Block Outlier Methods for Malicious User Detection in Cooperative Spectrum Sensing

Block outlier detection methods, based on Tietjen-Moore (TM) and Shapiro-Wilk (SW) tests, are proposed to detect and suppress spectrum sensing data falsification (SSDF) attacks by malicious users in cooperative spectrum sensing. First, we consider basic and statistical SSDF attacks, where the malicious users attack independently. Then we propose a new SSDF attack, which involves cooperation among malicious users by masking. In practice, the number of malicious users is unknown. Thus, it is necessary to estimate the number of malicious users, which is found using clustering and largest gap method. However, we show using Monte Carlo simulations that, these methods fail to estimate the exact number of malicious users when they cooperate. To overcome this, we propose a modified largest gap method.Comment: Accepted in Proceedings of 79th IEEE Vehicular Technology Conference-Spring (VTC-Spring), May 2014, Seoul, South Kore

Novel channel sensing and access strategies in opportunistic spectrum access networks

textTraditionally radio spectrum was considered a commodity to be allocated in a fixed and centralized manner, but now the technical community and the regulators approach it as a shared resource that can be flexibly and intelligently shared between competing entities. In this thesis we focus on novel strategies to sense and access the radio spectrum within the framework of Opportunistic Spectrum Access via Cognitive Radio Networks (CRNs). In the first part we develop novel transmit opportunity detection methods that effectively exploit the gray space present in packet based networks. Our methods proactively detect the maximum safe transmit power that does not significantly affect the primary network nodes via an implicit feedback mechanism from the Primary network to the Secondary network. A novel use of packet interarrival duration is developed to robustly perform change detection in the primary network's Quality of Service. The methods are validated on real world IEEE 802.11 WLANs. In the second part we study the inferential use of Goodness-of-Fit tests for spectrum sensing applications. We provide the first comprehensive framework for decision fusion of an ensemble of goodness-of-fit tests through use of p-values. Also, we introduce a generalized Phi-divergence statistic to formulate goodness-of-fit tests that are tunable via a single parameter. We show that under uncertainty in the noise statistics or non-Gaussianity in the noise, the performance of such non-parametric tests is significantly superior to that of conventional spectrum sensing methods. Additionally, we describe a collaborative spatially separated version of the test for robust combining of tests in a distributed spectrum sensing setting. In the third part we develop the sequential energy detection problem for spectrum sensing and formulate a novel Sequential Energy Detector. Through extensive simulations we demonstrate that our doubly hierarchical sequential testing architecture delivers a significant throughput improvement of 2 to 6 times over the fixed sample size test while maintaining equivalent operating characteristics as measured by the Probabilities of Detection and False Alarm. We also demonstrate the throughput gains for a case study of sensing ATSC television signals in IEEE 802.22 systems.Electrical and Computer Engineerin

A statistical approach to spectrum sensing using bayes factor and p-Values

The sensing methods with multiple receive antennas  in the Cognitive Radio (CR) device, provide a promising solution for reducing the error rates in the detection of the Primary User (PU) signal. The received Signal to Noise Ratio at the CR receiver is enhanced using the diversity combiners. This paper proposes a statistical approach based on minimum Bayes factors and p-Values as diversity combiners in the spectrum sensing scenario. The effect of these statistical measures in sensing the spectrum in a CR environment is investigated. Through extensive Monte Carlo simulations it is shown that this novel statistical approach based on Bayes factors provides a promising solution to combine the test statistics from multiple receiver antennas and can be used as an alternative to the conventional hypothesis testing methods for spectrum sensing. The Bayesian results provide more accurate results when measuring the strength of the evidence against the hypothesis

Communication Subsystems for Emerging Wireless Technologies

The paper describes a multi-disciplinary design of modern communication systems. The design starts with the analysis of a system in order to define requirements on its individual components. The design exploits proper models of communication channels to adapt the systems to expected transmission conditions. Input filtering of signals both in the frequency domain and in the spatial domain is ensured by a properly designed antenna. Further signal processing (amplification and further filtering) is done by electronics circuits. Finally, signal processing techniques are applied to yield information about current properties of frequency spectrum and to distribute the transmission over free subcarrier channels

NOVEL RECEIVER DIVERSITY COMBINING METHODS FOR SPECTRUM SENSING USING META-ANALYTIC APPROACH BASED ON P-VALUES

The need for efficient spectrum utilization with reduced error rates has brought a paradigm shift in wireless communication systems from a Single Input and Single Output (SISO) systems to Multiple Input Multiple Output (MIMO) systems. Conventional diversity combiners are used to boost the received Signal to Noise Ratio at the Cognitive Radio receiver. However, these methods require perfect estimation of the channel. This paper proposes a Meta-Analytic approach based on p-Values for combining the data received from a secondary user equipped with multiple antennas. The effect of the p-Value method as receiver diversity combiner is studied and is compared with the existing non-coherent combining schemes, which do not need channel state information. The weighted Z test and Fisher’s method are used to combine the p-Values derived from the Anderson Darling (AD) and Jarque Bera (JB) test statistics. A ballpark figure of the merits of these diversity combining methods are provided in this study. Through extensive Monte Carlo simulations, it is shown that the weighted Z test using the Anderson Darling test statistic provides a probability of detection very close to the existing non-coherent diversity combiners. Hence, this novel statistical approach based on p-Values provides a promising solution to combine the test statistics from multiple receiver antennas

Deep Stacked CNN-LSTM (DS-CNN-LSTM) based Spectrum Sensing in Cognitive Radio

The multidimensionality of spectrum sensing, the intrinsic complexity of its dependence, and the unpredictability associated with spectrum data all contribute to the difficulty of the task. The network of cognitive radio (CR) is comprised of both primary and secondary users inside its network. The SUs that are part of the CR network are able to identify the spectrum band and access white space in an opportunistic manner. Enhancing spectrum efficiency may be accomplished by using white spaces. This study presents a Deep Stacked CNN-LSTM (DS-CNN-LSTM)-based spectrum sensing strategy that learns implicit features from spectrum data, such as temporal correlation. This approach is based on the research that we have conducted. The effectiveness of the recommended method is shown by a sufficient number of simulations, and the results of the simulations demonstrate that it outperforms the current state of the art in terms of detection probability and classification accuracy. A comparison is made between the most cutting-edge spectrum sensing approaches and the DS-CNN-LSTM method that has been recommended. The results of the experiments indicate that the proposed methods improve detection performance and classification accuracy even when the signal-to-noise ratio is low. As we can see, the improvement that was achieved comes at the price of a longer amount of time spent on training and a little increase in the amount of time spent on execution