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Online Offloading for Energy-Efficient and Delay-Aware MEC Systems With Cellular-Connected UAVs

IEEE INTERNET OF THINGS JOURNAL 2024年第12期11卷 22321-22336页

作者： Liu, Binghong Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

In this article, an unmanned aerial vehicle (UAV)-mobile edge computing (MEC) network is considered, where cellular-connected UAVs can either handle the computing tasks locally or offload to base stations. Considering the emerging computation-intensive and delay-sensitive applications, how to strike a balance between energy and delay, is one of the key design issues in UAV-MEC networks. Against this backdrop, we establish a double-queue model innovatively, in which the virtual queue is introduced to sense the backlog status of the actual queue. As such, the delay guarantee of computing tasks can be turned into the stable control of virtual queues. The network quality and server heterogeneity are considered to schedule the workloads rationally. Based on the Lyapunov optimization method, we formulate the deterministic problem to achieve a tradeoff between the long-term energy consumption and the time-average traffic delay, by jointly optimizing the offloading decision, resource allocation and trajectory planning, subject to the constraints of queue stability, resource budgets and flying kinematics. To solve this mixed-integer nonlinear programming problem, we propose an energy-efficient and delay-aware online algorithm, in which the problem is first split into equivalent resource allocation and trajectory planning subproblems, and the closed-form solutions of power, slot and computing resource allocation can be derived. Then, based on the Lagrange dual and successive convex approximation methods, these subproblems are solved iteratively to explore the optimality. Extensive simulations validate the superiority of our proposed algorithm over various benchmark schemes, showing its effectiveness in minimizing the energy consumption while simultaneously maintaining the low latency.

关键词： Autonomous aerial vehicles Task analysis Resource management Energy consumption Delays Optimization Servers Lyapunov optimization mobile edge computing (MEC) resource allocation unmanned aerial vehicle (UAV) trajectory planning UAV

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A Relay System for Semantic Image Transmission Based on Shared Feature Extraction and Hyperprior Entropy Compression

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IEEE INTERNET OF THINGS JOURNAL 2024年第9期11卷 16158-16170页

作者： An, Wannian Bao, Zhicheng Liang, Haotai Dong, Chen Xu, Xiaodong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Nowadays, the need for high-quality image reconstruction and restoration is more and more urgent. However, most image transmission systems may suffer from image quality degradation or transmission interruption in the face of interference such as channel noise and link fading. To solve this problem, the a relay communication network for semantic image transmission based on shared feature extraction and hyperprior entropy compression (HEC) is proposed, where the shared feature extraction technology based on Pearson correlation is proposed to eliminate partial shared feature of extracted semantic latent feature. In addition, the HEC technology is used to resist the effect of channel noise and link fading and carried out, respectively, at the source node and the relay node. Experimental results demonstrate that compared with other recent research methods, the proposed system has lower transmission overhead and higher semantic image transmission performance. Particularly, compared with the element-distance-based scheme, the proposed shared feature extraction method based on Pearson correlation not only exhibits an approximate 0.2 advantage in multiscale structural similarity (MS-SSIM) but also efficiently eliminates the burden caused by the transmission of shared feature indexes.

关键词： Hyperprior entropy compression (HEC) relay system semantic communication shared feature extraction

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Hierarchical Multiple Split Federated Learning for Low-Carbon Resource-Constrained User Equipment

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IEEE INTERNET OF THINGS JOURNAL 2024年第24期11卷 39127-39141页

作者： Guo, Chengwei Zhou, Fanqin Feng, Lei Li, Wenjing Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Split federated learning (SFL) allows clients with limited resources to engage in distributed machine learning, yet it grapples with issues related to energy usage and the efficiency of training. We propose low-carbon hierarchical multiple SFL (HMSFL) to address these issues and advance sustainable computing. HMSFL partitions the client model into several segments, enabling local aggregation among clients. This process amplifies energy efficiency and diminishes the carbon footprint. We formulate the training cost minimization problem and solve it using a generalized task allocation algorithm. Evaluation across real-world tasks demonstrates that HMSFL achieves a 36% reduction in training time and a 33% decrease in energy consumption compared to baseline methods, showcasing its potential for sustainable distributed machine learning.

关键词： Training Computational modeling Servers Energy consumption Federated learning Costs Green computing Resource management Heuristic algorithms Collaboration Federated learning (FL) low carbon split learning (SL) sustainable computing training cost minimization (TCM)

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HDR-DANet: single HDR image reconstruction via dual attention

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MULTIMEDIA SYSTEMS 2025年第1期31卷 1-12页

作者： Ma, Jindong Zhang, Haitao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Key Lab Intelligent Telecomm Software & Mu Beijing Peoples R China

Restoring high dynamic range images with a single low dynamic range image is a challenging task due to the limited brightness range captured by most consumer camera sensors, leading to missing details in overexposed or underexposed areas of the image. The current methods suffer from weak reconstruction ability for inconspicuous complex features in the extreme exposure area, resulting in artifacts still appearing in the extreme exposure area after reconstruction. This paper proposes an end-to-end network based on dual attention to solve this problem. Specifically, this paper designs a pixel-level spatial attention mechanism with luminance mask to refine the spatial attention to the pixel level, and can focus on inconspicuous feature areas in the overexposed and underexposed regions. This paper also designs a concentrated channel attention mechanism to maximize the use of image context information, accurately assigning the channel weight and effectively extracting the complex features in the image. Qualitative and quantitative evaluations show the superiority of our approach over state-of-the-art methods.

关键词： HDR reconstruction Attention mechanism Convolutional neural network

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Efficiently Transfer User Profile Across Networks

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IEEE TRANSACTIONS ON BIG DATA 2025年第1期11卷 271-285页

作者： Diao, Mengting Zhang, Zhongbao Su, Sen Gao, Shuai Cao, Huafeng Ye, Junda Beijing Univ Posts ands Telecommun BUPT State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

User profiling has very important applications for many downstream tasks. Most existing methods only focus on modeling user profiles of one social network with plenty of data. However, user profiles are difficult to acquire, especially when the data is scarce. Fortunately, we observed that similar users have similar behavior patterns in different social networks. Motivated by such observations, in this paper, we for the first time propose to study the user profiling problem from the transfer learning perspective. We design two efficient frameworks for User Profile transferring acrOss Networks, i.e., UPON and E-UPON. In UPON, we first design a novel graph convolutional networks based characteristic-aware domain attention model to find user dependencies within and between domains (i.e., social networks). We then design a dual-domain weighted adversarial learning method to address the domain shift problem existing in the transferring procedure. In E-UPON, we optimize UPON in terms of computational complexity and memory. Specifically, we design a mini-cluster gradient descent based graph representation algorithm to shrink the searching space and ensure parallel computation. Then we use an adaptive cluster matching method to adjust the clusters of users. Experimental results on Twitter-Foursquare dataset demonstrate that UPON and E-UPON outperform the state-of-the-art models.

关键词： Social networking (online) Transfer learning Knowledge engineering Computational complexity Clustering algorithms Big Data Adversarial machine learning User profiling transfer learning social network

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Turbulent Single-Scattering Channel Model for Ultraviolet Communications Using Equivalent Scattering Point Approach

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IEEE COMMUNICATIONS LETTERS 2024年第7期28卷 1579-1583页

作者： Chu, Xinyi Yuan, Renzhi Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

None-line-of-sight (NLOS) ultraviolet (UV) communication has attracted increasing attention in recent years. The atmospheric turbulence will deteriorate the performance of NLOS UV communications. Existing works on the turbulent model of NLOS UV single-scattering channels only considered the narrow beam cases. In this letter, we propose a turbulent channel model using the equivalent scattering points (ESPs) approach to characterize the fluctuation effect on UV signals under large full-width beam divergence angles and field-of-view angles. An analytical turbulent scintillation index is derived considering the turbulent correlation between different ESPs and is verified by its numerical solutions. Numerical results demonstrate that the turbulent scintillation index will increase with the turbulent correlation distance. Besides, we demonstrate that a large common volume can mitigate the turbulent fluctuation effect on UV communications, which can be explained by an "equivalent spatial diversity effect" due to the large common volume.

关键词： Ultraviolet communication single-scattering model scintillation index turbulent channels Ultraviolet communication single-scattering model scintillation index turbulent channels

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Cost-Aware Resource Allocation for Hybrid Multiple Access in Cell-Free MIMO System

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IEEE COMMUNICATIONS LETTERS 2024年第4期28卷 852-856页

作者： Qin, Wen Meng, Yue Zhang, Zhi Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

In this letter, we investigate the resource allocation problem for multiple access in the cell-free multiple-input multiple-output (CF MIMO) system. To fully utilize multi-dimensional resources and meet the diverse demands of users, we extend the hybrid multiple access technology into the CF MIMO system based on its user-centric architecture, which unifies space division multiple access, non-orthogonal multiple access and orthogonal multiple access. Considering distinct expenses of different multiple access modes at receivers, we design a cost-aware utility function as the metric and develop a cost-aware utilization maximization optimization problem. To address this non-convex combinatorial optimization problem, an iterative resource allocation algorithm is proposed. This algorithm consists of a subchannel allocation algorithm based on matching theory and a power allocation algorithm based on Lagrange dual method. Simulation results validate the superiority of proposed scheme over fixed multiple access schemes.

关键词： Cell-free MIMO resource allocation hybrid multiple access matching theory

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Channel Estimation for Holographic MIMO: Wavenumber-Domain Sparsity-Inspired Approaches

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第8期13卷 2305-2309页

作者： Guo, Yuqing Chen, Yuanbin Wang, Ying Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

This letter investigates the sparse channel estimation for holographic multiple-input multiple-output (HMIMO) systems. Given that the wavenumber-domain representation is based on a series of Fourier harmonics that are in essence a series of orthogonal basis functions, a novel wavenumber-domain sparsifying basis is designed to expose the sparsity inherent in HMIMO channels. Furthermore, by harnessing the beneficial sparsity in the wavenumber domain, the sparse estimation of HMIMO channels is structured as a compressed sensing problem, which can be efficiently solved by our proposed wavenumber-domain orthogonal matching pursuit (WD-OMP) algorithm. Finally, numerical results demonstrate that the proposed wavenumber-domain sparsifying basis maintains its detection accuracy regardless of the number of antenna elements and antenna spacing. Additionally, in the case of antenna spacing being much less than half a wavelength, the wavenumber-domain approach remains highly accurate in identifying the significant angular power of HMIMO channels.

关键词： Antennas Channel estimation Transmitters Receivers Matching pursuit algorithms MIMO communication Channel models Holographic MIMO wavenumber domain channel estimation compressed sensing

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Explainable and Energy-Efficient Selective Ensemble Learning in Mobile Edge Computing Systems

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2025年第2期22卷 1744-1759页

作者： Feng, Lei Liao, Chaorui Shi, Yingji Zhou, Fanqin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Explainable ensemble learning combines explainable artificial intelligence (XAI) and ensemble learning (EL) to solve the closed-box problem of EL and provide a clear and transparent explanation of the decision-making process in the model. As a distributed machine learning architecture, EL deploys base learners trained with local data at edge node and infers on target tasks, then combines the inference results of the participating base learners. However, selecting all base learners into EL may result in wasting more computing resources and not obtain better performance. To address this issue, we put forward the definition of confidence level (ConfLevel) on the basis of XAI and verify its effectiveness as the metric of selecting the base learner. Then, we take the joint optimization model of considering high ConfLevel and low computing power to determine the participating base learners for selective ensemble learning (SEL). Due to the non-convex and combinatorial nature of the problem, we propose a node selection and power control algorithm on the premise of Benders' Decomposition (referred to BD-NSPC) to obtain the global optimal solution efficiently. In addition, simulation results show that BD-NSPC consumes about 30% less energy per EN on average and improves accuracy by 1-2% compared to other SEL algorithms. Besides, compared with federated learning (FL) framework, BD-NSPC reduces the energy consumption by about 25% and the latency by about 28%, achieving comparable accuracy in the edge computing system.

关键词： Computational modeling Machine learning algorithms Machine learning Ensemble learning Training Predictive models Explainable AI Prediction algorithms Energy consumption Accuracy Edge computing ensemble learning ensemble selection explainable artificial intelligence

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Dynamic Beam Pattern Based on Cooperation Multi-Agent VDN-D3QN for LEO Satellite Communication System

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and networking 2024年第2期9卷 725-738页

作者： Meng, Meng Hu, Bo Chen, Shanzhi Kang, Shaoli Beijing University of Posts and Telecommunications State Key Lab of Networking and Switching Technology Beijing China State Key Lab of Wireless Mobile Communication Beijing China

Due to the cooperative coverage characteristic of LEO satellites and non-uniform traffic demand of beam positions, allocating the limited beam and power resource to massive beam positions flexibly and effectively is a challenge in beam hopping LEO satellite communication system. The agents in existing beam hopping schemes, which rely on deep reinforcement learning, are limited to acquiring state information within the coverage area of LEO satellite. For this reason, we propose a cooperation multi-agent Value-Decomposition Networks with Dueling Double Deep Q-Learning Network (VDN-D3QN) framework to generate dynamic beam hopping pattern for assuring delay fairness and throughput among beam positions in LEO satellite communication system. The proposed VDN-D3QN dynamic beam hopping method is divided into training and test phase, where each agent is only responsible for the beam hopping pattern of one LEO satellite. During the train phase, the agents learn to cooperate with other agents to maximize the system throughput and minimize the delay fairness among beam positions by Dueling Double Deep Q-Learning Network. Then, the Value-Decomposition Networks is employed to learn the optimal policy in a centralized manner through interaction with the environment. In test phase, the trained agents are deployed to address the challenging problem of inter-satellite communication in a distributed manner, and one agent is deployed per LEO satellite. The trained agents can make decisions about the dynamic beam hopping pattern based on the available local state information in LEO satellite communication system. The evaluation results demonstrate that the proposed multi-agent VDN-D3QN algorithm can effectively handle the non-uniform traffic demand of multi-satellites simultaneously. Besides, the simulation results indicate that the proposed VDN-D3QN algorithm can allocate resource intelligently for adapting the requirements of beam positions and achieving better performance compared to

关键词： Satellite communication systems

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