作者机构：
[Xu, Hongbo; Wang, Dong; Huang, Xingxing; Li, Ruijie; Chen, Yun; Zhang, Guoping] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan 430079, Peoples R China.
通讯机构：
[Zhang, GP ] C;Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan 430079, Peoples R China.
关键词：
Integrated sensing and communication (ISAC);Outage probability (OP);Power optimization;Non -orthogonal multiple access (NOMA)
摘要：
This paper investigates a framework for integrated sensing and communication (ISAC) based on non-orthogonal multiple access (NOMA). In this framework, a dual-function base station (BS) utilizes NOMA technology to send superimposed signals to various users, and this superimposed signal also acts on target sensing simultaneously. Considering the channel estimation error, ensuring the communication performance, and sensing performance requirements, a transmit power optimization problem of ISAC system using NOMA is studied. Specifically, in the statistical channel state information (CSI) error model, the total system communicate power is minimized while ensuring all single users' rate outage probability (OP) constraints and the requirements for the beampattern gains of all single radar targets. Unfortunately, the proposed problem is challenging to solve and non-convex. But we have devised a feasible way to deal with this problem. First, we use Bernstein inequality to transform the rate OP constraint, and this challenging non-convex problem is then successfully solved using a method based on semi-definite relaxation (SDR). The numerical outcomes demonstrate that the system's overall transmission power will increase due to the channel estimation error. The numerical findings also show that the ISAC system performs better with NOMA assistance than with OMA when comparing the NOMA and OMA schemes.
摘要：
As an novel paradigm, computation offloading in the mobile edge computing (MEC) system can effectively support the resource-intensive applications for the mobile devices (MD) equipped with limited computing capability. However, the hostile radio transmission and data leakage during the offloading process may erode the MEC system's potential. To tackle these hindrances, we investigate an IRS-assisted secure MEC system with eavesdroppers, where the intelligent reflecting surface (IRS) is deployed to enhance the communication between the MD and the AP equipped with edge servers and the malicious eavesdroppers may attack the wireless data offloaded by MD. The MD opt for offloading part of the tasks to the edge server for execution to support the computation-intensive applications. Moreover, the relevant latency minimization problem is formulated by optimizing the offloading ratio, the allocation of edge server computing capability, the multiple-user-detection (MUD) matrix and the IRS phase shift parameters, subject to the constraints of edge computation resource and practical IRS phase shifts. Then, the original problem is decouple into two subproblem, and the computing and communication subproblems are alternatively optimized by block coordinate descent (BCD) method with low complexity. Finally, simulation results demonstrate that the proposed scheme can significantly enhance the performance of secure offloading in the MEC system.(c) 2023 Elsevier B.V. All rights reserved.
作者机构：
[Xu, Hongbo; Zhu, Li; Li, Ruijie; Chen, Yun; Zhang, Guoping] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan 430079, Peoples R China.;[Zhu, Li] Hubei Minzu Univ, Coll Intelligent Syst Sci & Engn, Enshi 445000, Peoples R China.
通讯机构：
[Zhang, GP ] C;Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan 430079, Peoples R China.
摘要：
Intelligent reflective surface (IRS) and unmanned aerial vehicle (UAV) communication are two key technologies in the sixth generation of mobile communication (6G). In this paper, IRS is equipped on UAV to form aerial IRS, which can achieve 360° panoramic full-angle reflection and flexible deployment of IRS. In order to achieve high-quality and ubiquitous network coverage under data privacy and low latency requirements, we propose an Federated learning (FL) network via Over-the-Air computation (AirComp) in IRS-assisted UAV communications. Our goal is to minimize the worst-case mean square error (MSE) by jointly optimizing the IRS phase shift, denoising factor for noise suppression, the user's transmission power, and UAV trajectory. Optimizing and quickly adjusting the UAV position and IRS phase shift, it flexibly assists the signal transmission between users and base stations (BS). In order to solve this complex non-convex problem, we propose a low-complexity iterative algorithm, which divides the original problem into four sub-problems, respectively using the semi-definite programming (SDP) method, slack variable introduction method, successive convex approximation (SCA) method to solve each sub-problem. Through the analysis of simulation results, our proposed design scheme is obviously better than other benchmark schemes.
摘要：
Herein, we aimed to solve the problem of difficulty in filtering the noise components in the monitoring of strain on wind turbine blades using fiber bragg grating, a denoising method based on parameter-optimized variational mode decomposition (VMD) is proposed. This method uses the minimum envelope entropy as the fitness function and the slime mould algorithm for self-adaptive optimization to find the optimal combination of modal decomposition components K and the quadratic penalty factor alpha of VMD. The optimized VMD was used to decompose the strain data of wind turbine blades over time into K intrinsic mode components, and the noise mode was removed using the sample entropy to obtain the effective signal. The proposed method was compared to ensemble empirical mode decomposition and complementary ensemble empirical mode decomposition using the simulated signals and engineering data. The experimental results show that the proposed method can effectively remove noise from the strain data of wind turbine blades and has better denoising performance than the other two methods, which provides a reliable basis for analyzing the strain data of wind turbine blades.
摘要：
Federated learning (FL) is an emerging artificial intelligence (AI) basic technology. It is essentially a distributed machine learning (ML) that allows the client to perform model training locally and then upload the trained model parameters to the server while leaving the original data locally, which guarantees the client’s privacy and significantly reduces communication pressure. This paper combines non-orthogonal multiple access (NOMA) for optimizing bandwidth allocation and FL to study a novel energy-efficient FL system which can effectively reduce energy consumption under the premise of ensuring user privacy. The considered model uses clustering for transmission between clients and the base station (BS). NOMA is used inside the cluster to transmit information to BS, and frequency division multiple access (FDMA) is used between the clusters to eliminate the interference between the user clusters caused by the clustering. We combine communication and computing design to minimize the system’s total energy consumption. Since the optimization problem is non-convex, it is first transformed into a Lagrangian function, and the original problem is divided into three sub-problems. Then the Karush–Kuhn–Tucker (KKT) conditions and Successive Convex Approximation (SCA) method are used to solve each sub-problem. Simulation analysis shows that our proposed novel energy-efficient FL method design has significantly improved the performance compared with other benchmarks.
摘要：
The energy efficiency of the photovoltaic (PV) panel is greatly influenced by the dust deposition, especially in a PV plant covering a wide area. However, hitherto there has been a lack of a real-time measurement technique for the dust deposition degree. To solve this problem, in this paper, some evaluation indicators are defined, and then a real-time evaluation strategy is proposed. In order to calculate these indicators, a method and three simplification methods based on some established equations (or equation sets) are proposed to obtain the equation solution for the irradiance. This solved data is compared with the irradiance directly measured by the sensor, so the dust deposition degree can be successfully evaluated. Finally, some simulations and experiments verify the feasibility, availability and workability of the proposed irradiance solution methods and the designed evaluation strategy. The simulation and experiment results show that the errors of the solved irradiance values are always less than 2.5%. Meanwhile, these results also show that the real-time dust deposition degree can be successfully characterized by the defined indicators in this evaluation strategy, even if it is compared with other work. Therefore, this work is very beneficial to judge the energy efficiency of the PV system and plan the cleaning schedule of the PV panel.
作者机构：
[Luo, Wenxing; Chen, Yun; Ren, Yinshuan] Qiannan Normal Univ Nationalities, Sch Phys & Elect, Duyun 558000, Peoples R China.;[Zhang, Jianqiang; Xu, Hongbo; Chen, Yun; Zhang, Guoping] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan 430079, Peoples R China.
关键词：
Introduction;Materials and Methods;Results;Discussion;Conclusion;Abstract;Data Availability;Additional Points;Ethical Approval;Consent;Disclosure;Conflicts of Interests;Authors’ Contributions;Funding Statement;Acknowledgements;Acknowledgments;Supplementary Materials;Reference;Dataset Description;Dataset Files;Abstract;Introduction;Introduction and Materials;Introduction and Methods;Materials;Materials and Methods;Methods;Results;Discussion;Results and Discussion;Discussion and Conclusion;Results and Conclusion;Conclusion;Conclusions;Data Availability;Additional Points;Ethical Approval;Consent;Disclosure;Conflicts of Interest;Authors’ Contributions;Funding Statement;Acknowledgements;Supplementary Materials;References;Appendix;Abbreviations;Preliminaries;Introduction and Preliminaries;Notation;Proof of Theorem;Proofs;Analysis of Results;Examples;Numerical Example;Applications;Numerical Simulation;Model;Model Formulation;Systematic Palaeontology;Nomenclatural Acts;Taxonomic Implications;Experimental;Synthesis;Overview;Characterization;Background;Experimental;Theories;Calculations;Model Verification;Model Implementation;Geographic location;Study Area;Geological setting;Data Collection;Field Testing;Data and Sampling;Dataset;Literature Review;Related Works;Related Work;System Model;Methods and Data;Experimental Results;Results and Analysis;Evaluation;Implementation;Case Presentation;Case Report;Search Terms;Case Description;Case Series;Background;Limitations;Additional Points;Case;Case 1;Case 2 etc.;Concern Details;Retraction Details;Copyright;Related Articles
摘要：
Traditional wireless data aggregation (WDA) technology based on the principle of separated communication and computation is difficult to achieve large-scale access under the limited spectrum resources, especially in scenarios with strict constraints on time latency. As an outstanding fast WDA technology, over-the-air computation (AirComp) can reduce transmit time while improving spectrum efficiency. Most edge devices in wireless networks are battery-powered. Therefore, optimizing the transmit power of devices could prolong the life cycle of nodes and save the system power consumption. In this research, we aim to minimize the device transmit power subject to aggregation error constraint. Additionally, to improve the harsh wireless transmission environment, we use reconfigurable intelligent surface (RIS) to assist AirComp. To solve the presented nonconvex problem, we present a two-step solution method. Specifically, we introduce matrix lifting technology to transform the original problems into semidefinite programming problems (SDP) in the first step and then propose an alternate difference-of-convex (DC) framework to solve the SDP subproblems. The numerical results show that RIS-assisted communication can greatly save system power and reduce aggregation error. And the proposed alternate DC method is superior to the alternate semidefinite relaxation (SDR) method.
摘要：
Intelligent reflecting surface (IRS) has arisen as a promising technology to reconfigure the wireless propagation environment cost-effectively. Most of the existing works on IRS focused on the passive beamforming (PB) optimization and performance enhancement without considering the multiple inter-IRS links cooperation that did not reveal the full preponderance of the multi-IRS-assisted reconfigurable communication system. In this work, we investigate a double-IRS-assisted multiple input single output (MISO) downlink communication system with the active beamforming (AB) and the cooperative PB design in the absence of direct link. Taking both the double-reflection links and the single-reflection links into account, the AB at the base station (BS) and the cooperative PB at two IRSs are jointly optimized to maximize the weight sum rate (WSR) under the constraint of the transmit power. To tackle the problem, we propose the double-IRS-assisted fractional programming block coordinate descent (D-FPBCD) method to find the sub-optimal solution with low complexity. We first reconstruct the original issue as a tractable one by the closed-form fractional programming (FP) approach, then, the prox-linear block coordinate descent (BCD) and successive convex approximation (SCA) techniques are used to find the sub-optimal solution. Finally, simulation results demonstrate the effectiveness of the proposed double-IRS-assisted wireless communication scheme. (C) 2022 Elsevier B.V. All rights reserved.
作者机构：
[Xu, Hongbo; Li, Ruijie; Chen, Yun; Zhang, Guoping; Chen, Xue] Cent China Normal Univ, Dept Elect & Informat Engn, Wuhan 430000, Peoples R China.
通讯机构：
[Guoping Zhang] D;Department of Electronics and Information Engineering, Central China Normal University, Wuhan 430000, China
关键词：
Data security;Federated Learning;Optimization;Wireless Power Transfer
摘要：
Federal Learning (FL) is an emerging technology in the field of machine learning (ML). Compared with traditional ML, FL is an attractive method to deal with data security issues of the user-side. So that FL can realizes its full potential in terms of low latency and high energy efficiency (EE), this paper introduces a new framework: In the wireless communication network scenario, we propose an FL architecture based on Wireless Power Transfer (WPT). By combining WPT technology and FL, we can realize green wireless communication under the premise of ensuring user privacy. We formulate a joint calculation and communication optimization problem to optimize the latency of local calculation, uplink and downlink transmission without consuming user-side energy. The problem formulas listed according to the optimization problem are non-convex. They are first transformed into convex form, and then a low-complexity iterative algorithm is used to solve them optimally. Simulations show that our proposed FL method design has achieved a significant performance improvement over other benchmark tests. (C) 2022 Elsevier B.V. All rights reserved.
作者机构：
[Xu, Hongbo; Li, Ruijie; Chen, Yun; Zhang, Guoping; Chen, Xue] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan 430079, Peoples R China.;[Chen, Yun; Ren, Yinshuan] Qiannan Normal Univ Nationalities, Sch Phys & Elect, Duyun 558000, Peoples R China.
通讯机构：
[Zhang, G.] C;Central China Normal University, China
关键词：
Transmitters;Security;Optimization;Array signal processing;Wireless communication;Channel estimation;Uncertainty;Robust beamforming;hardware impairments;secure communication;power allocation
摘要：
In practice, hardware impairments (HIs) of communication systems are inevitable. In addition, perfect channel state information (CSI) is difficult to get due to channel estimation errors. In this letter, we design the power allocation strategy (PAS) at transmitters to optimize the worst-case secrecy rate for the wireless communication system with both norm-bounded channel uncertainty and HIs. The proposed problem is non-convex and difficult to solve. To solve this thorny problem, we use S-procedure, conservative approximation (CA), robust beamforming, and sequential convex approximation (SCA) to effectively convert the non-convex problem into a series of convex problems that are easy to solve. The numerical results demonstrate that the proposed PAS and robust beamforming method can offset the performance loss caused by HIs to a large extent.
期刊：
Wireless Personal Communications,2022年124(2):1841-1860 ISSN：0929-6212
通讯作者：
Zhang, Guoping
作者机构：
[Xu, Hongbo; Li, Ruijie; Chen, Yun; Zhang, Guoping; Chen, Xue] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan 430079, Peoples R China.
通讯机构：
[Zhang, Guoping] C;Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan 430079, Peoples R China.
关键词：
MEC;Binary offloading decision;Unsupervised deep learning;Auxiliary network
摘要：
Mobile edge computation (MEC) is a potential technology to reduce the energy consumption and task execution delay for tackling computation-intensive tasks on mobile device (MD). The resource allocation of MEC is an optimization problem, however, the existing large amount of computation may hinder its practical application. In this work, we propose a multiuser MEC framework based on unsupervised deep learning to reduce energy consumption and computation by offloading tasks to edge servers. The binary offloading decision and resource allocation are jointly optimized to minimize energy consumption of MDs under latency constraint and transmit power constraint. This joint optimization problem is a mixed integer nonconvex problem which result in the gradient vanishing problem in backpropagation. To address this, we propose a novel binary computation offloading scheme (BCOS), in which a deep neural network (DNN) with an auxiliary network is designed. By using the auxiliary network as a teacher network, the student network can obtain the lossless gradient information in joint training phase. As a result, the sub-optimal solution of the optimization problem can be acquired by the learning-based BCOS. Simulation results demonstrate that the BCOS is effective to solve the binary offloading problem by the trained network with low complexity.
作者机构：
[Xu, Hongbo; Li, Ruijie; Chen, Yun; Zhang, Guoping; Chen, Xue] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan 430079, Peoples R China.;[Chen, Yun; Ren, Yinshuan] Qiannan Normal Univ Nationalities, Sch Phys & Elect, Duyun 558000, Peoples R China.
通讯机构：
[Zhang, GP ] C;Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan 430079, Peoples R China.
摘要：
Intelligent reflecting surface (IRS) is a promising technology that can help wireless communications achieve efficient spectrum and energy efficiency. However, because of its weak signal processing ability, it is difficult to get ideal channel state information (CSI). Under the imperfect channel state information hypothesis, we investigate a device-to-device (D2D) offload network. And an IRS is used to help calculate offloading from one set of task-intensive users to another set of idle users. We aim to jointly optimize transmit beamforming and IRS phase shifts to minimize system transmit power while requiring each user's rate to meet the minimum rate constraint in the presence of channel errors. Unfortunately, the problem presented is nonconvex, and the imperfection of CSI makes it even more difficult to solve. Therefore, we apply the S-Procedure to convert the original problem to two effectively solvable semidefinite programming (SDP) subproblems and then solve them through the convex-concave procedure (CCP) algorithm and the alternate optimization method. Numerical results show the effectiveness of the algorithm and verify that the assistance of the IRS can greatly save the system transmit power.
作者机构：
[Zhang, Guoping; Li, Guoqing] Cent China Normal Univ, Coll Phys Sci & Technol, 152 Luoyu Rd, Wuhan 430079, Peoples R China.;[Zhang, Guoping; Li, Guoqing] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, 152 Luoyu Rd, Wuhan 430079, Peoples R China.;[Qin, Chanchan] Guizhou Normal Univ, Sch Big Data & Comp Sci, Guiyang 550025, Peoples R China.;[Qin, Chanchan] Guizhou Normal Univ, Dept Educ, Ctr RFID & WSN Engn, Guiyang 550025, Peoples R China.
关键词：
interactive image segmentation;Markov Decision Process (MDP);Deep Reinforcement Learning (DRL);inside point localization;Deep Q-Network (DQN)
摘要：
In the task of interactive image segmentation, the Inside-Outside Guidance (IOG) algorithm has demonstrated superior segmentation performance leveraging Inside-Outside Guidance information. Nevertheless, we observe that the inconsistent input between training and testing when selecting the inside point will result in significant performance degradation. In this paper, a deep reinforcement learning framework, named Inside Point Localization Network (IPL-Net), is proposed to infer the suitable position for the inside point to help the IOG algorithm. Concretely, when a user first clicks two outside points at the symmetrical corner locations of the target object, our proposed system automatically generates the sequence of movement to localize the inside point. We then perform the IOG interactive segmentation method for precisely segmenting the target object of interest. The inside point localization problem is difficult to define as a supervised learning framework because it is expensive to collect image and their corresponding inside points. Therefore, we formulate this problem as Markov Decision Process (MDP) and then optimize it with Dueling Double Deep Q-Network (D3QN). We train our network on the PASCAL dataset and demonstrate that the network achieves excellent performance.
