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Energy-Efficient D2D-Aided Dual UAV Data Collection

Chinese Journal of Electronics 2025年第1期34卷 176-185页

作者： Qiulei Huang Wei Wang Zhaohui Song Nan Zhao School of Information and Communication Engineering Dalian University of Technology Space Information Research Institute Hangzhou Dianzi University

Aided by device-to-device(D2D) connections, unmanned aerial vehicle(UAV) can significantly enhance the coverage of wireless communications. In this paper, we consider a data collection system with the assistance of D2D, where two fixed-wing UAVs as aerial base stations cooperatively serve the ground devices. To accommodate more devices, we propose two effective algorithms to establish the multi-hop D2D connections. Then, the user scheduling, UAV trajectory, and transmit power are jointly optimized to maximize the energy efficiency, which is a non-convex problem. Accordingly, we decompose it into three subproblems. The scheduling optimization is first converted into a linear programming. Then, the trajectory design and the transmit power optimization are reformulated as two convex problems by the Dinkelbach method. Finally, an iterative algorithm is proposed to effectively solve the original problem. Simulation results are presented to verify the effectiveness of the proposed scheme.

关键词： Wireless communication Simulation Data collection Autonomous aerial vehicles Linear programming Energy efficiency Trajectory Device-to-device communication Iterative methods Optimization

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Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid Massive MIMO Receive Array

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Chinese Journal of Electronics 2024年第1期33卷 175-184页

作者： Xichao ZHAN Zhongwen SUN Feng SHU Yiwen CHEN Xin CHENG Yuanyuan WU Qi ZHANG Yifan LI Peng ZHANG School of Information and Communication Engineering Hainan University School of Electronic and Optical Engineering Nanjing University of Science and Technology

For a sub-connected hybrid multiple-input multiple-output(MIMO) receiver with K subarrays and N antennas, there exists a challenging problem of how to rapidly remove phase ambiguity in only single time-slot. A direction of arrival(DOA) estimator of maximizing received power(Max-RP) is proposed to find the maximum value of K-subarray output powers, where each subarray is in charge of one sector, and the center angle of the sector corresponding to the maximum output is the estimated true DOA. To make an enhancement on precision, Max-RP plus quadratic interpolation(Max-RP-QI) method is designed. In the proposed Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three DOA values corresponding to the largest three receive powers of *** achieve the Cramer Rao lower bound, a Root-MUSIC plus Max-RP-QI scheme is developed. Simulation results show that the proposed three methods eliminate the phase ambiguity during one time-slot and also show low computational complexities. The proposed Root-MUSIC plus Max-RP-QI scheme can reach the Cramer Rao lower bound,and the proposed Max-RP and Max-RP-QI are still some performance losses 2–4 d B compared to the Cramer Rao lower bound.

关键词： Hybrid analog and digital Direction of arrival Massive hybrid multiple-input multiple-output Phase ambiguity Low computational complexity

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Intelligent secure near-field communication

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Science China(information Sciences) 2024年第9期67卷 340-341页

作者： Nan ZHANG Jifa ZHANG Chengwen XING Na DENG Nan ZHAO School of Information and Communication Engineering Dalian University of Technology School of Information and Electronics Beijing Institute of Technology

The research and development efforts for the sixth generation(6G) wireless communication are underway to fulfill the growing demands for wireless communications. 6G is expected to become the key enabler for diverse new applications and services [1]. To achieve the excellent performance, 6G will implement extremely large-scale antenna arrays(ELAAs), terahertz [2], and new types of antennas with metamaterials, leading to a paradigm shift for the electromagnetic characteristics.

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A Morris-Lecar model with novel fast-slow structure and its FPGA implementation

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Science China Technological Sciences 2025年第5期68卷 215-225页

作者： Xihong YU Lilian HUANG Han BAO Bocheng BAO College of Information and Communication Engineering Key Laboratory of Advanced Marine Communication and Information Technology (Ministry of Industry and Information Technology)National Key Laboratory of Underwater Acoustic TechnologyHarbin Engineering University Wang Zheng School of Microelectronics Changzhou University

The Morris-Lecar model is a neuronal model designed to replicate the oscillatory activity of barnacle giant muscle fibers. This work presents a three-dimensional Morris-Lecar model with a novel fast-slow structure. The model introduces a current containing slow variables, thereby forming a new fast-slow structure with the membrane potential. Numerical results reveal the existence of two independent unstable foci and complex dynamical behaviors that include period-doubling bifurcations and spiking/bursting activities. Two types of bifurcation mechanisms for bursting activities are elucidated through theoretical analysis based on four typical bursting activities. Finally, a digital neuronal circuit was developed based on FPGA, and experimental results show good consistency with numerical results.

关键词： Morris-Lecar model fast-slow structure firing activity bifurcation mechanism digital neuronal circuit

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A Multi-Condition Sequential Network Ensemble for Industrial Energy Storage Prediction Considering the Condition Switching Characteristics

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 369-380页

作者： Tianyu Wang Fan Zhou Yangjie Wu Jun Zhao Wei Wang IEEE the School of Control Science and Engineering Dalian University of Technology the School of Information Science and Engineering Dalian Polytechnic University

As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system scheduling. Considering that the continuous switching of the pressure and valve status(mechanism knowledge) would bring about multiple working conditions of the equipment, a multi-condition time sequential network ensembled method is proposed. In order to especially consider the time dependence of different conditions, a centralwise condition sequential network is developed, where the network branches are specially designed based on the condition switching sequences. A branch combination transfer learning strategy is developed to tackle the sample imbalance problem of different condition data. Since the condition or status data are real-time information that cannot be recognized during the prediction process, a pre-trained and ensemble learning approach is further proposed to fuse the outputs of the multi-condition networks and realize a transient-state involved prediction. The performance of the proposed method is validated on practical energy data coming from a domestic steel plant, comparing with the state-of-the-art algorithms. The results show that the proposed method can maintain a high prediction accuracy under different condition switching cases, which would provide effective guidance for the optimal scheduling of the industrial energy systems.

关键词： Ensemble learning industrial energy prediction multi-working condition structural deep network transfer learning

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Masked Autoencoders as Single Object Tracking Learners

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Computers, Materials & Continua 2024年第7期80卷 1105-1122页

作者： Chunjuan Bo XinChen Junxing Zhang School of Information and Communication Engineering Dalian Minzu UniversityDalian116600China School of Information and Communication Engineering Dalian University of TechnologyDalian116024China

Significant advancements have beenwitnessed in visual tracking applications leveragingViT in recent years,mainly due to the formidablemodeling capabilities of Vision Transformer(ViT).However,the strong performance of such trackers heavily relies on ViT models pretrained for long periods,limitingmore flexible model designs for tracking *** address this issue,we propose an efficient unsupervised ViT pretraining method for the tracking task based on masked autoencoders,called *** pretraining,we employ two shared-parameter ViTs,serving as the appearance encoder and motion encoder,*** appearance encoder encodes randomly masked image data,while the motion encoder encodes randomly masked pairs of video ***,an appearance decoder and a motion decoder separately reconstruct the original image data and video frame data at the pixel *** this way,ViT learns to understand both the appearance of images and the motion between video frames *** results demonstrate that ViT-Base and ViT-Large models,pretrained with TrackMAE and combined with a simple tracking head,achieve state-of-the-art(SOTA)performance without additional ***,compared to the currently popular MAE pretraining methods,TrackMAE consumes only 1/5 of the training time,which will facilitate the customization of diverse models for *** instance,we additionally customize a lightweight ViT-XS,which achieves SOTA efficient tracking performance.

关键词： Visual object tracking vision transformer masked autoencoder visual representation learning

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Weighted sum power maximization for STAR-RIS-aided SWIPT systems with nonlinear energy harvesting

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Science China(information Sciences) 2024年第10期67卷 343-358页

作者： Weiping SHI Cunhua PAN Feng SHU Yongpeng WU Jiangzhou WANG Yongqiang BAO Jin TIAN School of Network and Communication Nanjing Vocational College of Information Technology National Mobile Communications Research Laboratory Southeast University School of Information and Communication Engineering Hainan University School of Electronic and Optical Engineering Nanjing University of Science and Technology Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University School of Engineering University of Kent School of Information and Communication Engineering Nanjing Institute of Technology Engineering School of Networks and Telecommunications Jinling Institute of Technology

The conventional reconfigurable intelligent surface(RIS) is limited to reflecting incident signals,thereby imposing constraints on the placement of the transmitter and receiver, which hinders achieving comprehensive signal coverage across an entire area. This paper investigates a simultaneously transmitting and reflecting(STAR)-RIS-aided simultaneous wireless information and power transfer(SWIPT) system with a nonlinear energy harvesting model under three different RIS transmission protocols: energy splitting(ES),time switching(TS), and mode switching(MS). The objective of this paper is to maximize the weighted sum power(WSP) of all energy harvesting receivers(EHRs) while ensuring fairness in the collected power among them. This is achieved by jointly optimizing the transmit beamforming at the base station(BS)and the transmission and reflection coefficients at the STAR-RIS, subject to rate constraints for information decoding receivers(IDRs), transmit power constraint at the BS, and coefficient constraints of each element at the STAR-RIS corresponding to the three protocols. Solving this optimization problem poses challenges because of the complicated objective function and numerous coupled optimization variables of the ES STAR-RIS. To address this complexity, an efficient alternating optimization(AO) approach is proposed as an iterative solution method that achieves suboptimal results. The AO algorithm is then extended to MS STAR-RIS and TS STAR-RIS. Specifically, for MS STRA-RIS, binary constraints in the STAR-RIS coefficient optimization subproblem are handled using the first-order approximation technique along with the penalty function method. For TS STAR-RIS, apart from optimizing BS transmit beamforming and STAR-RIS coefficients subproblems, the transmission and reflection time allocation of STAR-RIS also needs *** findings demonstrate that compared to conventional RIS-aided systems, utilizing three different protocols in a STAR-RIS-aided sy

关键词： STAR-RIS beamforming SWIPT alternating optimization nonlinear energy harvesting

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Intelligent integrated sensing and communication:a survey

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Science China(information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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Low rank optimization for efficient deep learning:making a balance between compact architecture and fast training

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Journal of Systems engineering and Electronics 2024年第3期35卷 509-531,F0002页

作者： OU Xinwei CHEN Zhangxin ZHU Ce LIU Yipeng School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Deep neural networks(DNNs)have achieved great success in many data processing ***,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power *** this paper,we focus on low-rank optimization for efficient deep learning *** the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network *** the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast *** model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,*** a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and *** addition to summary of recent technical advances,we have two findings for motivating future *** is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network *** other is a spatial and temporal balance for tensorized neural *** accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.

关键词： model compression subspace training effective rank low rank tensor optimization efficient deep learning

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Scale effect removal and range migration correction for hypersonic target coherent detection

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Journal of Systems engineering and Electronics 2024年第1期35卷 14-23页

作者： WU Shang SUN Zhi JIANG Xingtao ZHANG Haonan DENG Jiangyun LI Xiaolong CUI Guolong School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

The detection of hypersonic targets usually confronts range migration(RM)issue before coherent integration(CI).The traditional methods aiming at correcting RM to obtain CI mainly considers the narrow-band radar ***,with the increasing requirement of far-range detection,the time bandwidth product,which is corresponding to radar’s mean power,should be promoted in actual ***,the echo signal generates the scale effect(SE)at large time bandwidth product situation,influencing the intra and inter pulse integration *** eliminate SE and correct RM,this paper proposes an effective algorithm,i.e.,scaled location rotation transform(ScLRT).The ScLRT can remove SE to obtain the matching pulse compression(PC)as well as correct RM to complete CI via the location rotation transform,being implemented by seeking the actual rotation *** to the traditional coherent detection algorithms,Sc LRT can address the SE problem to achieve better detection/estimation *** last,this paper gives several simulations to assess the viability of ScLRT.

关键词： hypersonic target detection coherent integration(CI) scale effect(SE)removal range migration(RM)correction scaled location rotation transform(ScLRT)

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