Selected work

Representative Papers

Comparative Analysis of Ray Tracing and Rayleigh Fading Models for Distributed MIMO Systems in Industrial Environments

Mar 03, 2025

This work addresses channel modeling and performance evaluation of distributed MIMO (D-MIMO) in industrial environments. We systematically compare, for the first time, deterministic ray-tracing models against stochastic Rayleigh fading models in predicting downlink/uplink single-user capacity. Leveraging a real-world 3D factory map, we construct multiple deployment scenarios to quantify how network densification affects user equipment (UE) multi-access point (AP) connectivity and coverage gain. Results show that densification significantly enhances D-MIMO capacity. Ray tracing more accurately captures spatial correlation and realistic propagation characteristics, whereas the Rayleigh model offers superior computational efficiency and maintains acceptable prediction error (<15%) in typical factory settings. The study establishes fundamental trade-offs among modeling accuracy, spatial correlation fidelity, and computational overhead, providing both theoretical guidance and empirical evidence for selecting appropriate D-MIMO channel models in industrial wireless systems.

1 citations1 influentialRead paper

Complete Suffix Prediction for Recommendation via Latent Retrieval over Process Graphs

Sep 14, 2026

研究解决了序列决策中完整后缀预测问题，通过基于图的度量学习框架和边缘条件图神经网络编码前缀和后缀，并在过程图上进行潜在检索。

0 citationsRead paper

LEED: Local Embedding Evolution Distance for over-smoothing estimation and virtual node selection in GNN

Aug 10, 2026

Graph neural networks often suffer from oversmoothing and overcompression, yet existing global metrics struggle to characterize representation degradation at the node level. To address this, this work proposes LEED—a node-level local metric that fine-grainedly quantifies oversmoothing by tracking the embedding evolution distance of each node across layers. This metric further serves as a unified guide for constructing local virtual nodes to mitigate overcompression. Departing from multiple heuristic centrality strategies, LEED uniquely adopts embedding evolution distance as a unified criterion for both diagnosis and optimization. It preserves global assessment capability while enabling precise, node-wise diagnostics, leading to significant performance improvements across multiple benchmark datasets.

0 citationsRead paper

Sinkhorn Hamiltonian Monte Carlo for Entropic Optimal Transport Generalized Bayes

Jul 30, 2026

This work addresses the limitations of traditional Bayesian inference, which relies on exact likelihoods and suffers when the likelihood is misspecified, intractable, or misaligned with the target discrepancy. The authors propose the first integration of Sinkhorn divergence as a generalized Bayesian loss within Hamiltonian Monte Carlo (HMC) and its adaptive variant NUTS, accommodating both balanced and unbalanced optimal transport settings. They further incorporate common random numbers to handle stochastic simulators and introduce a heuristic for hyperparameter selection that preserves gradient consistency with Hamiltonian dynamics. Empirical evaluations on Gaussian models, noisy spiral manifolds, pulse misalignment, and CIFAR-10 image patch alignment demonstrate the method’s effectiveness, robustness, and the nuanced differences between transport mechanisms.

0 citationsRead paper

Evaluating Power Control Strategies for UORA in IEEE 802.11be Systems with Capture Effect

Jul 20, 2026

This study addresses the fairness issues in uplink random access under the IEEE 802.11be UORA mechanism, particularly in the presence of capture effects and uneven spatial distribution of stations. For the first time, it systematically evaluates the interplay between two power control strategies and capture effects. Through system-level simulations integrating the standard UORA mechanism, power control algorithms, and a capture effect model, the work reveals the critical influence of spatial distribution on multi-user uplink random access performance. The results demonstrate that the adopted power control strategies significantly improve access success probability, reduce latency, enhance resource utilization, and increase energy efficiency—thereby boosting overall system throughput while maintaining fairness among users.

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Recent publications

Latest Papers

Complete Suffix Prediction for Recommendation via Latent Retrieval over Process Graphs

Sep 14, 2026

研究解决了序列决策中完整后缀预测问题，通过基于图的度量学习框架和边缘条件图神经网络编码前缀和后缀，并在过程图上进行潜在检索。

0 citationsRead paper

LEED: Local Embedding Evolution Distance for over-smoothing estimation and virtual node selection in GNN

Aug 10, 2026

Graph neural networks often suffer from oversmoothing and overcompression, yet existing global metrics struggle to characterize representation degradation at the node level. To address this, this work proposes LEED—a node-level local metric that fine-grainedly quantifies oversmoothing by tracking the embedding evolution distance of each node across layers. This metric further serves as a unified guide for constructing local virtual nodes to mitigate overcompression. Departing from multiple heuristic centrality strategies, LEED uniquely adopts embedding evolution distance as a unified criterion for both diagnosis and optimization. It preserves global assessment capability while enabling precise, node-wise diagnostics, leading to significant performance improvements across multiple benchmark datasets.

0 citationsRead paper

Sinkhorn Hamiltonian Monte Carlo for Entropic Optimal Transport Generalized Bayes

Jul 30, 2026

This work addresses the limitations of traditional Bayesian inference, which relies on exact likelihoods and suffers when the likelihood is misspecified, intractable, or misaligned with the target discrepancy. The authors propose the first integration of Sinkhorn divergence as a generalized Bayesian loss within Hamiltonian Monte Carlo (HMC) and its adaptive variant NUTS, accommodating both balanced and unbalanced optimal transport settings. They further incorporate common random numbers to handle stochastic simulators and introduce a heuristic for hyperparameter selection that preserves gradient consistency with Hamiltonian dynamics. Empirical evaluations on Gaussian models, noisy spiral manifolds, pulse misalignment, and CIFAR-10 image patch alignment demonstrate the method’s effectiveness, robustness, and the nuanced differences between transport mechanisms.

0 citationsRead paper

Evaluating Power Control Strategies for UORA in IEEE 802.11be Systems with Capture Effect

Jul 20, 2026

This study addresses the fairness issues in uplink random access under the IEEE 802.11be UORA mechanism, particularly in the presence of capture effects and uneven spatial distribution of stations. For the first time, it systematically evaluates the interplay between two power control strategies and capture effects. Through system-level simulations integrating the standard UORA mechanism, power control algorithms, and a capture effect model, the work reveals the critical influence of spatial distribution on multi-user uplink random access performance. The results demonstrate that the adopted power control strategies significantly improve access success probability, reduce latency, enhance resource utilization, and increase energy efficiency—thereby boosting overall system throughput while maintaining fairness among users.

0 citationsRead paper

Fast SDP certification of neural networks : towards large multi-class datasets

Jul 03, 2026

This work addresses the high computational cost in verifying adversarial robustness of multi-class neural networks, which traditionally requires separate optimization for each target class. To overcome this limitation, the authors propose a unified quadratic model that jointly verifies all target classes through a single semidefinite programming (SDP) relaxation. The approach introduces an active neuron pruning strategy to reduce problem dimensionality and accelerate SDP convergence. Notably, this is the first method to handle all output classes simultaneously within a single optimization framework, substantially improving verification efficiency. Experimental results demonstrate that the proposed technique significantly speeds up robustness certification, enabling SDP-based verification to scale to large-scale multi-class datasets.

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