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Algorithms for Globally-Optimal Secure Signaling over Gaussian MIMO Wiretap Channels Under Interference Constraints

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arXiv 2020年

作者： Dong, Limeng Loyka, Sergey Li, Yong Ministry of Education Key Lab for Intelligent Networks and Network Security School of Information and Communications Engineering Xi’an Jiaotong University Xi’anShaanxi710049 China School of Electrical Engineering and Computer Science University of Ottawa Canada School of Electronics and Information Northwestern Polytechnical University Xian China

Multi-user Gaussian MIMO wiretap channel is considered under interference power constraints (IPC), in addition to the total transmit power constraint (TPC). Algorithms for global maximization of its secrecy rate are proposed. Their convergence to the secrecy capacity is rigorously proved and a number of properties are established analytically. Unlike known algorithms, the proposed ones are not limited to the MISO case and are proved to converge to a global rather than local optimum in the general MIMO case, even when the channel is not degraded. In practice, the convergence is fast as only a small to moderate number of Newton steps is required to achieve a high precision level. The interplay of TPC and IPC is shown to result in an unusual property when an optimal point of the max-min problem does not provide an optimal transmit covariance matrix in some (singular) cases. To address this issue, an algorithm is developed to compute an optimal transmit covariance matrix in those singular cases. It is shown that this algorithm also solves the dual (nonconvex) problems of globally minimizing the total transmit power subject to the secrecy and interference constraints;it provides the minimum transmit power and respective signaling strategy needed to achieve the secrecy capacity, hence allowing power savings. Copyright © 2020, The Authors. All rights reserved.

关键词： Covariance matrix

Physical layer security enhancement using artificial noise in cellular vehicle-to-everything (C-V2X) networks

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arXiv 2020年

作者： Wang, Chao Li, Zan Xia, Xiang-Gen Shi, Jia Si, Jiangbo Zou, Yulong Integrated Service Networks Lab Xidian University Xi’an710071 China College of Communications Engineering Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States School of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China

The secure transmission of confidential information in cellular vehicle-to-everything (C-V2X) communication networks is vitally important for user’s personal safety. However, for C-V2X there have not been much studies on the physical layer security (PLS). Since artificial noise (AN) and secure beamforming are popular PLS techniques for cellular communications, in this paper we investigate the potential of these PLS techniques for enhancing the security of C-V2X networks. In particular, leveraging stochastic geometry, we study the PLS of an AN assisted C-V2X network, where the locations of legitimate vehicular nodes, malicious vehicular nodes and road side units (RSUs) are modeled by Cox processes driven by a common Poisson line process (PLP), and the locations of cellular base stations (BSs) are modeled by a two-dimensional (2D) Poisson point process (PPP). Based on the maximum signal-to-interference-ratio (SIR) association scheme, we calculate the coverage probability of the network. We also derive bounds on the secrecy probability, which are validated by simulation results. Moreover, we obtain an analytical result of the effective secrecy throughput for characterizing the reliability and security of wiretap channels. Simulation results are given to validate the analytical result, and provide interesting insights into the impact of network parameters on the achievable secrecy performance. Simulation results show that a larger array antenna can provide a better robustness of the secure transmission strategy, and the optimal power allocation ratio between confidential information and AN remains almost unchanged for different numbers of antennas. Copyright © 2020, The Authors. All rights reserved.

关键词： Geometry

A Novel Random Access Scheme for M2M Communication in Crowded Asynchronous Massive MIMO Systems

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arXiv 2020年

作者： Han, Huimei Zhai, Wenchao Wu, Zhefu Li, Ying Zhao, Jun Chen, Mingda College of Information Engineering Zhejiang University of Technology HangzhouZhejiang Province China College of Information Engineering China Jiliang University HangzhouZhejiang Province China State Key Lab of Integrated Services Networks Xidian University Xian710071 China School of Computer Science and Engineering Nanyang Technological University Singapore

A new random access scheme is proposed to solve the intra-cell pilot collision for M2M communication in crowded asynchronous massive multiple-input multiple-output (MIMO) systems. The proposed scheme utilizes the proposed estimation of signal parameters via rotational invariance technique enhanced (ESPRIT-E) method to estimate the effective timing offsets, and then active UEs obtain their timing errors from the effective timing offsets for uplink message transmission. We analyze the mean squared error of the estimated effective timing offsets of UEs, and the uplink throughput. Simulation results show that, compared to the exiting random access scheme for the crowded asynchronous massive MIMO systems, the proposed scheme can improve the uplink throughput and estimate the effective timing offsets accurately at the same time. Copyright © 2020, The Authors. All rights reserved.

关键词： MIMO systems

Boosting fronthaul capacity: Global optimization of power sharing for centralized radio access network

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arXiv 2018年

作者： Zhang, Jiankang Chen, Sheng Guo, Xinying Shi, Jia Hanzo, Lajos School of Electronics and Computer Science University of Southampton United Kingdom King Abdulaziz University Jeddah Saudi Arabia College of Information Science and Engineering Henan University of Technology China Integrated Service Networks Lab Xidian University Xi'an China

The limited fronthaul capacity imposes a challenge on the uplink of centralized radio access network (C-RAN). We propose to boost the fronthaul capacity of massive multipleinput multiple-output (MIMO) aided C-RAN by globally optimizing the power sharing between channel estimation and data transmission both for the user devices (UDs) and the remote radio units (RRUs). Intuitively, allocating more power to the channel estimation will result in more accurate channel estimates, which increases the achievable throughput. However, increasing the power allocated to the pilot training will reduce the power assigned to data transmission, which reduces the achievable throughput. In order to optimize the powers allocated to the pilot training and to the data transmission of both the UDs and the RRUs, we assign an individual power sharing factor to each of them and derive an asymptotic closed-form expression of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU) links. We then exploit the CRAN architecture's central computing and control capability for jointly optimizing the UDs' power sharing factors and the RRUs' power sharing factors aiming for maximizing the fronthaul capacity. Our simulation results show that the fronthaul capacity is significantly boosted by the proposed global optimization of the power allocation between channel estimation and data transmission both for the UDs and for their host RRUs. As a specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing factors improves 33% compared with the one attained without optimizing power sharing factors. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal to noise ratio

An ML estimation based robust Chinese remainder theorem for reals

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An ML estimation based robust Chinese remainder theorem for ...

IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP)

作者： Wenjie Wang Xiaoping Li Xiang-Gen Xia MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xian Shaanxi P. R. China Xidian University Xian China Department of Electrical and Computer Engineering University of Delaware Newark DE USA

ISBN: (纸本)9781479919499

In this paper, we consider the CRT problem for real numbers with noisy remainders that follow wrapped Gaussian distributions. We propose the maximum likelihood (ML) estimation based CRT when the remainder noises may not necessarily have the same variances. The proposed algorithm only needs to search for the solution among L elements, where L is the number of remainders. We compare the performances of the newly proposed algorithm and the existing algorithm in term of numerical simulations. The results demonstrate that the proposed algorithm not only has a better performance when the remainders have different error levels/variances, but also has a much lower computational complexity.

关键词： Robustness Maximum likelihood estimation Noise Image reconstruction Noise measurement Computational complexity

On privacy protection of latent dirichlet allocation model training

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arXiv 2019年

作者： Zhao, Fangyuan Ren, Xuebin Yang, Shusen Yang, Xinyu School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi'an Jiaotong University China

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive information of the training datasets and bring significant privacy risks. To mitigate the privacy issues in LDA, we focus on studying privacy-preserving algorithms of LDA model training in this paper. In particular, we first develop a privacy monitoring algorithm to investigate the privacy guarantee obtained from the inherent randomness of the collapsed gibbs sampling (CGS) process in a typical LDA training algorithm on centralized curated datasets. Then, we further propose a locally private LDA training algorithm on crowdsourced data to provide local differential privacy for individual data contributors. The experimental results on real-world datasets demonstrate the effectiveness of our proposed algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistics

ZSTAD: Zero-Shot Temporal Activity Detection

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ZSTAD: Zero-Shot Temporal Activity Detection

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Lingling Zhang Xiaojun Chang Jun Liu Minnan Luo Sen Wang Zongyuan Ge Alexander Hauptmann School of Computer Science and Technology Xi'an Jiaotong University Xian China Ministry of Education Key Lab For Intelligent Networks and Network Security Xian China Faculty of Information Technology Monash University Australia National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Xian China School of Information Technology and Electrical Engineering The University of Queensland Australia School of Computer Science Carnegie Mellon University USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected. We design an end-to-end deep network based on R-C3D as the architecture for this solution. The proposed network is optimized with an innovative loss function that considers the embeddings of activity labels and their super-classes while learning the common semantics of seen and unseen activities. Experiments on both the THUMOS'14 and the Charades datasets show promising performance in terms of detecting unseen activities.

关键词： Semantics Proposals Training Task analysis Feature extraction Microsoft Windows Three-dimensional displays

Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification

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arXiv 2019年

作者： Luo, Yong Tao, Dacheng Xu, Chang Xu, Chao Liu, Hong Wen, Yonggang School of Electronics Engineering and Computer Science Peking University Beijing100871 China Centre for Quantum Computation and Intelligent Systems University of Technology Sydney Jones Street Ultimo SydneyNSW2007 Australia Engineering Lab on Intelligent Perception for Internet of Things Shenzhen Graduate School Peking University China Division of Networks and Distributed Systems School of Computer Engineering Nanyang Technological University Singapore Singapore

—In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g. pedestrian, bicycle and tree) and is properly characterized by multiple visual features (e.g. color, texture and shape). Currently available tools ignore either the label relationship or the view complementary. Motivated by the success of the vector-valued function that constructs matrix-valued kernels to explore the multi-label structure in the output space, we introduce multi-view vector-valued manifold regularization (MV3MR) to integrate multiple features. MV3MR exploits the complementary property of different features and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifold regularization. We conducted extensive experiments on two challenging, but popular datasets, PASCAL VOC’ 07 (VOC) and MIR Flickr (MIR), and validated the effectiveness of the proposed MV3MR for image classification. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

An Operating System Level Data Migration Scheme in Hybrid DRAM-NVM Memory Architecture

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An Operating System Level Data Migration Scheme in Hybrid DR...

Design, Automation & Test in Europe Conference & Exhibition

作者： Reza Salkhordeh Hossein Asadi Data Storage Systems & Networks (DSN) Lab Department of Computer Engineering Sharif University of Technology Tehran Iran

ISBN: (纸本)9781467392280

With the emergence of Non-Volatile Memories (NVMs) and their shortcomings such as limited endurance and high power consumption in write requests, several studies have suggested hybrid memory architecture employing both Dynamic Random Access Memory (DRAM) and NVM in a memory system. By conducting a comprehensive experiments, we have observed that such studies lack to consider very important aspects of hybrid memories including the effect of: a) data migrations on performance, b) data migrations on power, and c) the granularity of data migration. This paper presents an efficient data migration scheme at the Operating System level in a hybrid DRAMNVM memory architecture. In the proposed scheme, two Least Recently Used (LRU) queues, one for DRAM section and one for NVM section, are used for the sake of data migration. With careful characterization of the workloads obtained from PARSEC benchmark suite, the proposed scheme prevents unnecessary migrations and only allows migrations which benefits the system in terms of power and performance. The experimental results show that the proposed scheme can reduce the power consumption up to 79% compared to DRAM-only memory and up to 48% compared to the state-of-the art techniques.

关键词： Operating System Level Data Migration Scheme Hybrid DRAM-NVM Memory Architecture emergence