PACC designs a propagation-aware dissimilarity metric that adapts to line-of-sight and non-line-of-sight propagation conditions, thereby preserving both local neighborhood relationships and the intrinsic geometry of the radio environment.
Abstract
Channel charting has emerged as a promising paradigm that maps high-dimensional channel state information into a low-dimensional latent space, facilitating tasks such as radio environment sensing and beam management. However, existing methods often rely on precise user locations or timestamp-based pseudo-labels, which are difficult to obtain in privacy-sensitive scenarios and rapidly varying wireless environments. To address these limitations, we propose propagation-aware channel charting with physics-guided metric learning (PACC), a location-free framework that constructs channel-domain supervision from propagation characteristics without requiring explicit geographic information. Specifically, PACC designs a propagation-aware dissimilarity metric that adapts to line-of-sight and non-line-of-sight propagation conditions, thereby preserving both local neighborhood relationships and the intrinsic geometry of the radio environment. Simulation results demonstrate that PACC consistently outperforms both classical dimensionality-reduction methods and state-of-the-art learning-based channel-charting approaches under diverse propagation conditions.
A channel knowledge map (CKM) provides location-specific channel priors and can reduce the overhead of real-time channel state information (CSI) acquisition for 6G environment-aware communications. In practice, CKM generation is often constrained by sparse and noisy measurements due to the high cost of wireless data collection. In this paper, we propose PDiff, a physics-informed conditional diffusion framework for CKM generation under sparse observations. Specifically, PDiff incorporates an analytical free-space propagation prior to capture the dominant distance-dependent attenuation trend, and combines it with environmental geometry, observation masks, and sparse observations as structured conditions. These conditional inputs guide the generation process with explicit propagation-aware, environmental, and measurement constraints. To improve inference efficiency, we further develop Prop-Cache, a training-free acceleration mechanism that reuses slowly varying intermediate features across denoising steps to reduce redundant computation during sampling. Experiments on RadioMapSeer demonstrate that PDiff outperforms a wide range of baseline methods for CKM generation.
Yu Chen, Jiao Chen, Jian Tang et al.· IEEE Transactions on Network...· 0 citations
This work introduces a physics- and tail-informed VAE-EVT (variational autoencoder-extreme value theory) framework that distinctly models both the bulk and tail distribution of SNR, and significantly outperforms the state-of-the-art GAN-based model.
A. Gamage, Niloofar Mehrnia, James Gross· 0 citations
Channel state information (CSI) acquisition, reconstruction, and prediction are fundamental yet costly tasks in modern MIMO-OFDM wireless systems. Direct coefficient-level prediction of raw CSI is fragile in realistic propagation environments, since small spatial perturbations, local scattering changes, and phase variations can cause large errors in the complex channel domain. However, the underlying wireless propagation field still contains stable and predictable structures that can be exploited across time, frequency, antenna, and carrier dimensions. Motivated by this observation, we propose a JEPA-based field-layer world model (FWM) that learns a shared latent propagation state from multi-resolution CSI observations across the considered carrier bands and predicts its task-relevant evolution in the latent domain. The proposed FWM maps multiple CSI observation resolutions to a shared latent propagation-field space through scale-specific tokenizer heads. A latent prediction backbone is then trained to infer masked or future field states, while an incremental multi-scale alignment strategy allows new observation scales to be incorporated without retraining the entire model from scratch. For downstream reconstruction, the predicted latent field is used as a structured prior and combined with sparse current pilots. Experiments on single-band and cross-band reconstruction demonstrate improved symbol detection and, more notably, substantial beamforming gains despite modest NMSE improvements, indicating that FWM captures task-relevant spatial propagation structure beyond coefficient-wise CSI fitting.
Yuzhi Yang, Brahim Mefgouda, Han Zou et al.· 0 citations
A deep learning-based approach is presented that optimizes environmental input construction for accurate channel path loss prediction and validate that the input feature construction improves channel prediction accuracy and provides guidance for future intelligent channel prediction.
Zhicheng Qiu, Rui-Si He, Bo Ai et al.· npj Wireless Technology· 0 citations
Accurate wireless localization is a key enabler for 6G networks, yet remains challenging under diverse and rapidly changing propagation conditions. Model-based methods degrade when multipath channels are non-resolvable and model mismatches occur, while supervised deep learning demands large labeled datasets and generalizes poorly to new deployments. Inspired by foundation models (FMs) in language and vision, this article presents 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. We review the fundamentals of FMs, compare the FM paradigm with existing localization approaches, and introduce a three-stage framework spanning large-scale pretraining, localization-oriented fine-tuning, and context-augmented inference, together with the location-aware applications it enables. Ray-tracing-based case studies show improved positioning accuracy and cross-environment generalization. Finally, we present an outlook on key research directions toward AI-native networks for wireless localization.
Guangjin Pan, Jiajia Guo, Zheng Xing et al.· 0 citations
Channel charting (CC) is a self-supervised learning technique which aims to construct a lower-dimensional representation of the channel measurements, while preserving the neighboring relationship of users. In this letter, we propose a machine learning approach for radio-based localization task with the aid of CC, which fully exploits the dissimilarity extracted solely from the channel measurements. To this end, a hybrid model structure inspired by physical principles is employed, which is realized by a computationally efficient two-layer neural network initialized with a channel chart. The training process employs a weakly-supervised approach that combines: 1) a Siamese network architecture preserving relative user neighborhood relationship through channel dissimilarity metrics, and 2) a limited set of anchor points with ground-truth location annotations to establish absolute positional references. The proposed approach is empirically validated on realistic channel data, achieving encouraging localization accuracy compared to benchmark approaches.
Tianxiang Xu, Li You, Jue Wang et al.· IEEE Wireless Communications...· 0 citations
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