Zobrazeno 1 - 10
of 369
pro vyhledávání: '"Luan, Tom."'
As a pivotal virtualization technology, network digital twin is expected to accurately reflect real-time status and abstract features in the on-going sixth generation (6G) networks. In this article, we propose an artificial intelligence (AI)-native n
Externí odkaz:
http://arxiv.org/abs/2410.01584
Large Model Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends
Large Model (LM) agents, powered by large foundation models such as GPT-4 and DALL-E 2, represent a significant step towards achieving Artificial General Intelligence (AGI). LM agents exhibit key characteristics of autonomy, embodiment, and connectiv
Externí odkaz:
http://arxiv.org/abs/2409.14457
This article presents a digital twin (DT)-enhanced reinforcement learning (RL) framework aimed at optimizing performance and reliability in network resource management, since the traditional RL methods face several unified challenges when applied to
Externí odkaz:
http://arxiv.org/abs/2406.07857
Semantic communication, emerging as a breakthrough beyond the classical Shannon paradigm, aims to convey the essential meaning of source data rather than merely focusing on precise yet content-agnostic bit transmission. By interconnecting diverse int
Externí odkaz:
http://arxiv.org/abs/2405.01221
This paper considers optimal traffic signal control in smart cities, which has been taken as a complex networked system control problem. Given the interacting dynamics among traffic lights and road networks, attaining controller adaptivity and scalab
Externí odkaz:
http://arxiv.org/abs/2311.03756
Deep Reinforcement Learning (DRL) is widely used to optimize the performance of multi-UAV networks. However, the training of DRL relies on the frequent interactions between the UAVs and the environment, which consumes lots of energy due to the flying
Externí odkaz:
http://arxiv.org/abs/2310.16302
In Space-air-ground integrated networks (SAGIN), the inherent openness and extensive broadcast coverage expose these networks to significant eavesdropping threats. Considering the inherent co-channel interference due to spectrum sharing among multi-t
Externí odkaz:
http://arxiv.org/abs/2308.14348
Autor:
Wang, Xiucheng, Cheng, Nan, Fu, Lianhao, Quan, Wei, Sun, Ruijin, Hui, Yilong, Luan, Tom, Shen, Xuemin
Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalabi
Externí odkaz:
http://arxiv.org/abs/2306.08938
With the widespread use of large artificial intelligence (AI) models such as ChatGPT, AI-generated content (AIGC) has garnered increasing attention and is leading a paradigm shift in content creation and knowledge representation. AIGC uses generative
Externí odkaz:
http://arxiv.org/abs/2305.18339
Due to the limited battery and computing resource, offloading unmanned aerial vehicles (UAVs)' computation tasks to ground infrastructure, e.g., vehicles, is a fundamental framework. Under such an open and untrusted environment, vehicles are reluctan
Externí odkaz:
http://arxiv.org/abs/2305.08691