arXiv subjects

Junchi Liu

Publications and source records attributed to Junchi Liu.

7 recordsLinked to original sources

arXiv2026

Adaptive Payload-Aided Near-Field Beam Tracking via Thompson Sampling

Extremely large antenna arrays at high frequencies substantially extend the radiative near-field region in 6G networks, making mobile beam alignment depend jointly on user angle and range. The added range dimension enlarges the beam-search space, making repeated pilot-based sweeping costly under mobility, while sensing-assisted tracking depends on propagation conditions, echo quality, and target reflectivity. We propose an adaptive payload-aided near-field beam-tracking framework based on maximum likelihood estimation (MLE) and Thompson sampling (TS). Selected received payload samples are fed back and reused as tracking observations, avoiding dedicated beam-sweeping symbols during tracking. Within each sliding window, local angle and range trajectories are modeled by low-order polynomials to capture velocity, acceleration, and higher-order motion variations, and are estimated by MLE using the spherical-wave channel model. A local Gaussian approximation centered at the MLE, with covariance from the inverse observed Fisher information, represents trajectory uncertainty. For payload transmissions selected for feedback, TS samples a trajectory hypothesis and maps it to a payload beam toward the sampled state, while the remaining transmissions use the MLE-predicted beam. To handle nonstationary mobility, an asymptotic chi-square characterization of in-window estimation risk motivates joint adaptation of observation-window length and polynomial degree, while online residual statistics adjust the update interval and feedback ratio. The framework is also extended to uniform planar arrays with elevation tracking. Simulations under smooth and sharp-turn trajectories show high payload-accounted mean normalized beamforming gain, low normalized-gain variance, and high effective-symbol reliability, while the adaptive mechanism provides substantial robustness under nonstationary sharp-turn mobility.

eess.SP↗

arXiv2026

Rethinking Automated Program Repair: The Impact of Bug Complexity, Fault Localization, and LLM Cost-efficiency

Background: Software bugs remain a critical challenge in development, necessitating effective Automated Program Repair (APR) techniques. While Large Language Model (LLM)-based APR systems have shown promise, prior studies primarily focus on overall repair effectiveness. The effects of bug complexity, fault localization, reasoning settings, and repair cost-effectiveness remain insufficiently explored. Aims: This study presents a comprehensive empirical analysis of LLM-based APR, focusing on how repair performance is shaped by bug complexity, fault localization, reasoning settings, and costs. Method: We evaluate two APR techniques (ChatRepair and CodeCorrector) using three LLMs (DeepSeek, GPT, and Llama), and examine their performance across diverse levels of bug complexity and localization strategies through a multi-dimensional empirical framework and statistical analysis. Results: Although structurally complex bugs and imprecise fault localization make repair more challenging, LLM-based APR techniques still achieve competitive repair effectiveness. Imprecise fault localization can substantially enlarge the performance gap between APR techniques. Furthermore, higher-cost LLMs and stronger reasoning settings do not consistently yield better cost-efficiency, revealing a nontrivial trade-off between repair effectiveness and computational cost. Conclusions: Over 50% of moderately complex bugs can be repaired by low-cost LLM-based APR techniques. The repair effectiveness gap between APR techniques becomes larger as fault localization becomes less precise. GPT-5 repairs 7 and 39 more complex bugs than DeepSeek-V4-pro and DeepSeek-V3.2, respectively; whereas the total repair cost of DeepSeek-V3.2 shows the best cost-efficiency performance.

cs.SE↗

arXiv2026

Efficient Near Field Beam Tracking via Thompson Sampling

The shift to the radiative near field region due to large antenna arrays necessitates beamforming that accounts for both angle and range, evolving mobility management into a joint angular range tracking challenge. Conventional schemes rely on rigid pilot payload structures with dedicated training slots, which interrupt data transmission and degrade spectral efficiency. To address this, we propose a pilot-free beam tracking framework leveraging Thompson sampling(TS). Within each sliding window, the user trajectory is modeled by local low-order polynomials in angle and range, and the motion parameters are estimated by maximum likelihood with uncertainty quantified via the Fisher information matrix. TS adaptively probes uncertain trajectory regions using beams that simultaneously serve as payload beams. Simulations demonstrate that the proposed framework maintains reliable connectivity while eliminating the overhead of dedicated pilot-based beam sweeping.

eess.SP↗

arXiv2026

Efficient, Adaptive Near-Field Beam Training based on Linear Bandit

This paper proposes a posterior sampling-based beam training framework for near-field communication under multi-path channels. By leveraging Thompson Sampling (TS), the framework adaptively balances exploration and exploitation to maximize the final beamforming gain under a finite pilot overhead. To ensure data-efficient learning, we incorporate a structured Gaussian prior in the DFT domain and use an RBF covariance as a tractable local-correlation prior, motivated by near-field energy leakage, to promote information sharing among neighboring DFT components. We develop three TS strategies: codebook-constrained search for rapid stabilization via structural regularization, continuous-space search for high accuracy refinement, and a two-stage hybrid refinement scheme that balances training efficiency and estimation accuracy. Simulation results show that the proposed framework reduces pilot overhead by over 90\% while achieving more than a 2 dB SNR gain over baselines in multipath environments. Furthermore, simulations show that the continuous-space search approaches the full-CSI benchmark as the available pilot numbers become sufficiently large.

eess.SP↗

arXiv2025

SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm, Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR), that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 times acceleration compared to conventional SR implementations that heavily rely on first principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.

physics.comp-ph↗

arXiv2024

M\textbf{\textit{O}}enes family materials with Dirac nodal loop, strong light-harvesting ability, long carrier lifetime and conduction-band valley spin splitting

M\textbf{\textit{O}}enes, as emerging MXenes-like materials, also have wide structural spaces and various chemical and physical properties. Using first-principles and high-throughput calculations, we have built an online library (\url{https://moenes.online}) for M\textbf{\textit{O}}enes family materials from basic summaries, mechanical, phonon and electron aspects, based on their structural diversities from 2 stoichiometric ratios, 11 early-transition metals, 4 typical functional groups and 4 oxygen group elements. Compared to MXenes, the main advantage of M\textbf{\textit{O}}enes at present is that we have discovered 14 direct semiconductors, which greatly increases the number of direct semiconductors and the range of band gap values in the MXenes family. Among them, 1T-Ti$_{2}$\textit{\textbf{O}}F$_{2}$ (\textbf{\textit{O}}=O, S, Se) reveal tunable semiconducting features and strong light-harvesting ability ranging from the ultraviolet to the near-infrared region. Besides, 2H- and 1T-Y$_{2}$TeO$_{2}$ have a long carrier lifetime of 2.38 and 1.24 ns, originating from their spatially distinguished VBM and CBM states and long dephasing times. In addition, 2H-Zr$_{2}$O(O)$_{2}$ shows spin-valley coupling phenomena, and the valley spin splitting is apparent and robust in its conduction band ($\sim$85 meV). Therefore, M\textbf{\textit{O}}enes have a wealth of physical properties, not limited to those reported here, and future studies of these emerging M\textbf{\textit{O}}enes are appealing.

cond-mat.mtrl-sci↗

arXiv2024

Realization of permutation groups by quantum circuit

In this paper, we exclusively utilize CNOT gates for implementing permutation groups generated by more than two elements. In Lemma 1, we recall that three CNOT gates are both necessary and sufficient to execute a two-qubit swap gate operation. Subsequently, in Lemma 2, we show that the maximum number of CNOT gates needed to carry out an n-qubit substitution operation is 3(n-1). Moving forward, our analysis in Section 3 reveals that utilizing five or fewer CNOT gates is insufficient for implementing a three-qubit swap gate corresponding to the permutation element (123). Hence six CNOT gates are both necessary and sufficient for implementing (123). This is done by employing a graph-theoretic approach to rigorously validate the results in terms of at most five CNOT gates. Using computational tools, we exhaustively explore all valid circuit diagrams containing exactly six CNOT gates to successfully execute the swap gate for (123), by explaining the equivalence classes in Remark 6 and Table 2. We conclude them in Theorem 7.To extend our analysis to the multiqubit scenario, we present the reducible and irreducible permutation elements in Definition 8. We clarify the equivalence between rows in the multi-qubit space and provide an approximate upper bound for multi-qubits to perform the aforementioned operations in Theorem 9. The comprehensive exploration of this paper aims to pave the way for further advancements in understanding quantum circuit optimization via multiple use of a specific two-qubit gate.

quant-ph↗