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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

IEEE Transactions on Mobile Computing 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In Nanyang Technological University College of Computing and Data Science Energy Research Institute @ NTU Interdisciplinary Graduate Program Singapore University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2002-2012 IEEE.

关键词： Resource allocation

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Revisiting Near-Far Field Boundary in Dual-Polarized XL-MIMO Systems

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IEEE Transactions on Wireless Communications 2025年

作者： Zeng, Shuhao Di, Boya Zhang, Hongliang Han, Zhu Vincent Poor, H. Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen China Princeton University Department of Electrical and Computer Engineering NJ United States Peking University School of Electronics Beijing100871 China University of Houston Electrical and Computer Engineering Department HoustonTX United States Kyung Hee University Department of Computer Science and Engineering Seoul Korea Republic of

Extremely large-scale multiple-input multiple-output (XL-MIMO) is expected to be an important technology in future sixth generation (6G) networks. Compared with conventional single-polarized XL-MIMO, where signals are transmitted and received in only one polarization direction, dual-polarized XL-MIMO systems achieve higher data rate by improving multiplexing performances, and thus are the focus of this paper. Due to enlarged aperture, near-field regions become non-negligible in XL-MIMO communications, necessitating accurate near-far field boundary characterizations. However, existing boundaries developed for single-polarized systems only consider phase or power differences across array elements while irrespective of cross-polarization discrimination (XPD) variances in dual-polarized XL-MIMO systems, deteriorating transmit covariance optimization performances. In this paper, we revisit near-far field boundaries for dual-polarized XL-MIMO systems by taking XPD differences into account, which faces the following challenge. Unlike existing near-far field boundaries, which only need to consider co-polarized channel components, deriving boundaries for dual-polarized XL-MIMO systems requires modeling joint effects of co-polarized and cross-polarized components. To address this issue, we model XPD variations across antennas and introduce a non-uniform XPD distance to complement existing near-far field boundaries. Based on the new distance criterion, we propose an efficient scheme to optimize transmit covariance. Numerical results validate our analysis and demonstrate the proposed algorithm's effectiveness. © 2002-2012 IEEE.

关键词： Polarization

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Reconfigurable Holographic Surface: A New Paradigm for Ultra-Massive MIMO

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Di, Boya Zhang, Hongliang Han, Zhu Zhang, Rui Song, Lingyang Peking University School of Electronics China University of Houston Department of Electrical and Computer Engineering HoustonTX United States Kyung Hee University Department of Computer Science and Engineering Seoul Korea Republic of The Chinese University of Hon g Kong School of Science and Engineering Guangdong Shenzhen China National University of Singapore Department of Electrical and Computer Engineering Singapore Singapore Peking University Shenzhen Graduate School School of Electronic and Computer Engineering China

Evolving from massive multiple-input multiple-output (MIMO) in current 5G communications, ultra-massive MIMO emerges as a seminal technology for fulfilling more stringent requirements of future 6G communications. However, widely-utilized phased arrays relying on active components make the implementation of ultra-massive MIMO in practice increasingly prohibitive from both cost and power consumption perspectives. In contrast, the development of reconfigurable holographic surface (RHS) provides a new paradigm to solve the above issue without the need of costly hardware components. By leveraging the holographic principle, the RHS serves as an ultra-thin and lightweight surface antenna integrated with the transceiver, which is a promising alternative to phased arrays for realizing ultra-massive MIMO. In this paper, we provide a comprehensive overview of the RHS, especially the RHS-aided communication and sensing. We first describe the basic concepts of RHS, and introduce its working principle and unique practical constraints. Moreover, we show how to utilize the RHS to achieve cost-efficient and high-performance wireless communication and sensing, and introduce the key technologies. In particular, we present the implementation of RHS with a wireless communication prototype, and report the experimental measurement results based on it. Finally, we outline some open challenges and potential future directions in this area. © 2015 IEEE.

关键词： Antenna phased arrays

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Room-scale magnetoquasistatic wireless power transfer using a cavity-based multimode resonator

arXiv

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arXiv 2025年

作者： Sasatani, Takuya Sample, Alanson P. Kawahara, Yoshihiro Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo Bunkyo-ku Tokyo Japan Electrical Engineering and Computer Science Department University of Michigan Ann ArborMI United States

Magnetoquasistatic wireless power transfer can be used to charge and power electronic devices such as smartphones and small home appliances. However, existing coil-based transmitters, which are composed of wire conductors, have a limited range. Here, we show that multimode-quasistatic cavity resonance can provide room-scale wireless power transfer. The approach uses multidirectional, widely distributed currents on conductive surfaces that are placed around the target volume. It generates multiple, mutually unique, three-dimensional magnetic field patterns, where each pattern is attributed to different eigenmodes of a single room scale resonator. Using these modes together, a power delivery efficiency exceeding 37.1% can be achieved throughout a 3 m × 3 m × 2 m test room. With this approach, power exceeding 50 W could potentially be delivered to mobile receivers, while in accordance with safety guidelines. Copyright © 2025, The Authors. All rights reserved.

关键词： Inductive power transmission

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Multi-Location and Multi-Feature LMP Forecasting: A 2D Spatiotemporal LSTM-CNN Approach

Multi-Location and Multi-Feature LMP Forecasting: A 2D Spati...

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Power, Energy and electrical engineering (CPEEE), International Conference on

作者： Noelyn M. De Jesus Enrique D. Festijo Gerard Francesco DG. Apolinario Dylan Josh Domingo Lopez Graduate School Technological Institute of the Philippines Manila Philippines Electrical Engineering Technological Institute of the Philippines Manila Philippines Electrical Engineering and Computer Science Chung Yuan Christian University Taoyuan Taiwan

ISBN: (数字)9798331520090

ISBN: (纸本)9798331520106

Accurate Locational Marginal Price (LMP) forecasting is crucial for effective energy procurement in electricity markets, as it helps utilities make informed, cost-efficient decisions. This study addresses the limitations of traditional models by introducing a 2D spatiotemporal LSTM-CNN model that utilizes multi-location and multi-feature inputs to improve forecast accuracy specifically for Batangas I Electric Cooperative, Inc. (BATELEC I). Using the Dynamic Interval Price Collection (DIPC) dataset from the Independent Electricity Market Operator of the Philippines (IEMOP), which provides 5-minute interval data from June 2021 to August 2024, the model captures short-term price fluctuations across resource locations. Results indicate that the 2D Multi-Features and Multi-Locations LSTM-CNN model outperforms 1D and 3D configurations, achieving the lowest RMSE of 5.0976E-03 and effectively capturing spatial and temporal dependencies. These findings support improved decision-making in electricity markets, with future applications in real-time adaptive forecasting for enhanced market responsiveness.

关键词： Solid modeling Adaptation models Accuracy Three-dimensional displays Predictive models Electricity supply industry Data models Real-time systems Spatiotemporal phenomena Forecasting

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Light extraction improvement via ITO p-electrodes for InGaN red micro-LEDs emitting at 640 nm

Optics Continuum

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Optics Continuum 2025年第5期4卷 1040-1050页

作者： Cesur Altinkaya Mohammed A Najmi Daisuke Iida Kazuhiro Ohkawa Material Science and Engineering Program Physical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia cesur.altinkaya@kaust.edu.sa Electrical and Computer Engineering Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia daisuke.iida@kaust.edu.sa kazuhiro.ohkawa@kaust.edu.sa

We present light extraction efficiency (LEE) improvement for InGaN red micro-light emitting diodes (micro-LEDs) of various sizes operating at low current densities. We compared the characteristics of micro-LEDs with indium tin oxide (ITO) transparent p-electrodes with conventional opaque metal p-electrodes. 50 µm × 50 µm micro-LEDs with ITO p-electrodes achieved a peak on-wafer external quantum efficiency (EQE) of 2.54% with an emission wavelength of 640 nm at a current density as low as 0.4 A/cm 2 . This represents a 1.18-fold improvement in peak EQE compared to devices with metal p-electrodes. Light ray tracing simulation confirmed that the ITO p-electrodes exhibit 1.18 times higher light escape than metal-based micro-LEDs, validating the role of enhanced light extraction. These findings provide valuable insights for advancing high-definition display and VR applications.

关键词： Atomic layer deposition Indium tin oxide Light extraction Opacity Quantum efficiency Ray tracing

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MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network

MSEMG: Surface Electromyography Denoising with a Mamba-based...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Yu-Tung Wang, Kuan-Chen Chao, Rong Siniscalchi, Sabato Marco Yeh, Ping-Cheng Tsao, Yu Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan University of Palermo Italy Research Center for Information Technology Innovation Academic Sinica Taiwan

ISBN: (纸本)9798350368741

Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart. Traditional signal processing-based approaches, such as high-pass filtering and template subtraction, have been used to remove ECG interference but are often limited in their effectiveness. Recently, neural network-based methods have shown greater promise for sEMG denoising, but they still struggle to balance both efficiency and effectiveness. In this study, we introduce MSEMG, a novel system that integrates the Mamba state space model with a convolutional neural network to serve as a lightweight sEMG denoising model. We evaluated MSEMG using sEMG data from the Non-Invasive Adaptive Prosthetics database and ECG signals from the MIT-BIH Normal Sinus Rhythm Database. The results show that MSEMG outperforms existing methods, generating higher-quality sEMG signals using fewer parameters. © 2025 IEEE.

关键词： Deep neural network ECG interference removal State space model Surface electromyography

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Data-Driven Incentive Mechanisms for Federated Learning in Vehicular Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Fathi, Faeze Montazeri, Mina N.araabi, Babak Du, Hongyang Niyato, Dusit Kebriaei, Hamed University of Tehran School of Ece College of Engineering Tehran Iran Urban Energy Systems Laboratory Dübendorf8600 Switzerland Nanyang Technological University School of Computer Science and Engineering The Energy Research Institute @ Ntu Interdisciplinary Graduate Program Singapore Nanyang Technological University School of Computer Science and Engineering Singapore School of Computer Science Tehran Iran

Centralized machine learning algorithms in vehicular networks face privacy and resource constraints. Federated Learning (FL) addresses these by enabling collaborative model training without sharing raw data. To incentivize vehicle participation despite resource constraints, we propose a contract between Roadside Units (RSUs) and vehicles, specifying required computation resources and rewards based on local data quality. The contract is designed to maximize the utility of the RSU, subject to constraints that ensure the participation of the vehicles in the task and truthful reporting of their data quality parameter. However, designing an optimal contract with classical methods is limited to specific utility functions. To extend the design methodology of optimal contracts to a broader range of utility functions, our study proposes a deep learning-based solution. Specifically, we present a novel application of Deep Neural Networks (DNNs), marking the first instance of applying DNNs in contract theory-based incentive mechanisms to achieve nearoptimal contracts. We design the DNN's structure to inherently satisfy specific constraints of the optimization problem, enhancing its efficiency and accuracy in determining the optimal contract. We utilize an augmented Lagrangian method to approximate the optimal contract in federated learning tasks. A case study on traffic sign recognition demonstrates the applicability and effectiveness of our method. Comparing our approach with the well-known generative diffusion model shows that in the FL scenarios, the DNN results are closer to the optimal contract compared to those of the generative diffusion model. © 1967-2012 IEEE.

关键词： Traffic signs

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Constructing Semantical Structure by Segmentation Integrated Video Embedding for Temporal Action Detection

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhao, Zixuan Liu, Shuming Zhao, Chengze Zhao, Xu Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China King Abdullah University of Science and Technology Computer Science Program Thuwal23599 Saudi Arabia

Video embedding is the pivot in Temporal Action Detection (TAD). Once the video embedding can robustly capture the essence of actions and perceive activities in complex scenes, the TAD model can more accurately localize action boundaries. Currently, video embedding is typically based on rule-based pixel convolution or cube-based transformer, wherein structured semantic information is intertwined, leading to the submergence of crucial spatial semantic information, such as the intrinsic motion of key semantic objects and interactions among semantic objects. To address these limitations, it is imperative to explore alternative approaches. With the remarkable performance of general semantic segmentation models in visual representation, we introduce the general segmentation model SEEM into the video embedding paradigm, constructing a semantically structured representation from perceptual semantics to cognitive semantics. To more effectively utilize SEEM for structured video representation, we designed the Semantic Adapter (Sem-Adapter) as a bridge to connect the two models. Firstly, we design a Self-Motion Module (SMM) to pay attention to the self-motion of key semantic regions. Secondly, we propose a Mutual Relation Module (MRM) to construct the interactions between semantic regions. Extensive experiments on ActivityNet-1.3, THUMOS-14 and EPIC-Kitchens-100 reveal that our method significantly outperforms state-of-the-art methods under the same input modality, and our method improves the average mAP from 60.6% to 64.2% on THUMOS-14 with the same backbone. The code is available on https://***/shouxiaozixuan/semtad. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Multi-level PEnet: A Robust Three-Stage Model for Parameter Estimation in Non-Gaussian Noise-Driven Stochastic Differential Equations

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Nonlinear Dynamics 2025年 1-28页

作者： Li, Shuaiyu Cheng, Yuzhong Ruan, Yang Saigo, Hiroto Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Joint Graduate School of Mathematics for Innovation Kyushu University Fukuoka Japan Sino-Jan Joint Lab of Natural Health Products Research School of Traditional Chinese Pharmacy China Pharmaceutical University Nanjing China Department of Electrical Engineering and Computer Science Kyushu University Fukuoka Japan

This study tackles key challenges in parameter estimation for stochastic differential equations (SDEs) driven by non-Gaussian noise, which are crucial for modeling dynamic phenomena like price fluctuations and epidemic spread. Traditional quasi-likelihood methods struggle in complex SDE scenarios due to strong assumptions and step-size constraints. Recent data-driven approaches using neural networks have shown promise but face issues like performance degradation on long sequences and limited generalization at domain boundaries. We propose the Multi-level Parameter Estimation Network (MLPEnet), a three-stage model incorporating a parameter heterogeneity condensation phase to improve inference speed and generalization. Experimental results on synthetic datasets indicate that MLPEnet provides better overall accuracy compared to previous methods and effectively reduces performance decline at the boundaries of the training domain, highlighting its potential for broad applicability.

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