arXiv subjects

Weicao Deng

Publications and source records attributed to Weicao Deng.

4 recordsLinked to original sources

arXiv2026

Few-Shot Beam Selection for Embodied AI via In-Context Learning and Conformal Prediction

Millimeter-wave (mmWave) communications are essential for supporting massive multimodal sensory data exchange in Embodied Artificial Intelligence (EAI) systems. However, fast and reliable beam selection for mmWave communication remains a critical bottleneck due to the prohibitive overhead associated with conventional beam training. Although location-aided beam prediction offers a promising alternative, its performance is often limited by localization errors and the need for extensive training data. To address these challenges, we propose a novel framework termed In-Context Learning driven Cross-Validation Conformal Prediction (ICL+CVCP) for beam prediction. The proposed framework utilizes the training-free adaptation capability of ICL, enabling effective operation with only a small number of demonstration samples and eliminating the need for large-scale data collection and model fine-tuning. In addition, the CVCP module constructs compact, distribution-free candidate beam sets that provably contain the optimal beam with a user-defined probability. Experimental results confirm that ICL+CVCP consistently satisfies the desired empirical coverage while significantly reducing pilot overhead and computational latency, thereby meeting the stringent reliability and efficiency requirements of EAI applications.

eess.SP↗

arXiv2026

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↗

arXiv2025

Optimizing In-Context Learning for Efficient Full Conformal Prediction

Reliable uncertainty quantification is critical for trustworthy AI. Conformal Prediction (CP) provides prediction sets with distribution-free coverage guarantees, but its two main variants face complementary limitations. Split CP (SCP) suffers from data inefficiency due to dataset partitioning, while full CP (FCP) improves data efficiency at the cost of prohibitive retraining complexity. Recent approaches based on meta-learning or in-context learning (ICL) partially mitigate these drawbacks. However, they rely on training procedures not specifically tailored to CP, which may yield large prediction sets. We introduce an efficient FCP framework, termed enhanced ICL-based FCP (E-ICL+FCP), which employs a permutation-invariant Transformer-based ICL model trained with a CP-aware loss. By simulating the multiple retrained models required by FCP without actual retraining, E-ICL+FCP preserves coverage while markedly reducing both inefficiency and computational overhead. Experiments on synthetic and real tasks demonstrate that E-ICL+FCP attains superior efficiency-coverage trade-offs compared to existing SCP and FCP baselines.

cs.LG↗

arXiv2024

CSI Transfer From Sub-6G to mmWave: Reduced-Overhead Multi-User Hybrid Beamforming

Hybrid beamforming is vital in modern wireless systems, especially for massive MIMO and millimeter-wave (mmWave) deployments, offering efficient directional transmission with reduced hardware complexity. However, effective beamforming in multi-user scenarios relies heavily on accurate channel state information, the acquisition of which often requires significant pilot overhead, degrading system performance. To address this and inspired by the spatial congruence between sub-6GHz (sub-6G) and mmWave channels, we propose a Sub-6G information Aided Multi-User Hybrid Beamforming (SA-MUHBF) framework, avoiding excessive use of pilots at mmWave. SA-MUHBF employs a convolutional neural network to predict mmWave beamspace from sub-6G channel estimate, followed by a novel multi-layer graph neural network for analog beam selection and a linear minimum mean-square error algorithm for digital beamforming. Numerical results demonstrate that SA-MUHBF efficiently predicts the mmWave beamspace representation and achieves superior spectrum efficiency over state-of-the-art benchmarks. Moreover, SA-MUHBF demonstrates robust performance across varied sub-6G system configurations and exhibits strong generalization to unseen scenarios.

cs.IT↗