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Range sidelobe suppression for OFDM-integrated radar and communication signal

JOURNAL OF ENGINEERING-JOE 2019年第21期2019卷 7624-7627页

作者： Zuo, Jiajun Yang, Ruijuan Luo, Shaohua Zhou, Yugang Air Force Early Warning Acad Wuhan 430010 Hubei Peoples R China

To improve the RadCom system of performing both data transmission and radar-sensing functions, here a waveform design method for orthogonal frequency division multiplexing (OFDM) signal is proposed to solve the problem of high range sidelobe level in radar detection process while maintaining a reasonable channel capacity. The method only transmits data in the sub-channels with preferable channel quality. Then, calculate the weight of the chosen subcarriers through the convex optimisation method to traditionally minimise the peak-to-sidelobe (PSL) ratio. Simulation results prove that, compared with traditional range sidelobe suppression method, this algorithm is more suitable for RadCom system.

关键词： radar detection convex programming radar signal processing radio networks OFDM modulation preferable channel quality convex optimisation method peak-to-sidelobe ratio traditional range sidelobe suppression method RadCom system communication signal data transmission radar-sensing functions waveform design method high range sidelobe level radar detection process reasonable channel capacity

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On the convexity of Latent Social Network Inference 10

On the Convexity of Latent Social Network Inference

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Annual Conference on Neural Information Processing Systems

作者： Seth A. Myers Jure Leskovec Institute for Computational and Mathematical Engineering Stanford University Department of Computer Science Stanford University

ISBN: (纸本)9781617823800

In many real-world scenarios, it is nearly impossible to collect explicit social network data. In such cases, whole networks must be inferred from underlying observations. Here, we formulate the problem of inferring latent social networks based on network diffusion or disease propagation data. We consider contagions propagating over the edges of an unobserved social network, where we only observe the times when nodes became infected, but not who infected them. Given such node infection times, we then identify the optimal network that best explains the observed data. We present a maximum likelihood approach based on convex programming with a l_1-like penalty term that encourages sparsity. Experiments on real and synthetic data reveal that our method near-perfectly recovers the underlying network structure as well as the parameters of the contagion propagation model. Moreover, our approach scales well as it can infer optimal networks of thousands of nodes in a matter of minutes.

关键词： Social Networks convexity Social Media Ganglia convex programming synthetic data Network observational data

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Distributed Interference-Aware Energy-Efficient Resource Allocation for Device-to-Device Communications Underlaying Cellular Networks

Distributed Interference-Aware Energy-Efficient Resource All...

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IEEE Global Communications Conference

作者： Zhenyu Zhou Mianxiong Dong Kaoru Ota Jun Wu Takuro Sato State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical and Electronic Engineering North China Electric Power University National Institute of Information and Communications Technology Department of Information and Electric Engineering Muroran Institute of Technology School of Information Security Engineering Shanghai Jiao Tong University Graduate School of Global Information and Telecommunication Studies Waseda University

ISBN: (纸本)9781479935130

The introduction of device-to-device (D2D) into cellular networks poses many new challenges in the resource allocation design due to the co-channel interference caused by spectrum reuse and limited battery life of user equipments (UEs). In this paper, we propose a distributed interference-aware energy-efficient resource allocation algorithm to maximize each UE's energy efficiency (EE) subject to its specific quality of service (QoS) and maximum transmission power constraints. We model the resource allocation problem as a noncooperative game, in which each player is self-interested and wants to maximize its own EE. The formulated EE maximization problem is a non-convex problem and is transformed into a convex optimization problem by exploiting the properties of the nonlinear fractional programming. An iterative optimization algorithm is proposed and verified through computer simulations.

关键词： cellular radio cochannel interference concave programming convex programming energy conservation iterative methods quality of service resource allocation D2D communication EE maximization problem QoS UE convex optimization problem device-to-device communication distributed interference-aware energy-efficient resource allocation iterative optimization algorithm nonconvex problem noncooperative game nonlinear fractional programming spectrum reuse transmission power constraint underlaying cellular network user equipment Games Interference Optimization Quality of service Receivers Resource management Transmitters

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Wireless Powered Communication Networks Aided by an Unmanned Aerial Vehicle

Wireless Powered Communication Networks Aided by an Unmanned...

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IEEE Vehicular Technology Conference

作者： Junhee Park Hoon Lee Subin Eom Inkyu Lee Sch. of Electr. Eng. Korea Univ. Seoul South Korea Inf. Syst. Technol. & Design Pillar Singapore Univ. of Technol. & Design Singapore Singapore

ISBN: (纸本)9781538663592;9781538663585

This paper investigates an unmanned aerial vehicle (UAV)-aided wireless powered communication network where a mobile hybrid access point serves multiple energy-constrained ground terminals (GTs) in terms of wireless energy transfer and data collection. Specifically, to support information transmission of the GTs, the mobile UAV first transfers wireless energy in the downlink. Then, by harvesting this wireless energy, the GTs transmit their uplink information signals to the UAV in a time division multiple access manner. In this system, we jointly optimize the trajectory of the UAV and the uplink power control policy in order to maximize the minimum throughput of the GTs. By applying the concave-convex procedure, we propose an iterative algorithm which efficiently identifies a locally optimal solution. Simulation results verify the efficiency of the proposed algorithm compared to conventional schemes.

关键词： autonomous aerial vehicles concave programming convex programming energy harvesting iterative methods mobile radio power control radiofrequency power transmission telecommunication control telecommunication power management telecommunication power supplie Communication and control networks(communications) Mobile radios convex programming drone honey bees Unmanned Aerial Vehicle iterative methods energy harvesting Power control wireless vehicle time division multiple access data transmission

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一种基于Peaceman-Rachford分裂算法的前景提取方法

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高等学校计算数学学报 2019年第3期41卷 277-288页

作者：怀开展倪明放高洁陆军工程大学研究生院吉林大学珠海学院

1引言快速准确地从视频流中提取背景和运动前景是计算机视觉领域一个重要的热门研究课题,特别是在视频监视、交通监控系统等实际应用中.然而,运动前景的不断变化使得求解这个问题变得很困难.许多学者对此问题进行了深入的研究,并提出了... 详细信息

1引言快速准确地从视频流中提取背景和运动前景是计算机视觉领域一个重要的热门研究课题,特别是在视频监视、交通监控系统等实际应用中.然而,运动前景的不断变化使得求解这个问题变得很困难.许多学者对此问题进行了深入的研究,并提出了一些行之有效的方法,其中Candes等人[1]指出可以将前景提取问题刻画为主成分追踪（Principal Component Pursuit）模型·主成分追踪模型可以简单地描述为:已知观测矩阵M∈Rm×n(由

关键词： Foreground extraction Peaceman-Rachford splitting method convex programming

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Energy Balance Based Power Generation Scheduling of Microgrid with Nonanticipativity

Energy Balance Based Power Generation Scheduling of Microgri...

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第三十九届中国控制会议

作者： Zhao JieXing Zhai QiaoZhu Miao HongYi Systems Engineering Institute MOE KLINNS Lab and SKLMS Lab Xi’an Jiaotong University

Optimal scheduling of the power generation system is of great importance to the security and reliability of the power grid. Microgrid can reduce operating costs through reasonable economic dispatch. At present, related research mainly uses the power balance constraint based on the discrete-time model, which couldn’t exactly model the integral relationship between electrical energy and power output rate of generators. A scheduling formulation is established with consideration of integral constraints and nonanticipativity in this paper. With detailed analyzing the original model, a three-stage numerical solution method is proposed. By solving convex programming, in most instances, the global optimal solution can be obtained efficiently. The optimal schedule in the sense of expectation can be obtained with the real-time load demand satisfied and nonanticipativity guaranteed. Numerical testing for autonomous power plants of a big steel enterprise is performed and the effectiveness of the method is verified.

关键词： optimal scheduling microgrid integral constraints nonanticipativity convex programming

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FREQUENCY-DOMAIN BLIND SPEECH SEPARATION USING INCOMPLETE DE-MIXING TRANSFORM

FREQUENCY-DOMAIN BLIND SPEECH SEPARATION USING INCOMPLETE DE...

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European Signal Processing Conference

作者： Zbynek Koldovsky Francesco Nesta Petr Tichavsky Nobutaka Ono Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Conexant System Institute of Information Theory and Automation National Institute of Informatics (NII) SOKENDAI (The Graduate University for Advanced Studies)

ISBN: (纸本)9781509018918

We propose a novel solution to the blind speech separation problem where the de-mixing transform is estimated only within selected frequency bins. This solution is based on Independent Vector Analysis applied to a subset of instantaneous mixtures, one per selected frequency bin. Next, two approaches are proposed to complete the transform: one based on null beamforming, and the other based on convex programming. In subsequent experiments, we compare combinations of both methods and evaluate their ability to retrieve the whole de-mixing transform. Depending on the number of selected frequencies and the sparsity of room impulse responses, the methods show improvements in terms of computational complexity as well as in terms of separation accuracy.

关键词： Blind Source Separation Independent Vector Analysis Relative Transfer Function Sparse Reconstruction convex Optimization Blind source separation convex function Speech Frequency-domain analysis Vector Analysis Transform convex programming separation complexity classes

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Robust PCA with compressed data 15

Robust PCA with compressed data

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Annual Conference on Neural Information Processing Systems

作者： Wooseok Ha Rina Foygel Barber University of Chicago

ISBN: (纸本)9781510825024

The robust principal component analysis (RPCA) problem seeks to separate low-rank trends from sparse outliers within a data matrix, that is, to approximate a n×d matrix D as the sum of a low-rank matrix L and a sparse matrix S. We examine the robust principal component analysis (RPCA) problem under data compression, where the data Y is approximately given by (L+S) C, that is, a low-rank -I- sparse data matrix that has been compressed to size n×m (with m substantially smaller than the original dimension d) via multiplication with a compression matrix C. We give a convex program for recovering the sparse component 5 along with the compressed low-rank component L · C, along with upper bounds on the error of this reconstruction that scales naturally with the compression dimension m and coincides with existing results for the uncompressed setting m = d. Our results can also handle error introduced through additive noise or through missing data. The scaling of dimension, compression, and signal complexity in our theoretical results is verified empirically through simulations, and we also apply our method to a data set measuring chlorine concentration across a network of sensors to test its performance in practice.

关键词： Data compression principal components analysis matrices convex programming Additive noise dimensions Upper bound Passive Cutaneous Anaphylaxis

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Katz centrality of Markovian temporal networks: Analysis and optimization

Katz centrality of Markovian temporal networks: Analysis and...

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American Control Conference

作者： Masaki Ogura Victor M. Preciado Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia 19014 USA

ISBN: (纸本)9781509045839

Identifying important nodes in complex networks is a fundamental problem in network analysis. Although a plethora of measures has been proposed to identify important nodes in static (i.e., time-invariant) networks, there is a lack of tools in the context of temporal networks (i.e., networks whose connectivity dynamically changes over time). The aim of this paper is to propose a system-theoretic approach for identifying important nodes in temporal networks. In this direction, we first propose a generalization of the popular Katz centrality measure to the family of Markovian temporal networks using tools from the theory of Markov jump linear systems. We then show that Katz centrality in Markovian temporal networks can be efficiently computed using linear programming. Finally, we propose a convex program for optimizing the Katz centrality of a given node by tuning the weights of the temporal network in a cost-efficient manner. Numerical simulations illustrate the effectiveness of the obtained results.

关键词： Network Network analysis Ganglia convex programming time-invariant complex networks Plethora

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The Sum-Rate Maximization Precoding for Multiuser MIMO SWIPT Systems

The Sum-Rate Maximization Precoding for Multiuser MIMO SWIPT...

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IEEE Vehicular Technology Conference

作者： Zhaohui Yue Hui Gao Ruohan Cao Tiejun Lv Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications

ISBN: (纸本)9781479980895

This paper proposes a linear precoding that aims at maximizing the sum-rate in a multiuser multiple-input multiple-output (MU-MIMO) simultaneous wireless information and power transfer (SWIPT) system. In this scenario some receivers harvest energy while the others decode information at the same time. The sum-rate for information decoding users is considered as the optimization policy where the transmit power constraint and power harvesting per user constraints are taken into account. Since the objective function of the sum-rate maximization problem is non-convex, it is difficult to find the optimal solution. Thus, we propose an iterative algorithm to find a locally optimal design based on a sequential convex approximation (SCA) method. In this way, the non-convex optimization problem is approximated by a convex program at each iteration. Finally, with the aid of the concept of the Rate-Energy (R-E) region simulation results show that the proposed scheme achieves significant improvement compared to existing works.

关键词： Non-convex prediction coding multiple input multiple output systems Transmitter power convex programming Optimal design DECODING Iterative algorithms Objective function convexity

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