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Energy-Efficient Bandwidth and Power Allocation in Relay-Assisted Multi-Layer Heterogeneous Networks with Energy Harvesting

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Journal of Shanghai Jiaotong university(Science) 2023年第6期28卷 822-830页

作者：高锦程赵宜升陈加法陈忠辉 Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information EngineeringFuzhou UniversityFuzhou 350108China

Aiming at excessive users existing in a pico base station(PBS)in the multi-layer heterogeneous networks,the resource allocation problem of maximizing the energy efficiency of the networks is investigated in this *** deploying a relay node with energy harvesting function,the data of some users in the PBS can be transferred to an adjacent idle *** bandwidth and transmitting power of users and the relay node are both considered to formulate the resource allocation optimization *** objective is to maximize the energy eficiency of the whole heterogeneous networks under the constraints of the user's minimum data rate and energy *** suboptimal solution is obtained by using the particle swarm optimization(PSO)algorithm and quantum-behaved particle swarm optimization(QPSO)*** results show that the adopted methods have higher energy efficiency than the conventional fixed power and bandwidth *** addition,the time complexity of the adopted methods is relatively low.

关键词： energy harvesting heterogeneous networks energy efficiency

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Resource Allocation Method for Unmanned Aerial Vehicle-Assisted and User Cooperation Non-Linear Energy Harvesting Mobile Edge Computing System

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Journal of Shanghai Jiaotong University (Science) 2023年 1-12页

作者： He, Ximei Zhao, Yisheng Xu, Zhihong Chen, Yong Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou350108 China

Aimed at the doubly near-far problems in a large range suffered by the remote user group and in a small range existing in both nearby and remote user groups during energy harvesting and computation offloading, a resource allocation method for unmanned aerial vehicle (UAV)-assisted and user cooperation non-linear energy harvesting mobile edge computing (MEC) system is proposed. The UAV equipped with an MEC server is introduced to provide energy and computing services for the remote user group to alleviate the doubly near-far problem in a large range suffered by the remote user group. The doubly near-far problem in a small range existing in both nearby and remote user groups is mitigated by user cooperation. The specific user cooperation strategy is that the user near the base station or the UAV is used as a relay to transfer the computing task of the user far from the base station or the UAV to the MEC server for computing. By jointly optimizing users’ offloading time, users’ transmitting power, and the hovering position of the UAV, the resource allocation problem is modeled as a nonlinear programming problem with the objective of maximizing computation efficiency. The suboptimal solution is obtained by adopting the differential evolution algorithm. Simulation results show that, compared with the resource allocation method based on genetic algorithm and the without user cooperation method, the proposed method has higher computation efficiency. © 2023, Shanghai Jiao Tong University.

关键词： Resource allocation

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Resource Allocation Strategy for MI Communication-Based UWSN in Ocean Current Scenario

Resource Allocation Strategy for MI Communication-Based UWSN...

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2025 IEEE wireless Communications and Networking Conference, WCNC 2025

作者： Zhang, Tao Zhao, Yisheng Zhang, Shen Liu, Peng Hu, Zhiyi College of Physics and Information Engineering Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou China

ISBN: (纸本)9798350368369

Aiming at the problems of low data rate and limited battery power of traditional underwater wireless sensor network (UWSN) based on acoustic communication, a resource allocation strategy for magnetic induction (MI) communication-based UWSN in ocean current scenario is investigated in this paper. Specifically, multiple sensor nodes (SNs) are distributed in seawater at different depths. First, an autonomous underwater vehicle (AUV) is introduced to charge the SNs by magnetic coupling resonant wireless power transfer (MCR-WPT) technology. Then, the SNs transmit data to the AUV through MI communication. The influence of the ocean current on the SNs and AUV is analyzed. A genetic algorithm-based particle swarm optimization algorithm is used to optimize the AUV navigation trajectory. After collecting the data from the SNs, the AUV moves to the position under a surface base station and sends the collected data to the surface base station by MI communication. To minimize system energy consumption, the transmitting power of AUV and SNs are jointly optimized under the constraints of energy causality and transmitting power. The suboptimal solution to the formulated optimization problem is obtained by adopting the improved sparrow search algorithm (ISSA) combining Cauchy variation and reverse learning. Simulation results show that the ISSA has lower system energy consumption than other benchmark methods. © 2025 IEEE.

关键词： Energy utilization

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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Scene-Adaptive Reference Selection and Inter Prediction for Gaming Video Coding 5

Scene-Adaptive Reference Selection and Inter Prediction for ...

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5th International Seminar on Artificial Intelligence, Networking and information Technology, AINIT 2024

作者： Lin, Chengda Lin, Simin Fang, Ying Yi, Shiqi Wei, Hongan Xu, Yiwen Fuzhou University Fujian Key Lab For Intelligent Processing and Wireless Transmission of Media Information Fuzhou350108 China

ISBN: (纸本)9798350385557

With the burgeoning growth of the e-sports industry, there has been a rapid proliferation of gaming videos on online platforms. Simultaneously, this surge undoubtedly presents significant challenges to the encoding of game videos. However, the recurring characteristic of gaming videos is not efficiently studied in the current standardization, such as Versatile Video Coding (VVC). Based on these observations, our general framework, utilizing the Structural SIMilarity index metric (SSIM), Hash SIMilarity index metric (HSIM), and Hash Matrix SIMilarity index metric (HMSIM), identifies recurring video clips and adjusts the reference frame of the first frame. Additionally, we analyze recurring patterns in different resolutions and types, proposing our Scene Adaptation (SA) optimization algorithm, which integrates SSIM, HSIM, and HMSIM to adapt to various resolutions and scenes. Experimental results show that the proposed approach can achieve a -4.71 % Bjontegaard Delta Bit Rate (BDBR) and 1.00% Time Save (TS), outperforming the benchmark. © 2024 IEEE.

关键词： Video signal processing

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UAV-Assisted Multi-User Secure Communication Based on Hybrid DF and AF Protocol

UAV-Assisted Multi-User Secure Communication Based on Hybrid...

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2023 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2023

作者： Jian, Kaige Zhao, Yisheng Liang, Li You, Hongyi Zhang, Xinyu Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou China

ISBN: (纸本)9798350345407

Aiming at the threat of eavesdropping in unmanned aerial vehicle (UAV)-assisted communication networks, multiuser secure communication with multi-relay nodes and multi-eavesdropping threats is investigated in this paper. By deploying a dual-antenna UAV relay node, a hybrid decode-and-forward (DF) and amplify-and-forward (AF) protocol is considered to transmit data. In addition, cooperative jamming techniques are used in order to improve the performance of the secure confidential communication. The secure communication problem is formulated as an optimization problem. The goal is to maximize the minimum secrecy transmission rate subject to UAV transmitting power, user transmitting power, and channel bandwidth. By introducing a grey wolf optimizer (GWO), the suboptimal solution is obtained. Simulation results show that the GWO has higher secrecy transmission rate than the firefly algorithm and the average bandwidth and fixed transmitting power method. © 2023 IEEE.

关键词： Secure communication

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SLPSO-Based Resource Allocation Strategy for Layered UAVs-Assisted MEC System with MCR-WPT 9

SLPSO-Based Resource Allocation Strategy for Layered UAVs-As...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Zhang, Xinyu Zhao, Yisheng You, Hongyi Liang, Li Jian, Kaige College of Physics and Information Engineering Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou China

ISBN: (纸本)9798350317251

Aiming at the problem that the ground terminals (GTs) often suffer from difficulties in energy harvesting and data processing in remote areas, a resource allocation strategy for layered unmanned aerial vehicles (UAVs)-assisted mobile edge computing system is investigated in this paper. According to different functions, the UAVs are deployed in three layers. By using a magnetic coupling resonance wireless power transfer (MCR-WPT) technology, the GTs can obtain sufficient energy from the first layer of UAVs equipped with transmitting coils. The computational tasks of the GTs are divided into popular, private, and non-popular tasks. The popular and private tasks are both offloaded to the second layer of popular tasks UAVs (PT-UAVs), while the non-popular tasks are offloaded to the third layer of non-popular tasks UAV (NPT-UAV). The resource allocation problem is formulated as an optimization problem. The optimization objective is to minimize the system overhead by jointly optimizing the PT-UAVs caching policy, the GTs partial offloading factor, the charging time of the GTs, the trajectory of the NPT-UAV, and the bandwidth and computational resource of the system. The suboptimal solution is derived by introducing a social learning particle swarm optimization (SLPSO) algorithm. Simulation results show that the SLPSO algorithm outperforms other benchmark methods in terms of the system overhead. © 2023 IEEE.

关键词： Resource allocation

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Resource Allocation Strategy in UAV-D2D-Aided Multi-Relay MEC System with Energy Harvesting

Resource Allocation Strategy in UAV-D2D-Aided Multi-Relay ME...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： You, Hongyi Zhao, Yisheng Jian, Kaige Liang, Li Zhang, Xinyu Fuzhou University College of Physics and Information Engineering Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou China

ISBN: (纸本)9798350345384

In the mobile edge computing (MEC) scenarios, users outside the coverage area of the base station are unable to offload tasks during the offloading process. In this paper, a resource allocation strategy in unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) multi-relay MEC system with energy harvesting is proposed. Multiple UAVs are deployed around the edge of the base station (BS) coverage area. D2D link between users outside the BS coverage area and edge users of BS is controlled by the UAV. Edge users of BS can harvest energy from the UAV. Computing tasks of users outside the BS coverage area can be sent to edge users of BS by different D2D links. Then, edge users of BS can forward computing tasks to the MEC server of BS. Transmitting power, energy harvesting time, computing resources, and channel bandwidth are jointly optimized to minimize the total task completion time. The formulated problem is a non-convex optimization problem. By introducing a series of auxiliary variables, a perspective function is used to convert some non-convex constraints into convex constraints. An alternate iterative optimization algorithm is adopted to obtain the optimal solution to the original problem. Simulation results show that compared with other baseline schemes, the proposed strategy can reduce the total task completion time more effectively. © 2023 IEEE.

关键词： Base stations

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Personalized Federated Learning with Neighbor Aggregation for Traffic Flow Prediction 9

Personalized Federated Learning with Neighbor Aggregation fo...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Guo, Junxin Feng, Xinxin Zheng, Haifeng Fuzhou University Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou China

ISBN: (纸本)9798350317251

As an important part of intelligent Transportation System (ITS), traffic flow prediction has attracted more and more attention from academia and industry. However, since traffic flow data are distributed among different organizations, traditional centralized learning methods are no longer applicable, thus federated learning has begun to play an important role in traffic flow prediction tasks. In this paper, we design an improved traffic flow prediction model based on federated learning. At the same time, we consider the interaction of topology and feature space between traffic data owned by different organizations, and propose a method named Federated Neighbor Aggregation (FedNe). The server update the model parameters with both the physical connectivity and similarity relationships between the organizations, and finally generate a personalized model for each organization. Experimental results on real-world dataset show that our proposed method has the lowest average MAE and average RMSE. Finally, based on our method, when new clients participate, they can also quickly obtain their personalized models. © 2023 IEEE.

关键词： Forecasting

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Target Parameter Estimation with Deep Unfolding Networks for MIMO-OFDM Based Integrated Sensing and Communication Systems 15

Target Parameter Estimation with Deep Unfolding Networks for...

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15th IEEE International Conference on wireless Communications and Signal processing, WCSP 2023

作者： Lin, Jingcai Zheng, Haifeng Feng, Xinxin Fuzhou University Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China

ISBN: (纸本)9798350324662

The multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology shows great potential in integrated sensing and communication (ISAC) systems. To address the challenges posed by the high computational complexity and low estimation accuracy of traditional target parameter estimation methods in a MIMO-OFDM based ISAC system, this paper introduces a compressed sensing based approach for target parameter estimation. We first derive the channel model within the MIMO-OFDM system, and then reformulate the problem of target parameter estimation as a least absolute shrinkage and selection operator (LASSO) problem. Furthermore, we propose a model-driven network, termed FISTA-Net, which deeply unfolds the fast iterative shrinkage thresholding algorithm (FISTA) into a deep neural network to solve the LASSO problem. Experimental results show that the proposed FISTA-Net algorithm outperforms the existing methods in terms of estimation accuracy and computation efficiency for MIMO-OFDM based target parameter estimation. © 2023 IEEE.

关键词： compressed sensing deep unfolding FISTA MIMO-OFDM parameter estimation

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