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). In 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 analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, 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. Next, 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 highlight the unsolved research challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral

Understanding the Hydrodynamics in a 2-Dimensional Downer by CFD-DEM Simulation

The gas-solid flows in a 2-dimensional downer were simulated using a CFD-DEM method. The predicted macro-scale flow structure had good agreement with the experiments. The distinct clustering phenomena at meso-scale were revealed throughout the downer. Influences of the collision properties of the wall and the particles on the hydrodynamics in downer were investigated

Adiabatic compressed air energy storage with packed bed thermal energy storage

AbstractThe majority of articles on Adiabatic Compressed Air Energy Storage (A-CAES) so far have focussed on the use of indirect-contact heat exchangers and a thermal fluid in which to store the compression heat. While packed beds have been suggested, a detailed analysis of A-CAES with packed beds is lacking in the available literature. This paper presents such an analysis. We develop a numerical model of an A-CAES system with packed beds and validate it against analytical solutions. Our results suggest that an efficiency in excess of 70% should be achievable, which is higher than many of the previous estimates for A-CAES systems using indirect-contact heat exchangers. We carry out an exergy analysis for a single charge–storage–discharge cycle to see where the main losses are likely to transpire and we find that the main losses occur in the compressors and expanders (accounting for nearly 20% of the work input) rather than in the packed beds. The system is then simulated for continuous cycling and it is found that the build-up of leftover heat from previous cycles in the packed beds results in higher steady state temperature profiles of the packed beds. This leads to a small reduction (<0.5%) in efficiency for continuous operation

Robust ground-state energy estimation under depolarizing noise

We present a novel ground-state energy estimation algorithm that is robust under global depolarizing error channels. Building upon the recently developed Quantum Exponential Least Squares (QCELS) algorithm [Ding, Lin, PRX Quantum, 4, 020331, 2023], our new approach incorporates significant advancements to ensure robust estimation while maintaining a polynomial cost in precision. By leveraging the spectral gap of the Hamiltonian effectively, our algorithm overcomes limitations observed in previous methods like quantum phase estimation (QPE) and robust phase estimation (RPE). Going beyond global depolarizing error channels, our work underscores the significance and practical advantages of utilizing randomized compiling techniques to tailor quantum noise towards depolarizing error channels. Our research demonstrates the feasibility of ground-state energy estimation in the presence of depolarizing noise, offering potential advancements in error correction and algorithmic-level error mitigation for quantum algorithms.Comment: 35 pages, 8 figures. The first two authors contributed equally to this wor