arXiv subjects

Bintao Hu

Publications and source records attributed to Bintao Hu.

3 recordsLinked to original sources

arXiv2026

AI/ML for mobile networks: Current status in Rel. 19 and challenges ahead

The transformative power of artificial intelligence (AI) and machine learning (ML) is recognized as a key enabler for sixth generation (6G) mobile networks by both academia and industry. Research on AI/ML in mobile networks has been ongoing for years, and the 3rd generation partnership project (3GPP) launched standardization efforts to integrate AI into mobile networks. However, a comprehensive review of the current status and challenges of the standardization of AI/ML for mobile networks is still missing. To this end, we provided a comprehensive review of the standardization efforts by 3GPP on AI/ML for mobile networks. This includes an overview of the general AI/ML framework, representative use cases (i.e., CSI feedback, beam management and positioning), and corresponding evaluation matrices. We emphasized the key research challenges on dataset preparation, generalization evaluation and baseline AI/ML models selection. Using CSI feedback as a case study, given the test dataset 2, we demonstrated that the pre-training-fine-tuning paradigm (i.e., pre-training using dataset 1 and fine-tuning using dataset 2) outperforms training on dataset 2. Moreover, we observed the highest performance enhancements in Transformer-based models through fine-tuning, showing its great generalization potential at large floating-point operations (FLOPs). Finally, we outlined future research directions for the application of AI/ML in mobile networks.

eess.SP↗

arXiv2026

ACDC: Adaptive Curriculum Planning with Dynamic Contrastive Control for Goal-Conditioned Reinforcement Learning in Robotic Manipulation

Goal-conditioned reinforcement learning has shown considerable potential in robotic manipulation; however, existing approaches remain limited by their reliance on prioritizing collected experience, resulting in suboptimal performance across diverse tasks. Inspired by human learning behaviors, we propose a more comprehensive learning paradigm, ACDC, which integrates multidimensional Adaptive Curriculum (AC) Planning with Dynamic Contrastive (DC) Control to guide the agent along a well-designed learning trajectory. More specifically, at the planning level, the AC component schedules the learning curriculum by dynamically balancing diversity-driven exploration and quality-driven exploitation based on the agent's success rate and training progress. At the control level, the DC component implements the curriculum plan through norm-constrained contrastive learning, enabling magnitude-guided experience selection aligned with the current curriculum focus. Extensive experiments on challenging robotic manipulation tasks demonstrate that ACDC consistently outperforms the state-of-the-art baselines in both sample efficiency and final task success rate.

cs.RO↗

arXiv2026

Dynamic Channel Charting: An LSTM-AE-based Approach

With the development of the sixth-generation (6G) communication system, Channel State Information (CSI) plays a crucial role in improving network performance. Traditional Channel Charting (CC) methods map high-dimensional CSI data to low-dimensional spaces to help reveal the geometric structure of wireless channels. However, most existing CC methods focus on learning static geometric structures and ignore the dynamic nature of the channel over time, leading to instability and poor topological consistency of the channel charting in complex environments. To address this issue, this paper proposes a novel time-series channel charting approach based on the integration of Long Short-Term Memory (LSTM) networks and Auto encoders (AE) (LSTM-AE-CC). This method incorporates a temporal modeling mechanism into the traditional CC framework, capturing temporal dependencies in CSI using LSTM and learning continuous latent representations with AE. The proposed method ensures both geometric consistency of the channel and explicit modeling of the time-varying properties. Experimental results demonstrate that the proposed method outperforms traditional CC methods in various real-world communication scenarios, particularly in terms of channel charting stability, trajectory continuity, and long-term predictability.

eess.SP↗