Primary user emulation attacks are one of the most severe attacks in which a malicious user emulates the primary user signal characteristics to either prevent other legitimate secondary users from accessing the idle channels or causing harmful interference to the primary users. There are several proposed approaches to detect the primary user emulation attackers. However, most of these techniques assume that the primary user location is fixed, which does not make them valid when the primary user is mobile. In this paper, we propose a new approach based on the Kalman filter framework for detecting the primary user emulation attacks with a non-stationary primary user. Several experiments have been conducted and the advantages of the proposed approach are demonstrated through the simulation results.Comment: 14 pages, 9 figure

A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

Jamming attacks target a wireless network creating an unwanted denial of service. 5G is vulnerable to these attacks despite its resilience prompted by the use of millimeter wave bands. Over the last decade, several types of jamming detection techniques have been proposed, including fuzzy logic, game theory, channel surfing, and time series. Most of these techniques are inefficient in detecting smart jammers. Thus, there is a great need for efficient and fast jamming detection techniques with high accuracy. In this paper, we compare the efficiency of several machine learning models in detecting jamming signals. We investigated the types of signal features that identify jamming signals, and generated a large dataset using these parameters. Using this dataset, the machine learning algorithms were trained, evaluated, and tested. These algorithms are random forest, support vector machine, and neural network. The performance of these algorithms was evaluated and compared using the probability of detection, probability of false alarm, probability of miss detection, and accuracy. The simulation results show that jamming detection based random forest algorithm can detect jammers with a high accuracy, high detection probability and low probability of false alarm

Exploration Of Deep Learning Models For Maximum Throughput To Hybrid Beamforming And Compressive Channel Estimation

The overall objective of this dissertation is to improve the capacity of 5G and Beyond systems by improving spectral efficiency, efficiently managing interference, and addressing the radio spectrum’s poor management while enhancing cost, energy, and computation efficiencies. The first contribution of this dissertation consists of designing effective hybrid beamforming solutions using the theory of deep reinforcement learning. The results demonstrate that deep reinforcement learning achieves near-optimal spectral efficiency and provides autonomous decision-making that can learn from interacting with the wireless environment. This method enhances hybrid beamforming computational efficiency, hardware efficiency, and energy efficiency. These methods are also desirable in scenarios where channel conditions change too fast, and we may not have existing channel datasets or the corresponding optimal beamforming solutions required for supervised learning. The second contribution of this dissertation is channel estimation in a hybrid architecture. Our numerical results demonstrated that compressive sensing could be leveraged to estimate the channel in hybrid architecture from a few training samples. The sensing matrix can be optimized, and the high dimensional channel can also be estimated using very few training samples. Our results revealed that orthogonal matching pursuit enables compressive channel estimation in low signal-to-noise ratio settings. We explored deep learning and deep reinforcement learning methods for channel estimation to design an end-to-end channel estimation where the model can map the training pilots directly to the channel estimate. The third contribution of this dissertation discusses the efficient design of jamming detection and mitigation algorithms. We developed jamming detection methods based on boosting decision trees. Our results indicated that boosting techniques achieve adequate detection performance and can be trained in a short time. The last contribution of this dissertation consists of developing narrow band and wideband spectrum sensing techniques based on machine learning and compressive sensing, respectively. We demonstrated that Bayesian compressive sensing with a Toeplitz measurement matrix could sense the activity of the primary users with high probabilities of detection while reducing the number of acquired samples, resulting in a reduction of the sensing time and complexity of the algorithm

Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach

Sensing the wideband spectrum is an important process for next-generation wireless communication systems. Spectrum sensing primarily aims at detecting unused spectrum holes over wide frequency bands so that secondary users can use them to meet their requirements in terms of quality-of-service. However, this sensing process requires a great deal of time, which is not acceptable for timely communications. In addition, the sensing measurements are often affected by uncertainty. In this paper, we propose an approach based on Bayesian compressive sensing to speed up the process of sensing and to handle uncertainty. This approach takes only a few measurements using a Toeplitz matrix, recovers the wideband signal from a few measurements using Bayesian compressive sensing via fast Laplace prior, and detects either the presence or absence of the primary user using an autocorrelation-based detection method. The proposed approach was implemented using GNU Radio software and Universal Software Radio Peripheral units and was tested on real-world signals. The results show that the proposed approach speeds up the sensing process by minimizing the number of samples while achieving the same performance as Nyquist-based sensing techniques regarding both the probabilities of detection and false alarm

Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach

Sensing the wideband spectrum is an important process for next-generation wireless communication systems. Spectrum sensing primarily aims at detecting unused spectrum holes over wide frequency bands so that secondary users can use them to meet their requirements in terms of quality-of-service. However, this sensing process requires a great deal of time, which is not acceptable for timely communications. In addition, the sensing measurements are often affected by uncertainty. In this paper, we propose an approach based on Bayesian compressive sensing to speed up the process of sensing and to handle uncertainty. This approach takes only a few measurements using a Toeplitz matrix, recovers the wideband signal from a few measurements using Bayesian compressive sensing via fast Laplace prior, and detects either the presence or absence of the primary user using an autocorrelation-based detection method. The proposed approach was implemented using GNU Radio software and Universal Software Radio Peripheral units and was tested on real-world signals. The results show that the proposed approach speeds up the sensing process by minimizing the number of samples while achieving the same performance as Nyquist-based sensing techniques regarding both the probabilities of detection and false alarm

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

Cognitive radio technology has the potential to address the shortage of available radio spectrum by enabling dynamic spectrum access. Since its introduction, researchers have been working on enabling this innovative technology in managing the radio spectrum. As a result, this research field has been progressing at a rapid pace and significant advances have been made. To help researchers stay abreast of these advances, surveys and tutorial papers are strongly needed. Therefore, in this paper, we aimed to provide an in-depth survey on the most recent advances in spectrum sensing, covering its development from its inception to its current state and beyond. In addition, we highlight the efficiency and limitations of both narrowband and wideband spectrum sensing techniques as well as the challenges involved in their implementation. TV white spaces are also discussed in this paper as the first real application of cognitive radio. Last but by no means least, we discuss future research directions. This survey paper was designed in a way to help new researchers in the field to become familiar with the concepts of spectrum sensing, compressive sensing, and machine learning, all of which are the enabling technologies of the future networks, yet to help researchers further improve the efficiently of spectrum sensing