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ML and MAP Device Activity Detections for Grant-Free Massive Access in Multi-Cell Networks

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第6期21卷 3893-3908页

作者： Jiang, Dongdong Cui, Ying Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Zhengzhou Univ Sch Informat Engn Henan Joint Int Res Lab Intelligent Networking & Zhengzhou 450001 Henan Peoples R China

Device activity detection is one main challenge in grant-free massive access, which is recently proposed to support massive machine-type communications (mMTC). Existing solutions for device activity detection fail to consider inter-cell interference generated by massive IoT devices or important prior information on device activities and inter-cell interference. In this paper, given different numbers of observations and network parameters, we consider both non-cooperative device activity detection and cooperative device activity detection in a multi-cell network, consisting of many access points (APs) and IoT devices. Under each activity detection mechanism, we consider the joint maximum likelihood (ML) estimation and joint maximum a posterior probability (MAP) estimation of both device activities and interference powers, utilizing tools from probability, stochastic geometry, and optimization. Each estimation problem is a challenging non-convex problem, and a coordinate descent algorithm is proposed to obtain a stationary point. Each proposed joint ML estimation extends the existing one for a single-cell network by considering the estimation of interference powers, together with the estimation of device activities. Each proposed joint MAP estimation further enhances the corresponding joint ML estimation by exploiting prior distributions of device activities and interference powers. The proposed joint ML estimation and joint MAP estimation under cooperative detection outperform the respective ones under non-cooperative detection at the costs of increasing backhaul burden, knowledge of network parameters, and computational complexities. Numerical results show the substantial gains of the proposed designs over well-known existing designs and reveal the importance of explicit consideration of inter-cell interference, the value of prior information, and the advantage of AP cooperation in device activity detection.

关键词： Estimation Maximum likelihood estimation Interference Intercell interference Channel estimation Matching pursuit algorithms approximation algorithms Massive machine-type communications (mMTC) grant-free massive access device activity detection inter-cell interference maximum likelihood (ML) estimation maximum a posterior probability (MAP) estimation

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Accelerated Log-Regularized Convolutional Transform Learning and its Convergence Guarantee

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第10期52卷 10785-10799页

作者： Li, Zhenni Zhao, Haoli Guo, Yongcheng Yang, Zuyuan Xie, Shengli Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Guangdong Univ Technol Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510275 Peoples R China 111 Ctr Intelligent Batch Mfg Based IoT Technol Guangzhou 510006 Peoples R China Guangdong HongKong Macao Joint Lab Smart Discrete Guangzhou 510006 Peoples R China

Convolutional transform learning (CTL), learning filters by minimizing the data fidelity loss function in an unsupervised way, is becoming very pervasive, resulting from keeping the best of both worlds: the benefit of unsupervised learning and the success of the convolutional neural network. There have been growing interests in developing efficient CTL algorithms. However, developing a convergent and accelerated CTL algorithm with accurate representations simultaneously with proper sparsity is an open problem. This article presents a new CTL framework with a log regularizer that can not only obtain accurate representations but also yield strong sparsity. To efficiently address our nonconvex composite optimization, we propose to employ the proximal difference of the convex algorithm (PDCA) which relies on decomposing the nonconvex regularizer into the difference of two convex parts and then optimizes the convex subproblems. Furthermore, we introduce the extrapolation technology to accelerate the algorithm, leading to a fast and efficient CTL algorithm. In particular, we provide a rigorous convergence analysis for the proposed algorithm under the accelerated PDCA. The experimental results demonstrate that the proposed algorithm can converge more stably to desirable solutions with lower approximation error and simultaneously with stronger sparsity and, thus, learn filters efficiently. Meanwhile, the convergence speed is faster than the existing CTL algorithms.

关键词： Convolution Convergence Acceleration Transforms Optimization Extrapolation approximation algorithms Convergence guarantee convolutional transform learning (CTL) extrapolation log-regularizer the difference of convex

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A Practical Diophantine approximation for Sparse Linear Array Direction Finding

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2021年第3期57卷 1405-1412页

作者： Sirianunpiboon, Songsri Ly, Peter Q. C. Elton, Stephen D. Def Sci & Technol Grp Edinburgh SA 5111 Australia

This article presents a computationally efficient, fixed complexity algorithm for estimating the angle of arrival of a radio frequency signal that impinges on a sparse linear array of sensors. The likelihood function for the bearing estimation problem is reduced to an inhomogeneous Diophantine approximation problem involving an unknown set of integer multiples of 2 pi. In solving for the integers, we propose a modification to the Cassels algorithm and generate an approximate linearized maximum-likelihood (ML) estimate of bearing. The number of iterations for convergence of the algorithm can be precomputed, which allows the algorithm to have fixed computational complexity. The proposed technique applies to a large class of array configurations, allowing the array configuration to be chosen to optimize estimation performance rather than to suit the algorithm. The simulation results show the performance of the proposed bearing estimation algorithm to be very close to that of ML estimation, but with a significantly reduced computational overhead.

关键词： approximation algorithms Maximum likelihood estimation Sensor arrays Sensors Linear antenna arrays Phased arrays Mathematical model

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APPROXIMATING MINIMUM REPRESENTATIONS OF KEY HORN FUNCTIONS*

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SIAM JOURNAL ON COMPUTING 2022年第1期51卷 116-138页

作者： Berczi, Kristof Boros, Endre Cepek, Ondrej Kucera, Petr Makino, Kazuhisa Eotvos Lorand Univ MTA ELTE Momentum Matroid Optimizat Res Grp Budapest Hungary Eotvos Lorand Univ MTA ELTE Egervary Res Grp Dept Operat Res Budapest Hungary Rutgers State Univ MSIS Dept New Brunswick NJ 08901 USA Rutgers State Univ RUTCOR New Brunswick NJ 08901 USA Charles Univ Prague Fac Math & Phys Dept Theoret Comp Sci & Math Log Prague Czech Republic Kyoto Univ Res Inst Math Sci RIMS Kyoto Japan

Horn functions form an important subclass of Boolean functions and appear in many different areas of computer science and mathematics as a general tool to describe implications and dependencies. Finding minimum sized representations for such functions with respect to most commonly used measures is a computationally hard problem admitting a 2log1-o(1) n inapproximability bound. In this paper we consider the natural class of key Horn functions representing keys of relational databases. For this class, the minimization problems for most measures remain NP-hard. In this paper we provide logarithmic factor approximation algorithms for key Horn functions with respect to all such measures.

关键词： Key words approximation algorithms directed hypergraphs Horn minimization implicational

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5G Private Network Deployment Optimization Based on RWSSA in Open-Pit Mine

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第8期18卷 5466-5476页

作者： Chang, Zhaozhao Gu, Qinghua Lu, Caiwu Zhang, Yanhong Ruan, Shunling Jiang, Song Xian Univ Architecture & Technol Sch Management Xian 710055 Peoples R China Xian Univ Architecture & Technol Sch Resources Engn Xian 710055 Peoples R China Luoyang Luanchuan Molybdenum Ind Grp Co Ltd Luoyang 471500 Peoples R China

5G communication network is vital to the intelligent and unmanned technology of open-pit mine, but the high cost of 5G base station deployment will bring huge cost increases to mining enterprises. Thus, this article proposes an improved sparrow search algorithm (SSA) using random walk strategy (RWSSA) to optimization the distribution and signal coverage of 5G base stations in open-pit mine. In order to verify the performance of the algorithm, the SSA, the modified ant lion optimizer, the particle swarm optimization, and the RWSSA were used to compare and analyze the accuracy and speed of the algorithm in solving high dimensionality benchmark functions, respectively. The dimensions used in the test are 30, 100, and 500 dimensions. After that, four algorithms were applied to a real example to optimize the deployment of 5G base stations, including macrobase stations and microbase stations. RWSSA has very good performance in terms of performance and practical application. Therefore, RWSSA is more suitable for the application of 5G base station distribution optimization in open-pit mines.

关键词： Heuristic algorithms Prediction algorithms Informatics approximation algorithms Wireless sensor networks Computer architecture 5G mobile communication 5G base station deployment open-pit mine random walk strategy signal coverage sparrow search algorithm (SSA)

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Optimizing Social Visibility in OSNs With Anonymity Guarantees: Efficient algorithms and Applications

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第4期9卷 2237-2251页

作者： Zheng, Shiyuan Xie, Hong Lui, John C. S. Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

Online social network (OSN) is an ideal venue to enhance one's visibility. This paper considers how a user (called requester) in an OSN selects a small number of users and invites them as new friends/followers so as to maximize her "social visibility". More importantly, the requester has to do this under the anonymity setting, which means she is not allowed to know the neighborhood information of other users in the OSN. In this paper, we first develop a mathematical model to quantify the social visibility and formulate the problem of visibility maximization with anonymity guarantee, abbreviated as "VisMAX-A". We prove that the VisMAX-A problem is NP-hard even with an "perfect query oracle" which knows users' neighborhood information. Then we design an algorithmic framework named as "AdaExp," which adaptively expands the requester's visible set in multiple rounds. In each round of the expansion, the algorithmic framework uses a query oracle with anonymity guarantee to select one user to increase the requester's visibility. We prove the theoretical guarantees of our algorithmic framework, which quantify how the query oracle may influence the solution guarantee. By using probabilistic data structures like the k-minimum values (KMV) sketch and online learning methods like multi-armed bandit (MAB), we design an efficient query oracle with anonymity guarantees to estimate the best candidate to increase requester's visibility. We also conduct experiments on real-world social networks and validate the effectiveness of our algorithms. Lastly, we demonstrate applications of our algorithm on real-world data and show that one can increase her social visibility significantly by only adding a few (e.g., one or two) friends/followers in the OSNs.

关键词： Social networking (online) Mathematical models approximation algorithms Estimation Computational complexity Boosting Advertising approximation algorithms KMV sketch multi-armed bandit social network social visibility

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Packing under convex quadratic constraints

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MATHEMATICAL PROGRAMMING 2022年第1-2期192卷 361-386页

作者： Klimm, Max Pfetsch, Marc E. Raber, Rico Skutella, Martin TU Berlin Inst Math Str 17 Juni 136 D-10623 Berlin Germany Tech Univ Darmstadt Dept Math Dolivostr 15 D-64293 Darmstadt Germany

We consider a general class of binary packing problems with a convex quadratic knapsack constraint. We prove that these problems are APX-hard to approximate and present constant-factor approximation algorithms based upon two different algorithmic techniques: a rounding technique tailored to a convex relaxation in conjunction with a non-convex relaxation, and a greedy strategy. We further show that a combination of these techniques can be used to yield a monotone algorithm leading to a strategyproof mechanism for a game-theoretic variant of the problem. Finally, we present a computational study of the empirical approximation of these algorithms for problem instances arising in the context of real-world gas transport networks.

关键词： approximation algorithms

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The Analysis of Optimization algorithms

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IEEE CONTROL SYSTEMS MAGAZINE 2022年第3期42卷 58-72页

作者： Lessard, Laurent Univ Wisconsin Elect & Comp Engn Madison WI USA Northeastern Univ Mech & Ind Engn Boston MA 02115 USA

Optimization problems in engineering and applied mathematics are typically solved in an iterative fashion, by systematically adjusting the variables of interest until an adequate solution is found. The iterative algorithms that govern these systematic adjustments can be viewed as a control system. In control systems, the output is measured and the input is adjusted using feedback to drive the error to zero. Similarly, in iterative algorithms, the optimization objective is evaluated and the candidate solution is adjusted to drive it toward the optimal point. Choosing an algorithm that works well for a variety of optimization problems is akin to robust controller design. Just as dissipativity theory can be used to analyze the stability properties of control systems, it can also be used to analyze the convergence properties of iterative algorithms. By defining an appropriate notion of “energy” that dissipates with every iteration of the algorithm, the convergence properties of the algorithm can be characterized. This article formalizes the connection between iterative algorithms and control systems and shows through examples how dissipativity theory can be used to analyze the performance of many classes of optimization algorithms. This control-theoretic viewpoint enables the selection and tuning of optimization algorithms to be performed in an automated and systematic way.

关键词： Computer science approximation algorithms Iterative algorithms Optimization Iterative methods Systematics Stability analysis

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Joint Resource Dimensioning and Placement for Dependable Virtualized Services in Mobile Edge Clouds

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2022年第10期21卷 3656-3669页

作者： Zhao, Peiyue Dan, Gyorgy KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Network & Syst Engn S-11428 Stockholm Sweden

Mobile edge computing (MEC) is an emerging architecture for accommodating latency sensitive virtualized services (VSs). Many of these VSs are expected to be safety critical, and will have some form of reliability requirements. In order to support provisioning reliability to such VSs in MEC in an efficient and confidentiality preserving manner, in this paper we consider the joint resource dimensioning and placement problem for VSs with diverse reliability requirements, with the objective of minimizing the energy consumption. We formulate the problem as an integer programming problem, and prove that it is NP-hard. We propose a two-step approximation algorithm with bounded approximation ratio based on Lagrangian relaxation. We benchmark our algorithm against two greedy algorithms in realistic scenarios. The results show that the proposed solution is computationally efficient, scalable and can provide up to 30 percent reduction in energy consumption compared to greedy algorithms.

关键词： approximation algorithms Resilience Process control Hardware Energy consumption Edge computing Cloud computing Resource allocation service placement Lagrangian relaxation mobile edge computing availability

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A Semidistributed Interior Point Algorithm for Optimal Coordination of Automated Vehicles at Intersections

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2022年第5期30卷 1977-1989页

作者： Hult, Robert Zanon, Mario Gros, Sebastien Falcone, Paolo Volvo Autonomous Solut S-41715 Gothenburg Sweden IMT Sch Adv Studies I-55100 Lucca Italy Norwegian Univ Sci & Technol Dept Engn Cybernet ITK N-7491 Trondheim Norway Univ Modena & Reggio Emilia Dipartimento Ingn Enzo Ferrari I-41121 Modena Italy

In this article, we consider the optimal coordination of automated vehicles at intersections under fixed crossing orders. We formulate the problem using direct optimal control and exploit the structure to construct a semidistributed primal-dual interior-point algorithm to solve it by parallelizing most of the computations. Differently from standard distributed optimization algorithms, where the optimization problem is split, in our approach we split the linear algebra steps, such that the algorithm takes the same steps as a fully centralized one, while still performing computations in a distributed fashion. We analyze the communication requirements of the algorithm, and propose an approximation scheme which can significantly reduce the data exchange. We demonstrate the effectiveness of the algorithm in hard but realistic scenarios, which show that the approximation leads to reductions in communicated data of almost 99% of the exact formulation, at the expense of less than 1% suboptimality.

关键词： Optimization Heuristic algorithms Collision avoidance Standards Vehicle dynamics Handheld computers approximation algorithms Distributed optimization intersection coordination model predictive control networked mobile systems

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