Linyuan Zhang

Robust Spectrum Sensing With Crowd Sensors

This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where the sensing data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute with abnormal data, which makes the existing cooperative sensing schemes ineffective. To tackle these challenges, we first propose a generalized modeling approach for sensing data with an arbitrary abnormal component. Under this model, we then analyze the impact of general abnormal data on the performance of the cooperative sensing, by deriving closed-form expressions of the probabilities of global false alarm and global detection. To improve sensing data quality and enhance cooperative sensing performance, we further formulate an optimization problem as stable principal component pursuit, and develop a data cleansing-based robust spectrum sensing algorithm to solve it, where the under-utilization of licensed spectrum bands and the sparsity of nonzero abnormal data are jointly exploited to robustly cleanse out the potential nonzero abnormal data component from the original corrupted sensing data. Extensive simulation results demonstrate that the proposed robust sensing scheme performs well under various abnormal data parameter configurations.

Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). Over the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Then, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we analyze the spear-and-shield relation between Byzantine attack and defense from an interactive game-theoretical perspective. Moreover, we highlight the unsolved research challenges and depict the future research directions.

Performance analysis of probabilistic soft SSDF attack in cooperative spectrum sensing

In cognitive radio networks, spectrum sensing data falsification (SSDF) attack is a crucial factor deteriorating the detection performance of cooperative spectrum sensing. In this paper, we propose and analyze a novel probabilistic soft SSDF attack model, which goes beyond the existing models for its generalization. Under this generalized SSDF attack model, we firstly obtain closed form expressions of global sensing performance at the fusion center. Then, we theoretically evaluate the performance of the proposed attack model, in terms of destructiveness and stealthiness, sequentially. Numerical simulations match the analytical results well. Last but not least, an interesting trade-off between destructiveness and stealthiness is discovered, which is a fundamental issue involved in SSDF attack, however, ignored by most of the previous studies.

Defending against Byzantine attack in cooperative spectrum sensing relying on a reliable reference

This paper considers countermeasures against Byzantine attack, also known as spectrum sensing data falsification (SSDF) attack, which poses huge threats on the reliability of cooperative spectrum sensing (CSS). Due to lack of the ground-truth spectrum state, a reliable defense reference is vital to identify malicious behaviors and perform effective data fusion. Motivated by the fact that the existing references have strong assumptions such as the attackers are in minority or a trust node exists for data fusion, this paper proposes a novel defense reference which jointly exploits the cognitive process of spectrum sensing and spectrum access in a closed-loop manner, to provide the defense scheme with a solid basis. Then, based on the proposed reference, we design an optimal cooperative spectrum sensing scheme. Furthermore, numerical simulations verify the proposed schemes favorable performance, even in critical cases that malicious sensors are in majority.

Robust Spectrum Sensing with Crowd Sensors

This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where one critical challenge is the uncertainty of the quality of sensing data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute abnormal data, which makes the existing defense schemes ineffective. To tackle these unique challenges, we propose a robust spectrum sensing scheme by developing a data cleansing framework, where the underutilization of licensed spectrum bands and the sparsity of nonzero abnormal data are jointly exploited to robustly cleanse out the potential nonzero abnormal data component from the original corrupted sensing data. Simulation results demonstrate that the proposed robust sensing scheme outperforms the state-of-art schemes under various abnormal data parameter configurations.

Coexistence among Device-to-Device communications in TV white space based on geolocation database

Device-to-Device (D2D) communication as a resource reuse technique underlaying TV white space (TVWS) can bring significant potential benefits. In this paper, we address the coexistence between primary TV receiver and multiple D2D links as well as coexistence among different D2D links in a cell. Relying on geo-location database, the access point coordinates channel allocation for D2D links and regulate power level of devices, aiming to realize coexistence goal. Firstly, we formulate the coexistence problem under the constraint of aggregate interference model, including co-channel and adjacent channel interference in the analysis. Then according to the formulated coexistence problem we propose an algorithm, as a result the algorithm could find the channel assignment policy and power control scheme for each D2D link. Further, we compare the proposed coexistence scheme with the state-of-the-art method, it is shown that our proposed algorithm outperforms in terms of total throughput. In addition, we quantitatively analyze the parameters closely affect the throughput performance of the cell, which shows a guideline of improvement in coexistence.

Spectrum Sensing Under Spectrum Misuse Behaviors: A Multi-Hypothesis Test Perspective

Spectrum misuse behaviors, brought either by illegitimate access or by rogue power emission, endanger the legitimate communication and deteriorate the spectrum usage environment. In this paper, our aim is to detect whether the spectrum band is occupied, and if it is occupied, recognize whether the misuse behavior exists. One vital challenge is that the legitimate spectrum exploitation and misuse behaviors probabilistically coexist and the illegitimate user may act in an intermittent and fast-changing manner, which brings about much uncertainty for spectrum sensing. To tackle it, we first formulate the spectrum sensing problems under illegitimate access and rogue power emission as a uniform ternary hypothesis test. Then, we develop a novel test criterion, named the generalized multi-hypothesis Neyman–Pearson (GMNP) criterion. Following the criterion, we derive two test rules based on the generalized likelihood ratio test and the Rao test, respectively, whose asymptotic performances are analyzed and an upper bound is also given. Furthermore, a cooperative spectrum sensing scheme is designed based on the global GMNP criterion to further improve the detection performances. In addition, extensive simulations are provided to verify the proposed schemes’ performance under various parameter configurations.