The paper presents an autonomous agent that designs machine learning algorithms for wireless power control, eliminating manual specification of architecture, loss, and training details. Using an autoresearch protocol, the agent iteratively edits a training script, runs experiments, and evaluates changes against a single metric, ultimately achieving 99.5% of a reference solution with vastly reduced inference cost. The agent’s discovered output parameterization matches the exact max‑min‑optimal allocation at the minimum percentile for all trained weights, demonstrating a principled, scalable approach to a complex, NP‑hard problem.
By Ahmad Khan, Akram Bin Sediq, Sara Azadegi Naeini, Raviraj S. Adve
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:2607. 27626v1 Announce Type: new Abstract: Safety-critical IoT systems such as industrial closed-loop control, V2X coordination, and remote teleoperation require every sensor's peak Age of Information (peak AoI, also abbreviated PAoI) to stay below a hard per-slot deadline, not merely an average bound.
By Wentao Zhang, Wentao Mo
arXiv:2606. 18272v1 Announce Type: cross Abstract: This paper presents an autonomous agentic resource negotiation framework designed to enable zero-touch network slicing in 6G architectures using Large Language Model (LLM) agents.
By Hatim Chergui, Claudia Carballo Gonz\'alez, Farhad Rezazadeh, Merouane Debbah
arXiv:2608. 09250v1 Announce Type: new Abstract: Federated learning (FL) must serve devices with varying computational capabilities.
By Bostan Khan, Masoud Daneshtalab
arXiv:2609.07428v1 Announce Type: cross
Abstract: In this article, we propose an online electronic counter-countermeasure (ECCM) framework designed to conceal the strategic decision-making processes...
By Sreedevi K, Nandhini K, Anup Aprem, Deepthi P P
The paper introduces GQ-FSL, a green quantized federated split learning framework designed for wireless edge networks. It uses stochastic quantization for both local training and wireless transmissions, allowing asymmetric precision between client and server submodels to balance device energy limits with global convergence. The authors develop energy models and a convergence bound for heterogeneous data, then formulate an optimization problem to set the DNN split point and precision levels, achieving lower energy consumption while meeting latency and accuracy targets.
By Idan Roth, Lutz Lampe
arXiv:2506. 18748v2 Announce Type: replace-cross Abstract: We consider resource allocation problems in multi-user wireless networks, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users.
By Yigit Berkay Uslu, Navid NaderiAlizadeh, Mark Eisen, Alejandro Ribeiro
arXiv:2607. 29659v1 Announce Type: new Abstract: Deploying state-of-the-art deep neural networks (DNNs) at the wireless edge is severely bottlenecked by the strict energy and resource constraints of mobile devices.
By Idan Roth, Lutz Lampe
arXiv:2608. 00855v1 Announce Type: cross Abstract: Uncrewed aerial vehicle (UAV)-enabled federated learning (FL) can provide flexible, on-demand edge intelligence for large-scale IoT deployments, but operating in shared unlicensed bands makes uplink update delivery interference-coupled and unreliable.
By Masoud Ghazikor, Zhou Ni, Morteza Hashemi
arXiv:2606. 03664v1 Announce Type: cross Abstract: Ultra Reliable and Low Latency Communications (URLLC) was one of the main motivations behind 5G, with 3GPP advertising 1-10 ms latency targets for applications such as industrial automation, Vehicle-To-Everything (V2X), tactical edge networking, and unmanned-system control.
By Maxime Elkael, Michele Polese, Yunseong Lee, Koichiro Furueda, Tommaso Melodia
arXiv:2607. 13160v1 Announce Type: cross Abstract: Active beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) enables hybrid transmitting and reflecting mode to achieve effective signal amplification and full-space coverage, thus providing a promising solution for blockage-aware uplink offloading in heterogeneous mobile edge computing (MEC) systems.
By Tianyu Pang, Hongyu Li