9,430 research outputs found

    Generalized Self-Healing Key Distribution using Vector Space Access Structure

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    Abstract. We propose and analyze a generalized self-healing key distribution using vector space access structure in order to reach more flexible performance of the scheme. Our self-healing technique enables better performance gain over previous approaches in terms of storage, communication and computation complexity. We provide rigorous treatment of security of our scheme in an appropriate security framework and show it is computationally secure and achieves forward and backward secrecy

    Self-healing high-dimensional quantum key distribution using hybrid spin-orbit Bessel states

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    Using spatial modes for quantum key distribution (QKD) has become highly topical due to their infinite dimensionality, promising high information capacity per photon. However, spatial distortions reduce the feasible secret key rates and compromise the security of a quantum channel. In an extreme form such a distortion might be a physical obstacle, impeding line-of-sight for free-space channels. Here, by controlling the radial degree of freedom of a photon's spatial mode, we are able to demonstrate hybrid high-dimensional QKD through obstacles with self-reconstructing single photons. We construct high-dimensional mutually unbiased bases using spin-orbit hybrid states that are radially modulated with a non-diffracting Bessel-Gaussian (BG) profile, and show secure transmission through partially obstructed quantum links. Using a prepare-measure protocol we report higher quantum state self-reconstruction and information retention for the non-diffracting BG modes as compared to Laguerre-Gaussian modes, obtaining a quantum bit error rate (QBER) that is up to 3 times lower. This work highlights the importance of controlling the radial mode of single photons in quantum information processing and communication as well as the advantages of QKD with hybrid states.Comment: Published version, 15 pages, 6 figures, 2 table

    Orbital angular momentum of photons and the entanglement of Laguerre-Gaussian modes

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    The identification of orbital angular momentum (OAM) as a fundamental property of a beam of light nearly twenty-five years ago has led to an extensive body of research around this topic. The possibility that single photons can carry OAM has made this degree of freedom an ideal candidate for the investigation of complex quantum phenomena and their applications. Research in this direction has ranged from experiments on complex forms of quantum entanglement to the interaction between light and quantum states of matter. Furthermore, the use of OAM in quantum information has generated a lot of excitement, as it allows for encoding large amounts of information on a single photon. Here we explain the intuition that led to the first quantum experiment with OAM fifteen years ago. We continue by reviewing some key experiments investigating fundamental questions on photonic OAM and the first steps into applying these properties in novel quantum protocols. In the end, we identify several interesting open questions that could form the subject of future investigations with OAM.Comment: 17 pages, 7 figures; close to accepted versio

    A self-healing key distribution scheme based on vector space secret sharing and one way hash chains

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    An efficient self-healing key distribution scheme with revocation capability is proposed for secure group communication in wireless networks. The scheme bases on vector space secret sharing and one way hash function techniques. Vector space secret sharing helps to realize general monotone decreasing structures for the family of subsets of users that can be revoked instead of a threshold one. One way hash chains contribute to reduce communication overhead. Furthermore, the most prominent characteristic of our scheme is resisting collusion between the new joined users and the revoked users, which is fatal weakness of hash function based self-healing key distribution schemes

    A cell outage management framework for dense heterogeneous networks

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    In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner
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