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Time-Division Energy Beamforming for Multiuser Wireless Power Transfer with Non-Linear Energy Harvesting

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arXiv 2020年

作者： Ma, Ganggang Xu, Jie Liu, Ya-Feng Vedady Moghadam, Mohammad R. Shenzhen China Shenzhen China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China TransferFi Pte Ltd Singapore

Energy beamforming has emerged as a promising technique for enhancing the energy transfer efficiency of wireless power transfer (WPT). However, the performance of conventional energy beamforming may seriously degrade due to the nonlinear radio frequency (RF) to direct current (DC) conversion at energy receivers (ERs). To tackle this issue, this letter proposes a new time-division energy beamforming, in which different energy beamforming matrices (of high ranks in general) are time shared to exploit the "convex-concave" shape of the RF-DC power relation at ERs. By considering a particular time duration for WPT, we maximize the minimum harvested DC energy among all ERs, by jointly optimizing the energy beamforming matrices and the corresponding time allocation. In order to solve the non-convex min-DC-energy maximization problem, we propose an efficient solution by using the techniques of alternating optimization and successive convex approximation (SCA). Numerical results show that the proposed time-division energy beamforming design indeed outperforms the conventional multi-beam and time-division-multiple-access (TDMA)-based energy transmissions. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy harvesting

Factoring integers with sublinear resources on a superconducting quantum processor

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arXiv 2022年

作者： Yan, Bao Tan, Ziqi Wei, Shijie Jiang, Haocong Wang, Weilong Wang, Hong Luo, Lan Duan, Qianheng Liu, Yiting Shi, Wenhao Fei, Yangyang Meng, Xiangdong Han, Yu Shan, Zheng Chen, Jiachen Zhu, Xuhao Zhang, Chuanyu Jin, Feitong Li, Hekang Song, Chao Wang, Zhen Ma, Zhi Wang, H. Long, Gui-Lu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China School of Physics ZJU-Hangzhou Global Scientific and Technological Innovation Center Interdisciplinary Center for Quantum Information Zhejiang Province Key Laboratory of Quantum Technology and Device Zhejiang University Hangzhou310000 China Beijing Academy of Quantum Information Sciences Beijing100193 China Institute of Information Technology Information Engineering University Zhengzhou450001 China Beijing National Research Center for Information Science and Technology School of Information Tsinghua University Beijing100084 China Frontier Science Center for Quantum Information Beijing100084 China

Shor’s algorithm has seriously challenged information security based on public key cryptosystems. However, to break the widely used RSA-2048 scheme, one needs millions of physical qubits, which is far beyond current technical capabilities. Here, we report a universal quantum algorithm for integer factorization by combining the classical lattice reduction with a quantum approximate optimization algorithm (QAOA). The number of qubits required is O(logN/loglogN), which is sublinear in the bit length of the integer N, making it the most qubit-saving factorization algorithm to date. We demonstrate the algorithm experimentally by factoring integers up to 48 bits with 10 superconducting qubits, the largest integer factored on a quantum device. We estimate that a quantum circuit with 372 physical qubits and a depth of thousands is necessary to challenge RSA-2048 using our algorithm. Our study shows great promise in expediting the application of current noisy quantum computers, and paves the way to factor large integers of realistic cryptographic significance. © 2022, CC BY-NC-ND.

关键词： Qubits

High-order finite element methods for interface problems: Theory and implementations 12th

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High-order finite element methods for interface problems: Th...

12th International Conference on Spectral and High-Order Methods, ICOSAHOM 2018

作者： Xiao, Yuanming Zhai, Fangman Zhang, Linbo Zheng, Weiying Department of Mathematics Nanjing University Nanjing China Department of Applied Mathematics Nanjing Forestry University Nanjing China State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030396466

We propose arbitrary order extended finite element (XFE) methods for solving both two and three dimensional elliptic interface problems. The meshes in our methods do not need to fit the interface and discontinuous Galerkin (DG) schemes are used in the vicinity of the interface to incorporate the transmission conditions between subdomains. Optimal error estimates in the piecewise H1-norm and in the L2-norm are rigorously proved for the scheme. Two implementation aspects are addressed in this paper: (1) An optimal multigrid solver is introduced for the generated linear system, which converges uniformly with respect to the mesh size. (2) A high order algorithm is presented to compute the integral over elements intersecting the interface. Numerical experiments are reported to support the theoretical results. © 2020, The Author(s).

关键词： Linear systems

Max-Min Fairness in IRS-Aided Multi-Cell MISO Systems with Joint Transmit and Reflective Beamforming

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arXiv 2020年

作者： Xie, Hailiang Xu, Jie Liu, Ya-Feng School of Information Engineering Guangdong University of Technology Guangzhou510006 China Shenzhen518172 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

This paper investigates an intelligent reflecting surface (IRS)-aided multi-cell multiple-input single-output (MISO) system with a set of multi-antenna base stations (BSs) each communicating with a single-antenna user, in which an IRS is dedicatedly deployed for assisting the wireless transmission and suppressing the inter-cell interference. Under this setup, we jointly optimize the coordinated transmit beamforming vectors at the BSs and the reflective beamforming vector at the IRS, for the purpose of maximizing the minimum weighted signal-to-interference-plus-noise ratio (SINR) at the users, subject to the individual maximum transmit power constraints at the BSs and the reflection constraints at the IRS. To solve the non-convex min-weighted-SINR maximization problem, we first present an exact-alternating-optimization approach to optimize the transmit and reflective beamforming vectors in an alternating manner, in which the transmit and reflective beamforming optimization subproblems are solved exactly in each iteration by using the techniques of second-order-cone program (SOCP) and semi-definite relaxation (SDR), respectively. However, the exact-alternating-optimization approach has high computational complexity, and may lead to compromised performance due to the uncertainty of randomization in SDR. To avoid these drawbacks, we further propose an inexact-alternating-optimization approach, in which the transmit and reflective beamforming optimization subproblems are solved inexactly in each iteration based on the principle of successive convex approximation (SCA). In addition, to further reduce the computational complexity, we propose a low-complexity inexact-alternating-optimization design, in which the reflective beamforming optimization subproblem is solved more inexactly. Via numerical results, it is shown that the proposed three designs achieve significantly increased min-weighted-SINR values, as compared with benchmark schemes without the IRS or with random re

关键词： Beamforming

An efficient quadratic programming relaxation based algorithm for large-scale MIMO detection

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arXiv 2020年

作者： Zhao, Ping-Fan Li, Qing-Na Chen, Wei-Kun Liu, Ya-Feng School of Mathematics and Statistics Beijing Institute of Technology Beijing China School of Mathematics and Statistics Beijing Key Laboratory on MCAACI Beijing Institute of Technology Beijing China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

Multiple-input multiple-output (MIMO) detection is a fundamental problem in wireless communications and it is strongly NP-hard in general. Massive MIMO has been recognized as a key technology in the fifth generation (5G) and beyond communication networks, which on one hand can significantly improve the communication performance, and on the other hand poses new challenges of solving the corresponding optimization problems due to the large problem size. While various efficient algorithms such as semidefinite relaxation (SDR) based approaches have been proposed for solving the small-scale MIMO detection problem, they are not suitable to solve the large-scale MIMO detection problem due to their high computational complexities. In this paper, we propose an efficient sparse quadratic programming (SQP) relaxation based algorithm for solving the large-scale MIMO detection problem. In particular, we first reformulate the MIMO detection problem as an SQP problem. By dropping the sparse constraint, the resulting relaxation problem shares the same global minimizer with the SQP problem. In sharp contrast to the SDRs for the MIMO detection problem, our relaxation does not contain any (positive semidefinite) matrix variable and the numbers of variables and constraints in our relaxation are significantly less than those in the SDRs, which makes it particularly suitable for the large-scale problem. Then we propose a projected Newton based quadratic penalty method to solve the relaxation problem, which is guaranteed to converge to the vector of transmitted signals under reasonable conditions. By extensive numerical experiments, when applied to solve small-scale problems, the proposed algorithm is demonstrated to be competitive with the state-of-the-art approaches in terms of detection accuracy and solution efficiency;when applied to solve large-scale problems, the proposed algorithm achieves better detection performance than a recently proposed generalized power *** Codes 90C2

关键词： Quadratic programming

Cramér-rao bound optimization for joint radar-communication design

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arXiv 2021年

作者： Liu, Fan Liu, Ya-Feng Li, Ang Masouros, Christos Eldar, Yonina C. The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematicsand Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The School of Information and Communications Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’anShaanxi710049 China The Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom The Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel

In this paper, we propose multi-input multi-output (MIMO) beamforming designs towards joint radar sensing and multi-user communications. We employ the Cramér-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios. We then propose minimizing the CRB of radar sensing while guaranteeing a pre-defined level of signal-to-interference-plus-noise ratio (SINR) for each communication user. For the single-user scenario, we derive a closed form for the optimal solution for both cases of point and extended targets. For the multi-user scenario, we show that both problems can be relaxed into semidefinite programming by using the semidefinite relaxation approach, and prove that the global optimum can always be obtained. Finally, we demonstrate numerically that the globally optimal solutions are reachable via the proposed methods, which provide significant gains in target estimation performance over state-of-the-art benchmarks. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal to noise ratio

Advanced Analysis of Arabidopsis thaliana Leaf Traits Using RGB Imaging and Machine Learning

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Advanced Analysis of Arabidopsis thaliana Leaf Traits Using ...

International Symposium on Industrial Electronics (INDEL)

作者： Rijad Sarić Edhem Čustović Martin Trtílek Amila Akagić Mathew G. Lewsey James Whelan La Trobe Institute for Sustainable Agriculture & Food (LISAF) La Trobe University Melbourne VIC Australia Australian Research Council Research Hub for Medicinal Agriculture La Trobe University Melbourne VIC Australia School of Computing Engineering and Mathematical Sciences La Trobe University (LTU) Melbourne VIC Australia Scientific Instruments Australia (SIA) Melbourne VIC Australia Photon Systems Instruments (PSI) Plant Phenotyping Research Centre Drasov Brno Czech Republic Faculty of Electrical Engineering University of Sarajevo (UNSA) Sarajevo Bosnia and Herzegovina State Key Laboratory of Plant Environmental Resilience College of Life Science Zhejiang University (ZJU) P.R. China Provincial International Science and Technology Cooperation Base on Engineering Biology Zhejiang University P.R China

ISBN: (数字)9798350352320

ISBN: (纸本)9798350352337

Image-based high-throughput plant phenotyping utilises various imaging techniques to automatically and non-invasively understand the growth of different plant species. These innovative imaging infrastructures are implemented to monitor plant development over time in indoor or outdoor environments. However, understanding the relationship between genotype and phenotype interactions under different environments remains challenging. This research study demonstrates superior extraction of leaf morphological features of different Arabidopsis thaliana ecotypes by analysing leaf geometry using a sequence of RGB images. Upon successful extraction of anatomical features, leaf length and area are converted into physical coordinates. Furthermore, considering these leaf features as 1D signals, the Fourier Spectrum is analysed, and most descriptive features are selected using PCA. Finally, leaf shape classification is established by training and testing five distinct ML models. A thorough evaluation of selected models demonstrates superiority in classifying two common leaf shapes of Arabidopsis plants.

关键词： Training Shape Biological system modeling Imaging Machine learning Feature extraction Real-time systems Reliability Testing Principal component analysis

Convergence rate of inertial forward-backward splitting algorithms based on the local error bound condition

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arXiv 2020年

作者： Liu, Hongwei Wang, Ting Liu, Zexian School Of Mathematics And Statistics Xidian University Xi'An710126 China State Key Laboratory Of Scientific And Engineering Computing Institute Of Computational Mathematics And Scientific/Engineering Computing AMSS Chinese Academy Of Sciences Beijing100190 China School Of Mathematics And Statistics Guizhou University Guiyang550025 China

The " Inertial Forward-Backward algorithm " (IFB) is a powerful tool for convex nonsmooth minimization problems, and under the local error bound condition, the R-linear convergence rates for the sequences of objective values and iterates have been proved if the inertial parameter γksatisfies supkγkkγk= 1 is not know. In this paper, based on the local error bound condition, we exploit a new assumption condition for the important parameter tkin IFB, which implies that limk→∞γk= 1, and establish the convergence rate of function values and strong convergence of the iterates generated by the IFB algorithms with six tksatisfying the above assumption condition in Hilbert space. It is remarkable that, under the local error bound condition, we show that the IFB algorithms with some tkcan achieve sublinear convergence rate of o(1/kp) for any positive integer p > 1. In addition, we propose a class of Inertial Forward-Backward algorithm with adaptive modification and show it has same convergence results as IFB under the error bound condition. Some numerical experiments are conducted to illustrate our results. Copyright © 2020, The Authors. All rights reserved.

关键词： Parameter estimation

Capacity optimality of AMP in coded systems

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arXiv 2021年

作者： Liu, Lei Liang, Chulong Ma, Junjie Ping, Li Department of Electronic Engineering City University of Hong Kong Hong Kong School of Information Science Japan Institute of Science and Technology Nomi923-1292 Japan State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE Corporation Shenzhen518057 China Institute of Computational Mathematics and Scientific Engineering Computing Chinese Academy of Sciences China

This paper studies a large random matrix system (LRMS) model involving an arbitrary signal distribution and forward error control (FEC) coding. We establish an area property based on the approximate message passing (AMP) algorithm. Under the assumption that the state evolution for AMP is correct for the coded system, the achievable rate of AMP is analyzed. We prove that AMP achieves the constrained capacity of the LRMS with an arbitrary signal distribution provided that a matching condition is satisfied. We provide related numerical results of binary signaling using irregular low-density parity-check (LDPC) codes. We show that the optimized codes demonstrate significantly better performance over unmatched ones under AMP. For quadrature phase shift keying (QPSK) modulation, bit error rate (BER) performance within 1 dB from the constrained capacity limit is observed. © 2021, CC BY-NC-SA.

关键词： Message passing