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Guoqiang Zheng

Publications and source records attributed to Guoqiang Zheng.

5 recordsLinked to original sources

Byzantine Attacks in RIS-Enhanced Cooperative Spectrum Sensing: A Decision Fusion Perspective

From the perspective of hard decision fusion, we investigate Byzantine attacks in Reconfigurable Intelligent Surface (RIS)-enhanced and decode-and-forward relay-assisted Cooperative Spectrum Sensing (CSS) for mobile Cognitive Radio Networks (CRNs) in this paper. Specially, a RIS-enhanced and decode-and-forward relay-assisted CSS configuration is first constructed under dynamic channel scenarios due to user mobility. Subsequently, the channel- and attack-aware hard decision fusion rules are developed, and the optimal channel-aware Byzantine attack strategies are then developed under both small-scale and large-scale attacking scenarios. The corresponding results depict that the optimal attack strategy does not require any a prior knowledge of the global instantaneous Channel State Information (ICSI) (e.g. false alarm probability and detection probability of all the secondary users), although perfect acquisition of ICSI is clearly always not affordable from the attacker perspective, which is further exacerbated by the RIS and decode-and-forward relays involved in CSS and the potential high mobility of secondary users that leads to fast fading channels. Furthermore, our counterintuitive results also indicate that, regardless of the attacker's awareness of the decision fusion rule, the optimal Byzantine attack can be achieved through a unifying framework, the explicit attack strategy may be not unique, and the attacking effectiveness is primarily determined by the fraction of the Byzantine nodes rather than the channel dynamics. That is, to make the channel-aware approach more practical, the challenge that the heavy reliance on the global ICSI and decision fusion rule in obtaining the Byzantine attacks is successfully relaxed. Finally, we empirically validate our theoretical analysis through extensive simulations across a wide range of attacking scenarios.

cs.IT

BSDEs driven by $G$-Brownian motion with uniformly continuous generators

The present paper is devoted to investigating the existence and uniqueness of solutions to a class of non-Lipschitz scalar valued backward stochastic differential equations driven by $G$-Brownian motion ($G$-BSDEs). In fact, when the generators are Lipschitz continuous in $y$ and uniformly continuous in $z$, we construct the unique solution to such equations by monotone convergence argument. The comparison theorem and related Feynman-Kac formula are stated as well.

math.PR

Quasi-continuous random variables and processes under the G-expectation framework

In this paper, we first use PDE techniques and probabilistic methods to identify a kind of quasi-continuous random variables. Then we give a characterization of the $G$-integrable processes and get a kind of quasi-continuous processes by Krylov's estimates. This result is useful for the development of $G$-stochastic analysis theory. Moreover, it also provides a tool for the study of the non-Markovian Itô processes.

math.PR

Some sample path properties of G-Brownian motion

In this paper, we shall study the basic absolute properties of $G$-Brownian motion, i.e., those properties which hold for q.s. $ω$. These include the characterization of the zero set and the local maxima of the $G$-Brownian motion paths. We also show that the indicator function of $G$-Brownian motion is in $\mathbb{L}_G^1(Ω)$, which is an useful tool for the study of $G$-expectation theory.

math.PR

Invariant and ergodic measures for G-diffusion processes

In this paper we study the problems of invariant and ergodic measures under G-expectation framework. In particular, the stochastic differential equations driven by G-Brownian motion have the unique invariant and ergodic measures. Moreover, the invariant and ergodic measures of G-SDEs are also sublinear expectations. However, the invariant measures may not coincide with ergodic measures, which is different from the classical case.

math.PR