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Sum Rate Maximization for Movable Antenna Enhanced Multiuser Covert Communications

IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第3期14卷 611-615页

作者： Mao, Haobin Pi, Xiangyu Zhu, Lipeng Xiao, Zhenyu Xia, Xiang-Gen Zhang, Rui Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beihang Univ State Key Lab CNS ATM Beijing 100191 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117583 Singapore Univ Delaware Dept Elect & Comp Engn Newark NJ 19716 USA Chinese Univ Hong Kong Shenzhen Shenzhen Res Inst Big Data Sch Sci & Engn Shenzhen 518172 Guangdong Peoples R China

In this letter, we propose to employ movable antenna (MA) to enhance covert communications with noise uncertainty, where the confidential data is transmitted from an MA-aided access point (AP) to multiple users with a warden attempting to detect the existence of the legal transmission. To maximize the sum rate of users under covertness constraint, we formulate an optimization problem to jointly design the transmit beamforming and the positions of MAs at the AP. To solve the formulated non-convex optimization problem, we develop a block successive upper-bound minimization (BSUM) based algorithm, where the proximal distance algorithm (PDA) and the successive convex approximation (SCA) are employed to optimize the transmit beamforming and the MAs' positions, respectively. Simulation results show that the proposed MAs-aided system can significantly increase the covert sum rate via antenna position optimization as compared to conventional systems with fixed-position antennas (FPAs).

关键词： Array signal processing Optimization Vectors Linear antenna arrays Uncertainty Radio frequency Physical layer security Communication system security Arrays approximation algorithms Movable antennas (MAs) covert communications beamforming antenna position optimization

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Truthful Auction-Based Resource Allocation Mechanisms With Flexible Task Offloading in Mobile Edge Computing

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第5期23卷 6377-6391页

作者： Wang, Xueyi Wu, Dongkuo Wang, Xingwei Zeng, Rongfei Ma, Lianbo Yu, Ruiyun Northeastern Univ Coll Software Shenyang 110819 Liaoning Peoples R China Northeastern Univ Coll Comp Sci & Engn Shenyang 110819 Liaoning Peoples R China

Mobile edge computation (MEC) has recently emerged as a promising computing paradigm for supporting latency-sensitive mobile applications. Due to the limited resources of the edge servers (ESs), efficient resource allocation mechanisms are key to realize the MEC paradigm. In such a resource allocation process, it is a significant challenge to guarantee truthfulness while enabling flexible task offloading and satisfying the locality constraint. To address such a challenge, we propose a truthful auction-based resource allocation mechanism with flexible task offloading (TARFO) in an MEC system. Specifically, we first design the minimum delay task graph partitioning algorithm, aiming at calculating the minimum completion time and the task offloading solutions under different resource profiles. Based on this algorithm, for each smart mobile device (SMD), we further determine the set of feasible non-dominated resource profiles and the corresponding task offloading solutions. We next propose an efficient primal-dual approximation winning bid selection algorithm to determine the set of the winning bids and a critical value based pricing algorithm to calculate the payments of the winning bids. Strict theoretical analysis demonstrates TARFO can ensure truthfulness, individual rationality, computational efficiency and a smaller approximation ratio. Simulation results verify the effectiveness and efficiency of TARFO.

关键词： Task analysis Resource management Delays approximation algorithms Cost accounting Computational modeling Pricing Mobile edge computing resource allocation task offloading auction truthfulness

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A Task Offloading Method Based on User Satisfaction in C-RAN With Mobile Edge Computing

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第4期23卷 3452-3465页

作者： Yang, Qingyong Chu, Shu-Chuan Hu, Chia-Cheng Kong, Lingping Pan, Jeng-Shyang Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Shandong Peoples R China Yango Univ Coll Artificial Intelligence Fuzhou 350015 Fujian Peoples R China VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava 70032 Czech Republic Chaoyang Univ Technol Dept Informat Management Taichung 413 Taiwan

With the continuous development of the communication service industry, users pay more attention to the quality of network service. Previous studies on offloading problems, especially in the Cloud Radio Access Network (C-RAN) architecture with Mobile Edge Computing (MEC), are primarily focused on the economic perspective, with little consideration given to user-oriented satisfaction problems. To fill this gap, this article proposes a mathematical model for maximizing user satisfaction in the C-RAN architecture with multi-layer MEC. The problem is divided into two stages for solution. The first stage addresses the optimal connection problem between users and Remote Radio Heads (RRHs). The second stage then schedules user tasks reasonably based on the solution obtained in the first stage. The two-stage problems are all proved to be NP-Hard. Two efficient approximation algorithms, namely User-to-RRH Association Algorithm (URAA) and Maximum Satisfaction Algorithm (MSA), are proposed to solve the problems in different stages. This article proves and analyzes the theoretical performance of the two algorithms. Finally, the performance of the proposed algorithms is verified by simulation experiments. The experimental results demonstrate that the two proposed algorithms can achieve reasonable solutions to the problems, and the user satisfaction level can be maintained at a high level.

关键词： Computer architecture Task analysis approximation algorithms Mobile handsets Servers Mathematical models Delays approximation algorithm cloud radio access network mobile edge computing task offloading user statisfaction

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Event-Triggered Resilient Strategy for Distributed Optimization With Unreliable Agents

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第1期11卷 913-925页

作者： Chen, Zhao Nian, Xiaohong Meng, Qing Cent South Univ Sch Automat Changsha 410075 Peoples R China

In this article, we investigate the distributed optimization problem in the presence of unreliable agents who transmit unfavorable state information to their neighbors, resulting in misbehavior among cooperative agents. This gives rise to untrustworthy interactive information between agents. To overcome this challenge, we propose observer-based and sample-and-hold-based event-triggered resilient optimization strategies, complemented by threshold-based detection and isolation strategies. These strategies enhance the system's robustness against unreliable agents and enable cooperative agents to identify and disregard interaction information from unreliable neighbors. The system exhibits right-discontinuity due to intermittently changing communication networks and event-triggered time sequences. To analyze the convergence of nonsmooth systems, we integrate Filippov's differential inclusions with Lyapunov stability theory. Finally, we validate the efficacy of the proposed algorithms through comprehensive simulation results.

关键词： Cost function Convergence Communication networks approximation algorithms Topology Robustness Heuristic algorithms Distributed optimization unreliable agents resilient optimization strategy event-triggered

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IoT Fog Computing Optimization Method Based on Improved Convolutional Neural Network

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IEEE ACCESS 2024年 12卷 2398-2408页

作者： Jing, Bing Xue, Huimin Shanxi Vocat & Tech Coll Finance & Trade Dept Internet Things Technol Taiyuan 030031 Peoples R China

The rapid development of communication technology has promoted the development of the Internet of Things technology. It has resulted in a scarcity of computing resources for the Internet of Things devices, and limited the further development of the Internet of Things. In order to improve the utilization efficiency of the system resources for the Internet of Things devices and promote the further development of the Internet of Things, the continuous Markov decision process model is constructed. The value function approximation algorithm of the convolutional neural network is used to solve the problem. Continuous Markov decision process model is an excellent single-user decision process model, but not optimal for multi-user systems. Using convolutional neural network to solve the value function of continuous Markov decision process model, so that it can be applied to multi-user system. The results show that the average algorithm has growth rates of 0.48 and 0.84, respectively, in comparison to the other two algorithms. The average arrival rate has the least effect on the average delay of the value function approximation algorithm and the greatest influence on its power consumption. With the average arrival rate, the average delay of the algorithm increased by 0.25S and the power consumption by 0.27W. The effectiveness of the value function approximation algorithm based on convolutional neural network surpasses that of the multi-user multi-task offloading algorithm and the queue-aware algorithm, thus applying continuous Markov decision process models to multi-user systems. The study combines the continuous Markov decision process model with the resource decision of IOT devices, resulting in optimized resource scheduling decisions and improved utilization efficiency of IOT devices.

关键词： Internet of Things Computational modeling Edge computing Task analysis approximation algorithms Processor scheduling Markov processes MDP CNN value function IoT fog computing

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On Intelligent Placement Decision-Making algorithms for Wireless Digital Twin Networks via Bandit Learning

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第6期73卷 8889-8902页

作者： Gu, Jialiang Fu, Yaru Hung, Kevin Hong Kong Metropolitan Univ Sch Sci & Technol Hong Kong 999077 Peoples R China

In this work, we present a novel approach to address the problem of minimizing the total cost in wireless digital twin network (WDTN) through the design of an intelligent digital twins (DTs) placement algorithm. The total cost is defined as the weighted sum of latency and energy consumption, evaluating the importance of these two factors. The dynamics in the WDTN require an adaptive and flexible approach to perform DT placement over time. To efficiently tackle the non-convex and non-stationary optimization problem for DT placement, we develop a multi-armed bandit (MAB) driven algorithm. Initially, we remodel the original problem of DT placement as a variation of the budgeted-MAB problem to enhance the efficiency of resources utilization within a fixed budget. Subsequently, we exploit the contextual information about the DTs and edge servers to establish associations across different time slots, effectively addressing the non-stationary nature of the problem. To further make the DT placement decisions and reduce uncertainty of the placement strategy, we propose an extension of the upper confidence bound (UCB) strategy. This extension incorporates both the efficiency of resources utilization and contextual information of the WDTN while considering energy consumption, latency, and other relevant factors. Extensive and comprehensive numerical simulations are conducted to evaluate the performance of our devised algorithm. The results demonstrate the superiority of the proposed method in terms of energy consumption, latency, and total cost when compared to various baseline schemes. These findings highlight the effectiveness and robustness of our developed algorithm in achieving significant improvements in cost reduction for WDTN.

关键词： Servers Costs Heuristic algorithms Vehicle dynamics Optimization Digital twins approximation algorithms Cost minimization DT placement multi-arm bandit context information limited resource

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Approximating Long Cycle Above Dirac's Guarantee

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ALGORITHMICA 2024年第8期86卷 2676-2713页

作者： Fomin, Fedor V. Golovach, Petr A. Sagunov, Danil Simonov, Kirill Univ Bergen Dept Informat Bergen Norway VA Steklov Inst Math St Petersburg Dept St Petersburg Russia Univ Potsdam Hasso Plattner Inst Potsdam Germany

Parameterization above (or below) a guarantee is a successful concept in parameterized algorithms. The idea is that many computational problems admit "natural" guarantees bringing to algorithmic questions whether a better solution (above the guarantee) could be obtained efficiently. For example, for every boolean CNF formula on m clauses, there is an assignment that satisfies at least m/2 clauses. How difficult is it to decide whether there is an assignment satisfying more than m / 2 + k \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$m/2 +k$$\end{document} clauses? Or, if an n-vertex graph has a perfect matching, then its vertex cover is at least n/2. Is there a vertex cover of size at least n / 2 + k \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n/2 +k$$\end{document} for some k >= 1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k\ge 1$$\end{document} and how difficult is it to find such a vertex cover? The above guarantee paradigm has led to several exciting discoveries in the areas of parameterized algorithms and kernelization. We argue that this paradigm could bring forth fresh perspectives on well-studied problems in approximation algorithms. Our example is the longest cycle problem. One of the oldest results in extremal combinatorics is the celebrated Dirac's theorem from 1952. Dirac's theorem provides the following guarantee on the length of the longest cycle: for every 2-connected n-vertex graph G with minimum degree

关键词： Longest path Longest cycle approximation algorithms Above guarantee parameterization Minimum degree Dirac theorem

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Map Matching Queries on Realistic Input Graphs Under the Frechet Distance

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ACM TRANSACTIONS ON algorithms 2024年第2期20卷 1-33页

作者： Gudmundsson, Joachim Seybold, Martin P. Wong, Sampson Univ Sydney Sydney NSW Australia Univ Vienna Vienna Austria Univ Copenhagen Copenhagen Denmark

Map matching is a common preprocessing step for analysing vehicle trajectories. In the theory community, the most popular approach for map matching is to compute a path on the road network that is the most spatially similar to the trajectory, where spatial similarity is measured using the Frechet distance. A shortcoming of existing mapmatching algorithms under the Frechet distance is that every time a trajectory is matched, the entire road network needs to be reprocessed from scratch. An open problem is whether one can preprocess the road network into a data structure, so that map matching queries can be answered in sublinear time. In this article, we investigate map matching queries under the Frechet distance. We provide a negative result for geometric planar graphs. We show that, unless SETH fails, there is no data structure that can be constructed in polynomial time that answers map matching queries in O((pq)(1-delta)) query time for any delta > 0, where p and q are the complexities of the geometric planar graph and the query trajectory, respectively. We provide a positive result for realistic input graphs, which we regard as the main result of this article. We show that for c-packed graphs, one can construct a data structure of (O) over tilde (cp) size that can answer (1+epsilon)-approximate map matching queries in (O) over tilde (c(4)q log(4) p) time, where (O) over tilde (center dot) hides lower-order factors and dependence on epsilon.

关键词： Computational geometry approximation algorithms map matching Frechet distance

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Switching Games for Image Compression

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1016-1020页

作者： Huhtanen, Marko Univ Oulu Fac Informat Technol & Elect Engn Oulu 090570 Finland

To compress an image, a technique based on optimal scalings with diagonal matrices is described. To start the process, an initial image of high compression ratio is required. Such an image can be produced, for example, with the 2D FFT or 2D DCT of the original image. Principal component analysis is a special case of this compression technique where the initial image is extremely rough, consisting of the first 2D Fourier basis function only. This initial image is then optimally orthogonalized and expanded by iteratively applying diagonal matrices from the left and right to attain double orthogonality. The process can be viewed as a continuous version of Berlekamp's switching game.

关键词： Image coding Switches Principal component analysis Discrete cosine transforms Games Eigenvalues and eigenfunctions approximation algorithms Solids Signal processing algorithms Matrix decomposition Lossy compression double orthogonality principal component analysis 2D FFT 2D DCT

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Low-Cost Yet Effective Trojan Mitigation Techniques for Approximate Systems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2025年第4期72卷 603-607页

作者： Tariq, Zohaib Waris, Haroon Ahmed, Rehan Dou, Yuqin Liu, Weiqiang Imran, Muhammad Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Rawalpindi 44000 Pakistan Inst Space Technol Elect Engn Dept Islamabad 44000 Pakistan DreamBig Semicond Hardware Engn San Jose CA USA Nanjing Univ Posts & Telecommun Coll Integrated Circuit Sci & Engn Nanjing 210023 Peoples R China Nanjing Univ Aeronaut & Astronaut NUAA Coll Elect & Informat Engn Nanjing 211106 Peoples R China

Approximate Computing (AC) is a promising computing paradigm that provides efficiency and speed while maintaining acceptable accuracy in error-tolerant applications. However, most of the existing AC designs are prone to Hardware Trojan (HT) attacks. Moreover, the existing HT mitigation techniques have been designed for exact systems and due to the architectural differences in the approximate systems, these conventional mitigation techniques have limited efficacy when used in the context of AC. Therefore, this brief presents two novel methods, named Partial Comparison (PC) and Hybrid Comparison (HC), that effectively mitigate HTs in approximate systems while achieving a 30% and 55% improvement in the area and the power efficiency, compared to the existing state-of-the-art technique. PC focuses on LSBs while HC considers both LSBs and MSBs for HT masking. The effectiveness of proposed techniques is validated through a practical case study of image processing;three IPs with different HTs are successfully masked with a PSNR and SSIM of 33dB and 0.92 respectively. In conclusion, this research provides practical solution to enhance security and reliability of approximate systems with optimal performance benefits.

关键词： Trojan horses Prevention and mitigation IP networks Supply chains Hardware security Costs approximation algorithms Power demand Physical unclonable function Payloads Approximate computing hardware trojan partial comparison hybrid comparison security reliability

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