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Secure Communication Optimization in NOMA Systems With UAV-Mounted STAR-RIS

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2024年 19卷 2300-2314页

作者： Guo, Liang Jia, Jie Chen, Jian Wang, Xingwei Northeastern Univ Minist Educ Sch Comp Sci & Engn Engn Res Ctr Secur Technol Complex Network Syst Shenyang 110819 Peoples R China Northeastern Univ Minist Educ Key Lab Intelligent Comp Med Image Shenyang 110819 Peoples R China

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), as a revolutionary technique, can boost transmission security by controlling unfavorable environments for signal eavesdropping and reducing interference. Integrating unmanned aerial vehicles (UAVs) with STAR-RISs has generated considerable interest due to its enhanced deployment flexibility. However, developing secure communication capabilities using flying STAR-RIS remains an open issue. Therefore, this work investigates the secrecy energy efficiency (SEE) maximization problem for the uplink non-orthogonal multiple access (NOMA) systems, where the UAV-mounted STAR-RIS is employed against the eavesdroppers. Specifically, we consider the joint optimization of the power control, the transmission/reflection coefficients, and the UAV/STAR-RIS's placement for static and mobile scenarios. The problems are also subject to the minimum data rate requirements and the safety flight region. To tackle the intractable problems, we first adopt the iterative-based method to solve the problem under the static scenario. After that, we invoke the fractional programming and successive convex approximation methods to get the power control scheme, the semidefinite relaxation method to get the transmission/reflection (T/R) coefficients design, and the search-based method to obtain the UAV/STAR-RIS position. Extending to the mobile scenario, we adopt the double deep Q-network (DDQN) algorithm to learn the online UAV trajectory design policy from a long-term perspective. Numerical results unveil that: 1) the proposed iterative-based joint optimization algorithm for static scenarios achieves a near-optimal solution;2) the NOMA communications aided by the UAV-mounted STAR-RIS achieve significant SEE gain over the conventional reflection-only RIS and the fixed STAR-RIS cases;3) the DDQN-based algorithm for mobile scenario achieves a near-optimal solution and obtains a valuable performance gain over the

关键词： DDQN fractional programming non-orthogonal multiple access STAR-RISs successive convex approximation UAV trajectory design

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Passive Beamforming for Practical RIS-Assisted Communication Systems With Non-Ideal Hardware

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第11期73卷 17743-17748页

作者： Liu, Yiming Wang, Rui Han, Zhu Tongji Univ Dept Informat & Commun Engn Shanghai 201804 Peoples R China Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 201804 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

Reconfigurable intelligent surface (RIS) technology is a promising solution for enhancing the performance of existing wireless communication systems. To achieve its advantage of cost-effectiveness, there inevitably exist certain hardware impairments in the system, leading to severe performance degradation. Some existing works have considered such problems under transceiver hardware impairments but neglected the practical model of RIS. Moreover, their performance still leaves room for improvement. To address these issues, RIS passive beamforming is re-optimized in this paper to maximize spectrum efficiency, taking into account both transceiver hardware impairments and practical RIS model. We deal with the fractional structure of the problem by using the quadratic transform. The remaining sub-problems are addressed utilizing the penalty-based method and the difference-of-convex programming. Since all sub-problems have closed-form solutions, our method is highly efficient. Numerical results demonstrate the superiority of our method. Most importantly, our solution reduces unnecessary resource consumption in practical scenarios.

关键词： Hardware Array signal processing Wireless communication Transceivers Optimization Reconfigurable intelligent surfaces Transforms fractional programming hardware impairments passive beamforming practical phase shifts reconfigurable intelligent surface (RIS)

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Sensing-Aided Covert Communications: Turning Interference Into Allies

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第9期23卷 10726-10739页

作者： Wang, Xinyi Fei, Zesong Liu, Peng Zhang, J. Andrew Wu, Qingqing Wu, Nan Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

In this paper, we investigate the realization of covert communication in a general radar-communication cooperation system, which includes integrated sensing and communications as a special example. We explore the possibility of utilizing the sensing ability of radar to track and jam the aerial adversary target attempting to detect the transmission. Based on the echoes from the target, the extended Kalman filtering technique is employed to predict its trajectory as well as the corresponding channels. Depending on the maneuvering altitude of adversary target, two channel state information (CSI) models are considered, with the aim of maximizing the covert transmission rate by jointly designing the radar waveform and communication transmit beamforming vector based on the constructed channels. For perfect CSI under the free-space propagation model, by decoupling the joint design, we propose an efficient algorithm to guarantee that the target cannot detect the transmission. For imperfect CSI due to the multi-path components, a robust joint transmission scheme is proposed based on the property of the Kullback-Leibler divergence. The convergence behaviour, tracking MSE, false alarm and missed detection probabilities, and covert transmission rate are evaluated. Simulation results show that the proposed algorithms achieve accurate tracking. For both channel models, the proposed sensing-assisted covert transmission design is able to guarantee the covertness, and significantly outperforms the conventional schemes.

关键词： Sensors Radar Radar tracking Jamming Array signal processing Communication systems Target tracking Radar-communication cooperation system covert communication extended Kalman filter beamforming design fractional programming

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On multiobjective fractional programs with vanishing constraints

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RAIRO-OPERATIONS RESEARCH 2024年第6期58卷 4701-4716页

作者： Laha, Vivek Singh, Harsh Narayan Mohapatra, Ram Banaras Hindu Univ Inst Sci Dept Math Varanasi 221005 India Univ Cent Florida Dept Math Orlando FL 32816 USA

The aim of this article is to combine the study of fractional programming and mathematical programs with vanishing constraints for the first time in literature. This paper deals with multiobjective fractional programs with vanishing constraints (MFPVC) involving continuously differentiable functions. Necessary and sufficient optimality conditions are derived for a feasible point to be an efficient (or local efficient) solution of the (MFPVC). A parametric dual model has been formulated and duality results are established with the primal (MFPVC).

关键词： fractional programming multiobjective optimization vanishing constraints generalized convexity optimality conditions duality results

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Designing Sequence Set and Mismatched Filter Bank With Diagonal-Ridge-Type Doppler Tolerance via Relative Level Optimization

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 4412-4427页

作者： Wang, Tao Liang, Junli So, H. C. Gao, Hongwei Li, Yongkang Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China Beijing Inst Radio Measurement Beijing 100039 Peoples R China

To detect high-speed moving targets using multiple-transmitter systems, the transmit sequence set should possess Doppler tolerance performance similar to that of linear frequency modulated signal, characterized by a diagonal-ridge-type auto-ambiguity function (AF). Although they provide space and waveform diversities to enhance sensing performance compared to single-transmitter systems, the cross-AFs introduced by the multiple transmit sequences decrease the degrees-of-freedom (DOFs), and thus the sidelobe levels of both the auto- and cross- AFs are not sufficiently low. In this paper, we first establish the relative level (between AF sidelobe and mainlobe) -based fractional models to jointly design the transmit sequence set and mismatched filter bank with diagonal-ridge-type Doppler tolerance and low sidelobe AFs. Moreover, a novel similarity constraint between the sequence and mismatched filter is devised to flexibly adjust the receiver output loss. Together with the relative-level scheme, it also reduces the DOF loss incurred by the cross AFs. Furthermore, the resultant challenging sum-of-fraction formulations with nonlinear and nonconvex constraints are effectively tackled via fraction separation and decoupling. Finally, we extend our design to solve the joint design problem of sequence set and mismatched filter bank with low sidelobe level in correlation function. Numerical results demonstrate the excellent performance of the proposed methods.

关键词： Filter banks Doppler effect Correlation Receivers Optimization Propagation losses Measurement Sequence set mismatched filter bank diagonal-ridge-type doppler tolerance similarity constraint relative level fractional programming

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Solving fractional programming Problems based on Swarm Intelligence

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Journal of Industrial Engineering International 2014年第2期10卷 1-10页

作者： Raouf, Osama Abdel Hezam, Ibrahim M. Operations Research and DSS Department Menofia University Shebien El-koum Menofia 32511 Egypt Department of Mathematics and computer Faculty of Education Ibb University Ibb city Yemen

This paper presents a new approach to solve fractional programming Problems (FPPs) based on two different Swarm Intelligence (SI) algorithms. The two algorithms are: Particle Swarm Optimization, and Firefly Algorithm. The two algorithms are tested using several FPP benchmark examples and two selected industrial applications. The test aims to prove the capability of the SI algorithms to solve any type of FPPs. The solution results employing the SI algorithms are compared with a number of exact and metaheuristic solution methods used for handling FPPs. Swarm Intelligence can be denoted as an effective technique for solving linear or nonlinear, non-differentiable fractional objective functions. Problems with an optimal solution at a finite point and an unbounded constraint set, can be solved using the proposed approach. Numerical examples are given to show the feasibility, effectiveness, and robustness of the proposed algorithm. The results obtained using the two SI algorithms revealed the superiority of the proposed technique among others in computational time. A better accuracy was remarkably observed in the solution results of the industrial application problems. © 2014, The Author(s).

关键词： Firefly algorithm fractional programming Particle swarm optimization Swarm intelligence

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A dual-interval fractional energy systems optimization for Nova Scotia, Canada

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2025年 212卷

作者： Wang, Siyu Huang, Guohe Zhou, Xiong Tian, Chuyin Univ Regina Inst Energy Environm & Sustainable Communities Regina SK S4S 0A2 Canada Beijing Normal Univ Sch Environm State Key Joint Lab Environm Simulat & Pollut Cont Beijing 100875 Peoples R China Univ Waterloo Dept Geog & Environm Management Waterloo ON Canada

In real-world energy systems, uncertainties are diverse and complex. Simply turning multi-objective energy planning into a single objective often does not work. This approach relies on subjective or unrealistic assumptions and also overlooks the trade-offs among economic cost, energy efficiency, and environmental performance. In this study, we developed a dual-interval fractional energy systems programming (DFEP) method to optimize energy systems under multiple objectives and uncertainties, particularly in the form of interval- and dual- interval-fractional representations. The DFEP framework integrated mixed-integer linear programming with fractional and dual-interval programming. This allowed it to handle uncertain parameters expressed as dual- interval boundaries and trade-offs between costs and benefits. This method was applied to Nova Scotia's energy system in Canada. The goal was to support energy planning under greenhouse gas (GHG) emission policies. Eight scenarios were analyzed to evaluate electricity generation, primary energy consumption, facilities expansion, and imported electricity. The results suggested that renewable energy and imported electricity can effectively replace fossil fuels, leading to a significant reduction in GHG emissions. A higher share of clean electricity generation and subsequent decline in fossil fuel use demonstrated the feasibility of achieving zero GHG emissions in electricity generation by 2030. Additionally, current coal-fired and oil-fired plant capacities were sufficient to meet future energy demand. These findings provide valuable insights and robust support for decision-makers in developing sustainable energy policies and achieving GHG mitigation targets.

关键词： DFEP method Dual-interval programming Uncertainties fractional programming Renewable electricity Optimization

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Distributed Adaptive Signal Fusion for fractional Programs

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2025年 11卷 258-273页

作者： Musluoglu, Cem Ates Bertrand, Alexander Katholieke Univ Leuven Stadius Ctr Dynam Syst Signal Proc & Data Analyt Dept Elect Engn ESAT B-3001 Leuven Belgium Leuven AI KU Leuven Inst AI B-3001 Leuven Belgium

The distributed adaptive signal fusion (DASF) is an algorithmic framework that allows solving spatial filtering optimization problems in a distributed and adaptive fashion over a bandwidth-constrained wireless sensor network. The DASF algorithm requires each node to sequentially build a compressed version of the original network-wide problem and solve it locally. However, these local problems can still result in a high computational load at the nodes, especially when the required solver is iterative. In this paper, we study the particular case of fractional programs, i.e., problems for which the objective function is a fraction of two continuous functions, which indeed require such iterative solvers. By exploiting the structure of a commonly used method for solving fractional programs and interleaving it with the iterations of the standard DASF algorithm, we obtain a distributed algorithm with a significantly reduced computational cost compared to the straightforward application of DASF as a meta-algorithm. We prove convergence and optimality of this "fractional DASF" (F-DASF) algorithm and demonstrate its performance via numerical simulations.

关键词： Wireless sensor networks Optimization Iterative methods Convergence Indexes Linear programming Europe Artificial intelligence Reviews Information processing Distributed optimization distributed signal processing spatial filtering signal fusion wireless sensor networks fractional programming

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User Connection and Resource Allocation Optimization in Blockchain Empowered Metaverse Over 6G Wireless Communications

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第1期24卷 19-34页

作者： Qian, Liangxin Liu, Chang Zhao, Jun Nanyang Technol Univ NTU Coll Comp & Data Sci CCDS Singapore 639798 Singapore Nanyang Technol Univ NTU Grad Coll Singapore 639798 Singapore

The convergence of blockchain, Metaverse, and non-fungible tokens (NFTs) brings transformative digital opportunities alongside challenges like privacy and resource management. Addressing these, we focus on optimizing user connectivity and resource allocation in an NFT-centric and blockchain-enabled Metaverse in this paper. Through user work-offloading, we optimize data tasks, user connection parameters, and server computing frequency division. In the resource allocation phase, we optimize communication-computation resource distributions, including bandwidth, transmit power, and computing frequency, which are crucial for 6G mobile edge computing and communication environments. We introduce the trust-cost ratio (TCR), a pivotal measure combining trust scores from users' resources and server history with delay and energy costs. This balance ensures sustained user engagement and trust. The DASHF algorithm, central to our approach, encapsulates the Dinkelbach algorithm, alternating optimization, semidefinite relaxation (SDR), the Hungarian method, and a novel fractional programming technique from a recent IEEE JSAC paper. The most challenging part of DASHF is to rewrite an optimization problem as Quadratically Constrained Quadratic programming (QCQP) via carefully designed transformations, in order to be solved by SDR and the Hungarian algorithm. Extensive simulations validate the DASHF algorithm's efficacy, revealing critical insights for enhancing blockchain-Metaverse applications, especially with NFTs.

关键词： Blockchains Resource management Metaverse Servers Optimization Computational modeling Hidden Markov models blockchain fractional programming semidefinite relaxation resource allocation trust-cost ratio

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Pentagonal fuzzy fractional three-stage sustainable transportation network problem incorporating extension principle

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AIN SHAMS ENGINEERING JOURNAL 2025年第2期16卷

作者： Baranidharan, B. Murali, Gunji Bala Cao, Zechun Mahapatra, G. S. Natl Inst Technol Puducherry Dept Math Karaikal 609609 India Vellore Inst Technol Sch Mech Engn Vellore India Texas A&M Univ Dept Comp Sci San Antonio TX 78224 USA

This study presents a framework for addressing the fuzzy fractional three-stage transportation network (FFTSTN) in the context of sustainable conveyance. The proposed model considers the transportation costs and involved in moving goods from manufacturers to storage godowns, storage godowns to distributors, distributors to consumers. The primary aim of this study is to mitigate transportation expenses and transportation's environmental viability through reducing emissions. A generalized pentagonal fuzzy number (GPFN) is employed to address the uncertainty of parameters of the three-stage transportation network. The established a methodology to explore solutions for the FFTSTN model without de-fuzzifying GPFNs. Additionally, it explored the economic sustainability and future transportation prospects using the extension principle to the proposed FFTSTN system. This solution approach considers the nonlinearity of the objective function utilizing the Charnes-Cooper transformation technique for the problem. A numerical example shows applicability, validity, and comparison between the compromise solutions.

关键词： Extension principle fractional programming Pentagonal fuzzy Three-stage transportation Sustainability

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