AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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These results establish LC‐based, AM‐driven glass panels as an energy‐efficient and manufacturing‐scalable materials platform for future transparent and reconfigurable wireless infrastructures.
Achieving high-precision wireless sensing using existing communication waveforms is a core challenge in integrated sensing and communication (ISAC) systems. Particularly for multi-source localization, traditional covariance-based subspace methods suffer from high computational complexity and fail to utilize higher-orde...
Intelligent channel state information (CSI) feedback is essential for realizing the high capacity and spectral efficiency goals of future 6G systems, yet existing deep learning solutions face a trade-off between model generalization and scenario-specific performance. Large neural networks generalize well but incur high...
Accurate joint tracking of mobile users, surrounding scatterers, and dynamic channels is a critical task for sixth-generation (6G) wireless systems, essential for both ensuring high-quality communications and empowering advanced sensing applications such as autonomous driving and immersive extended reality. While extre...
Large-scale multiple-input multiple-output (MIMO) detection is fundamental to modern wireless networks but constrained by performance-complexity trade-offs. Existing detectors, whether classical or learning-based, often fall short in either scalability or generalizability across heterogeneous scenarios. To overcome the...
Radio-based simultaneous localization and mapping (radio-SLAM) offers a promising solution for user equipment (UE) localization and radio feature map construction without external positioning infrastructure, with its feasibility already demonstrated through integrated sensing and communication (ISAC) prototypes. Howeve...
Under incomplete observation, a population-level theory of deterministic radiomap blind prediction is established that identifies the conditional mean as its optimal target and separates prediction error into reducible predictor approximation and irreducible uncertainty caused by missing physical information.
Recent advances in 3D Gaussian Splatting (3DGS)-based wireless radiance field (WRF) reconstruction provide an efficient solution for wireless channel modeling. However, existing WRF reconstruction methods rely on pre-collected observations and offline optimization, and thus struggle to provide real-time channel knowled...
A unified framework for FM-based wireless localization that learns transferable channel representations from large-scale unlabeled channel state information and adapts to new environments with minimal or even no supervision is presented.
The rapid growth of the low-altitude economy calls for integrated sensing and communication (ISAC) networks capable of robust flight monitoring. This article advocates wireless imaging as a unifying sensing paradigm that enables ISAC networks to function as comprehensive low-altitude guardians. We present a hierarchica...
RadioDecomp is proposed, which treats a prior-guided predictor as a correctable base and uses deterministic residual refinement to learn its remaining predictable discrepancy, and is instantiate as RadioLSR (LoS-Shadow-Residual).
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