arXiv:2606. 04328v1 Announce Type: cross Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM.
By Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci
arXiv:2510. 21127v2 Announce Type: replace-cross Abstract: Despite rapid advancements in sensor networks, conventional battery-powered sensor networks suffer from limited operational lifespans and frequent maintenance requirements that severely constrain their deployment in remote and inaccessible environments.
By Bowei Tong, Hui Kang, Jiahui Li, Geng Sun, Jiacheng Wang, Yaoqi Yang, Bo Xu, Dusit Niyato
arXiv:2609.00284v1 Announce Type: cross
Abstract: Emerging 6G wireless networks are expected to operate across diverse deployment scenarios, where variations in network topology, user mobility, traff...
By Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci
arXiv:2605. 13221v2 Announce Type: replace Abstract: In cloud manufacturing, unmanned aerial vehicles (UAVs) can support both product collection and mobile edge computing (MEC).
By Hanwen Zhang, Dusit Niyato, Wei Zhang, Xin Lou, Malcolm Yoke Hean Low
arXiv:2605. 02965v2 Announce Type: replace Abstract: Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers.
By Yang Fu, Peng Qin, Liming Chen, Zihao Zhang, Hao Yu, Yifei Wang
arXiv:2507. 19712v3 Announce Type: replace-cross Abstract: In this paper, we explore mission assignment and task offloading in an Open Radio Access Network (Open RAN)-based intelligent transportation system (ITS), where autonomous vehicles leverage mobile edge computing for efficient processing.
By Ngoc Hung Nguyen, Nguyen Van Thieu, Quang-Trung Luu, Anh Tuan Nguyen, Senura Wanasekara, Nguyen Cong Luong, Fatemeh Kavehmadavani, Van-Dinh Nguyen
arXiv:2607. 18288v1 Announce Type: new Abstract: Load imbalance across edge and cloud layers degrades latency performance in hierarchical edge-cloud computing (HECC) systems under dynamic task arrivals and heterogeneous resources, leading to severe queuing delays and inefficient resource utilization.
By Vo Phi Son, Van-Dinh Nguyen, Ngoc Hung Nguyen, Trinh Van Chien, Symeon Chatzinotas
arXiv:2606. 10600v1 Announce Type: cross Abstract: Radio frequency wireless power transfer (RF-WPT) is an enabling technology for supporting uninterrupted communications in future Internet of Things systems by reducing the need for battery replacement and mitigating battery-waste-related issues.
By Amirhossein Azarbahram, Osmel M. Rosabal, David Ernesto Ruiz-Guirola, Melike Erol-Kantarci, Kaibin Huang, Onel L. A. L\'opez
The paper introduces a reinforcement‑learning‑guided evolutionary policy optimization framework for scheduling heterogeneous agile Earth observation satellites, addressing task selection, satellite assignment, and sequencing under diverse visibility windows, maneuvering constraints, energy use, and storage limits. It combines assignment‑based indirect encoding with decoder‑based cost evaluation to capture satellite‑dependent constraints while integrating task gain, energy savings, and load balance into a single utility metric. The resulting RLOSMEA algorithm uses reinforcement learning to select high‑level search operators, achieving higher weighted utility and more stable convergence than baseline metaheuristics across varied AEOS scenarios.
By He Wang, Junyu Wu, Hui Li, Yanjie Song, Witold Pedrycz, Liang Li
arXiv:2606. 28339v1 Announce Type: cross Abstract: Industrial 6G networks require ultra-reliable, low-latency, and energy-efficient connectivity in dynamic and blockage-prone environments, where conventional terrestrial deployments often fail to ensure stable coverage.
By Marwan Dhuheir, Thang X. Vu, Symeon Chatzinotas
In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures. Sustaining reliable operation in these systems requires intelligent management of highly volatile stored energy.
arXiv:2608.28878v1 Announce Type: cross
Abstract: This paper develops a hybrid offline-online multi-agent reinforcement learning framework based on decision transformers. The policy is first pretrain...
By Yiming Zhang, Kun Yang, Cong Shen, Dongning Guo