arXiv subjects

Binpu Shi

Publications and source records attributed to Binpu Shi.

3 recordsLinked to original sources

arXiv2026

PACC: Propagation-Aware Channel Charting with Physics-Guided Metric Learning

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.

eess.SP↗

arXiv2025

AMBER: An Adaptive Multimodal Mask Transformer for Beam Prediction with Missing Modalities

With the widespread adoption of millimeter-wave (mmWave) massive multi-input-multi-output (MIMO) in vehicular networks, accurate beam prediction and alignment have become critical for high-speed data transmission and reliable access. While traditional beam prediction approaches primarily rely on in-band beam training, recent advances have started to explore multimodal sensing to extract environmental semantics for enhanced prediction. However, the performance of existing multimodal fusion methods degrades significantly in real-world settings because they are vulnerable to missing data caused by sensor blockage, poor lighting, or GPS dropouts. To address this challenge, we propose AMBER ({A}daptive multimodal {M}ask transformer for {BE}am p{R}ediction), a novel end-to-end framework that processes temporal sequences of image, LiDAR, radar, and GPS data, while adaptively handling arbitrary missing-modality cases. AMBER introduces learnable modality tokens and a missing-modality-aware mask to prevent cross-modal noise propagation, along with a learnable fusion token and multihead attention to achieve robust modality-specific information distillation and feature-level fusion. Furthermore, a class-former-aided modality alignment (CMA) module and temporal-aware positional embedding are incorporated to preserve temporal coherence and ensure semantic alignment across modalities, facilitating the learning of modality-invariant and temporally consistent representations for beam prediction. Extensive experiments on the real-world DeepSense6G dataset demonstrate that AMBER significantly outperforms existing multimodal learning baselines. In particular, it maintains high beam prediction accuracy and robustness even under severe missing-modality scenarios, validating its effectiveness and practical applicability.

cs.IT↗

arXiv2025

SCAN-BEST: Sub-6GHz-Aided Near-field Beam Selection with Formal Reliability Guarantees

As millimeter-wave (mmWave) MIMO systems adopt larger antenna arrays, near-field propagation becomes increasingly prominent, especially for users close to the transmitter. Traditional far-field beam training methods become inadequate, while near-field training faces the challenge of large codebooks due to the need to resolve both angular and distance domains. To reduce in-band training overhead, prior work has proposed to leverage the spatial-temporal congruence between sub-6 GHz (sub-6G) and mmWave channels to predict the best mmWave beam within a near-field codebook from sub-6G channel estimates. To cope with the uncertainty caused by sub-6G/mmWave differences, we introduce a novel Sub-6G Channel Aided Near-field BEam SelecTion (SCAN-BEST) framework that wraps around any beam predictor to produce candidate beam subset with formal suboptimality guarantees. The proposed SCAN-BEST builds on conformal risk control (CRC), and is calibrated offline using limited calibration data. Its performance guarantees apply even in the presence of statistical shifts between calibration and deployment. Numerical results validate the theoretical properties and efficiency of SCAN-BEST.

cs.IT↗