AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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Here you can find the complete list of our publications.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Liu, G.; Du, H.; Wang, J.; Niyato, D.; Kim, D. I.
Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse Journal Article
In: IEEE Transactions on Mobile Computing, 2025, ISSN: 15361233 (ISSN).
Abstract | Links | BibTeX | Tags: Contest Theory, Deep learning, Deep reinforcement learning, Diffusion Model, Generative adversarial networks, Generative AI, High quality, Image generation, Image generations, Immersive technologies, Metaverses, Mobile edge computing, Reinforcement Learning, Reinforcement learnings, Resource allocation, Resources allocation, Semantic data, Virtual addresses, Virtual environments, Virtual Reality
@article{liu_contract-inspired_2025,
title = {Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse},
author = {G. Liu and H. Du and J. Wang and D. Niyato and D. I. Kim},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000066834&doi=10.1109%2fTMC.2025.3550815&partnerID=40&md5=3cb5a2143b9ce4ca7f931a60f1bf239c},
doi = {10.1109/TMC.2025.3550815},
issn = {15361233 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Mobile Computing},
abstract = {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.},
keywords = {Contest Theory, Deep learning, Deep reinforcement learning, Diffusion Model, Generative adversarial networks, Generative AI, High quality, Image generation, Image generations, Immersive technologies, Metaverses, Mobile edge computing, Reinforcement Learning, Reinforcement learnings, Resource allocation, Resources allocation, Semantic data, Virtual addresses, Virtual environments, Virtual Reality},
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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.
Zhang, Z.; Jiang, Y.; Wei, X.; Chen, M.; Dong, H.; Yu, S.
Generative-AI for XR Content Transmission in the Metaverse: Potential Approaches, Challenges, and a Generation-Driven Transmission Framework Journal Article
In: IEEE Network, 2025, ISSN: 08908044 (ISSN).
Abstract | Links | BibTeX | Tags: 'current, Cloud servers, Collaboration architecture, Content transmission, Decision making, Deep learning, Deep reinforcement learning, Edge server, Generative adversarial networks, Intelligence models, Large volumes, Metaverses, Network bottlenecks, Reinforcement Learning, Through current
@article{zhang_generative-ai_2025,
title = {Generative-AI for XR Content Transmission in the Metaverse: Potential Approaches, Challenges, and a Generation-Driven Transmission Framework},
author = {Z. Zhang and Y. Jiang and X. Wei and M. Chen and H. Dong and S. Yu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-86000661262&doi=10.1109%2fMNET.2025.3547385&partnerID=40&md5=1e00d40542ec58ef1489934abb2a990c},
doi = {10.1109/MNET.2025.3547385},
issn = {08908044 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Network},
abstract = {How to efficiently transmit large volumes of Extended Reality (XR) content through current networks has been a major bottleneck in realizing the Metaverse. The recently emerging Generative Artificial Intelligence (GAI) has already revolutionized various technological fields and provides promising solutions to this challenge. In this article, we first demonstrate current networks' bottlenecks for supporting XR content transmission in the Metaverse. Then, we explore the potential approaches and challenges of utilizing GAI to overcome these bottlenecks. To address these challenges, we propose a GAI-based XR content transmission framework which leverages a cloud-edge collaboration architecture. The cloud servers are responsible for storing and rendering the original XR content, while edge servers utilize GAI models to generate essential parts of XR content (e.g., subsequent frames, selected objects, etc.) when network resources are insufficient to transmit them. A Deep Reinforcement Learning (DRL)-based decision module is proposed to solve the decision-making problems. Our case study demonstrates that the proposed GAI-based transmission framework achieves a 2.8-fold increase in normal frame ratio (percentage of frames that meet the quality and latency requirements for XR content transmission) over baseline approaches, underscoring the potential of GAI models to facilitate XR content transmission in the Metaverse. © 2025 IEEE.},
keywords = {'current, Cloud servers, Collaboration architecture, Content transmission, Decision making, Deep learning, Deep reinforcement learning, Edge server, Generative adversarial networks, Intelligence models, Large volumes, Metaverses, Network bottlenecks, Reinforcement Learning, Through current},
pubstate = {published},
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How to efficiently transmit large volumes of Extended Reality (XR) content through current networks has been a major bottleneck in realizing the Metaverse. The recently emerging Generative Artificial Intelligence (GAI) has already revolutionized various technological fields and provides promising solutions to this challenge. In this article, we first demonstrate current networks' bottlenecks for supporting XR content transmission in the Metaverse. Then, we explore the potential approaches and challenges of utilizing GAI to overcome these bottlenecks. To address these challenges, we propose a GAI-based XR content transmission framework which leverages a cloud-edge collaboration architecture. The cloud servers are responsible for storing and rendering the original XR content, while edge servers utilize GAI models to generate essential parts of XR content (e.g., subsequent frames, selected objects, etc.) when network resources are insufficient to transmit them. A Deep Reinforcement Learning (DRL)-based decision module is proposed to solve the decision-making problems. Our case study demonstrates that the proposed GAI-based transmission framework achieves a 2.8-fold increase in normal frame ratio (percentage of frames that meet the quality and latency requirements for XR content transmission) over baseline approaches, underscoring the potential of GAI models to facilitate XR content transmission in the Metaverse. © 2025 IEEE.
Zhang, Z.; Wang, J.; Chen, J.; Fu, H.; Tong, Z.; Jiang, C.
Diffusion-Based Reinforcement Learning for Cooperative Offloading and Resource Allocation in Multi-UAV Assisted Edge-Enabled Metaverse Journal Article
In: IEEE Transactions on Vehicular Technology, 2025, ISSN: 00189545 (ISSN).
Abstract | Links | BibTeX | Tags: Aerial vehicle, Content creation, Content services, Contrastive Learning, Decision making, Deep learning, Deep reinforcement learning, Diffusion Model, Global industry, Helicopter services, Markov processes, Metaverse, Metaverses, Reinforcement Learning, Reinforcement learnings, Resource allocation, Resources allocation, Typical application, Unmanned aerial vehicle, Unmanned aerial vehicle (UAV), Unmanned aerial vehicles (UAV)
@article{zhang_diffusion-based_2025,
title = {Diffusion-Based Reinforcement Learning for Cooperative Offloading and Resource Allocation in Multi-UAV Assisted Edge-Enabled Metaverse},
author = {Z. Zhang and J. Wang and J. Chen and H. Fu and Z. Tong and C. Jiang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219108203&doi=10.1109%2fTVT.2025.3544879&partnerID=40&md5=fdbe1554f6cf7d47d4bbbb73b4b0d487},
doi = {10.1109/TVT.2025.3544879},
issn = {00189545 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Vehicular Technology},
abstract = {As one of the typical applications of 6G, the metaverse, with its superior immersion and diversified services, has garnered widespread attention from both the global industry and academia. Simultaneously, the emergence of AI-generated content (AIGC), exemplified by ChatGPT, has revolutionized the mean of content creation in the metaverse. Providing meataverse users with diversified AIGC services anytime and anywhere to meet the demand for immersive and blended virtual-real experiences in the physical world has become a major challenge in the development of the metaverse. Considering the flexibility and mobility of unmanned aerial vehicles (UAVs), we innovatively incorporate multiple UAVs as one of the AIGC service providers and construct a multi-UAV assisted edge-enabled metaverse system in the context of AIGC-as-a-Service (AaaS) scenario. To solve the complex resource management and allocation problem in the aforementioned system, we formulate it as a Markov decision process (MDP) and propose utilizing the generative capabilities of the diffusion model in combination with the robust decision-making abilities of reinforcement learning to tackle these issues. In order to substantiate the efficacy of the proposed diffusion-based reinforcement learning framework, we propose a novel diffusion-based soft actor-critic algorithm for metaverse (Meta-DSAC). Subsequently, a series of experiments are executed and the simulation results empirically validate the proposed algorithm's comparative advantages of the ability to provide stable and substantial long-term rewards, as well as the enhanced capacity to model complex environment. © 2025 IEEE.},
keywords = {Aerial vehicle, Content creation, Content services, Contrastive Learning, Decision making, Deep learning, Deep reinforcement learning, Diffusion Model, Global industry, Helicopter services, Markov processes, Metaverse, Metaverses, Reinforcement Learning, Reinforcement learnings, Resource allocation, Resources allocation, Typical application, Unmanned aerial vehicle, Unmanned aerial vehicle (UAV), Unmanned aerial vehicles (UAV)},
pubstate = {published},
tppubtype = {article}
}
As one of the typical applications of 6G, the metaverse, with its superior immersion and diversified services, has garnered widespread attention from both the global industry and academia. Simultaneously, the emergence of AI-generated content (AIGC), exemplified by ChatGPT, has revolutionized the mean of content creation in the metaverse. Providing meataverse users with diversified AIGC services anytime and anywhere to meet the demand for immersive and blended virtual-real experiences in the physical world has become a major challenge in the development of the metaverse. Considering the flexibility and mobility of unmanned aerial vehicles (UAVs), we innovatively incorporate multiple UAVs as one of the AIGC service providers and construct a multi-UAV assisted edge-enabled metaverse system in the context of AIGC-as-a-Service (AaaS) scenario. To solve the complex resource management and allocation problem in the aforementioned system, we formulate it as a Markov decision process (MDP) and propose utilizing the generative capabilities of the diffusion model in combination with the robust decision-making abilities of reinforcement learning to tackle these issues. In order to substantiate the efficacy of the proposed diffusion-based reinforcement learning framework, we propose a novel diffusion-based soft actor-critic algorithm for metaverse (Meta-DSAC). Subsequently, a series of experiments are executed and the simulation results empirically validate the proposed algorithm's comparative advantages of the ability to provide stable and substantial long-term rewards, as well as the enhanced capacity to model complex environment. © 2025 IEEE.