arXiv:2607. 02537v1 Announce Type: cross Abstract: Jamming and spoofing pose significant threats to wireless and satellite navigation by disrupting radio-frequency (RF) signals and compromising availability and integrity.
By Nisha L. Raichur, Lucas Heublein, Dominik Seu{\ss}, Frank Deinzer, Felix Ott
The paper proposes a CSI‑free hierarchical multi‑agent reinforcement learning framework for controlling reconfigurable reflective surfaces in millimeter‑wave networks. By replacing per‑element channel estimation with user localization data, the system uses a two‑tier neural architecture: a high‑level controller for discrete user‑to‑reflector assignments and low‑level controllers that optimize continuous focal points via MAPPO under a CTDE scheme. Deterministic ray‑tracing tests show RSSI gains of up to 7.79 dB over centralized PPO baselines and robust performance with sub‑meter localization errors for multiple users and reflector arrays.
By Hieu Le, Mostafa Ibrahim, Oguz Bedir, Jian Tao, Sabit Ekin
arXiv:2608. 14599v1 Announce Type: cross Abstract: The placement of base station (BS) is a fundamental determinant of coverage and capacity of urban wireless networks.
By Zhenyu Tao, Yuxuan Li, Wei Xu, Yongming Huang, Xiaohu You
The paper introduces Physics-Unrolled Hybrid Neural Operator (PU‑HNO), a three‑stage cascade that transforms low‑fidelity ray‑tracing outputs and scene priors into high‑fidelity indoor radio maps by sequentially modeling reflection, diffraction, and scattering. It demonstrates that, under conditionally unbiased label noise, the model can learn stable propagation structures and surpass its own training labels. Experiments on varied floorplans show PU‑HNO outperforming image‑to‑image baselines, wireless learning models, and monolithic neural operators in both image quality and wireless deployment metrics.
By Rafid Umayer Murshed, Saif Ur Rahman, Mingyue Tang, Elahe Soltanaghai
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:2609.26214v1 Announce Type: new
Abstract: We present a methodology that places a 3D digital twin (DT) of the environment as the main enabler behind the development of radio sensing at scale. Th...
By \'Eloi Sainte-Beuve (Orange Research), Guillaume Larue (Orange Research), Louis-Adrien Dufr\`ene (Orange Research), Quentin Lampin (Orange Research), Ali Al Khansa (Orange Research)