arXiv subjects

Xiaonan Chen

Publications and source records attributed to Xiaonan Chen.

7 recordsLinked to original sources

arXiv2026

A One-Third Bound for the Maker--Breaker Degree Game

Let $H$ be a finite hypergraph with rank $r$ and minimum degree $d$. In the Maker--Breaker degree game, Maker and Breaker alternately claim previously unclaimed hyperedges of $H$, with Maker moving first, and Breaker seeks to maximize the minimum degree $δ(H_{\mathrm B})$ of his spanning subhypergraph $H_{\mathrm B}$. We prove that, for every fixed $r\ge2$ and all sufficiently large $d$, Breaker has a deterministic strategy satisfying \[ δ(H_{\mathrm B})\ge \frac{d}{r+1}-\sqrt{d\log d}. \] For graphs, this improves the classical universal lower bound $d/4$ to $d/3-\sqrt{d\log d}$. The proof introduces a virtual balancing process, encodes local imbalance by a multiplicative risk, and keeps the resulting risk vector in the Shearer region throughout the game.

math.CO↗

arXiv2026

Exact noise characterization of entanglement distribution in star networks

Multipartite entanglement forms the core of many networking applications. In the near-term future, it is expected that multipartite distribution will be achieved first through star topologies, making it important to understand the noise incurred during the distribution process. In such networks, elementary links are created stochastically and successful links must be stored while waiting for the remaining links, causing memory decoherence that depends on the random waiting times. We derive analytical expressions for both the average noise and its distribution, when distributing GHZ states under memory dephasing in star networks. We study and compare two distribution protocols: the factory and piecemaker protocol. Furthermore, we find expressions for the case of a global cut-off (allowing fast optimization of the cut-off without requiring Monte Carlo simulations) and extend the analysis for the factory protocol to depolarizing noise for arbitrary states.

quant-ph↗

arXiv2026

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation

Accurate channel estimation remains challenging in high-mobility wireless systems because Doppler shifts induce severe inter-carrier interference (ICI) in Orthogonal Frequency Division Multiplexing (OFDM). We propose an unsupervised online channel estimation framework based on Implicit Neural Representation (INR). Unlike discrete-grid estimators, the proposed method decouples channel representation from the OFDM sampling resolution by modeling the time-varying frequency-selective channel as a continuous function of time-frequency coordinates. A Sinusoidal Representation Network (SIREN) with Gaussian Fourier feature mapping captures fine-grained channel variations and high-frequency details without offline pre-training or labeled data. For each received slot, the network parameters are updated by per-slot online fitting that minimizes a physics-aware ICI loss, while a confidence-aware decision-directed loop balances reliable pilots and dynamically harvested pseudo-pilots. Simulations in realistic Vehicle-to-Everything (V2X) environments show that the proposed method achieves near-optimal link-level reliability, significantly outperforming Least Squares (LS) and robust Linear Minimum Mean Square Error (LMMSE) estimators. Compared with supervised deep learning baselines, it also exhibits strong out-of-distribution (OOD) robustness under environmental distribution shifts, establishing an adaptable data-efficient physical-layer paradigm.

eess.SP↗

arXiv2025

Improved Sublinear Algorithms for Classical and Quantum Graph Coloring

We present three sublinear randomized algorithms for vertex-coloring of graphs with maximum degree $Δ$. The first is a simple algorithm that extends the idea of Morris and Song to color graphs with maximum degree $Δ$ using $Δ+1$ colors. Combined with the greedy algorithm, it achieves an expected runtime of $O(n^{3/2}\sqrt{\log n})$ in the query model, improving on Assadi, Chen, and Khanna's algorithm by a $\sqrt{\log n}$ factor in expectation. When we allow quantum queries to the graph, we can accelerate the first algorithm using Grover's famous algorithm, resulting in a runtime of $\tilde{O}(n^{4/3})$ quantum queries. Finally, we introduce a quantum algorithm for $(1+ε)Δ$-coloring, achieving $O(ε^{-1}n^{5/4}\log^{3/2}n)$ quantum queries, offering a polynomial improvement over the previous best bound by Morris and Song.

cs.DS↗

arXiv2023

Communication under Mixed Gaussian-Impulsive Channel: An End-to-End Framework

In many communication scenarios, the communication signals simultaneously suffer from white Gaussian noise (WGN) and non-Gaussian impulsive noise (IN), i.e., mixed Gaussian-impulsive noise (MGIN). Under MGIN channel, classical communication signal schemes and corresponding detection methods usually can not achieve desirable performance as they are optimized with respect to WGN. Moreover, as the widely adopted IN model has no analytical and general closed-form expression of probability density function (PDF), it is extremely hard to obtain optimal communication signal and corresponding detection schemes based on classical stochastic signal processing theory. To circumvent these difficulties, we propose a data-driven end-to-end framework to address the communication signal design and detection under MGIN channel in this paper. In this proposed framework, a channel noise simulator (CNS) is elaborately designed based on an improved generative adversarial net (GAN) to simulate the MGIN without requirement of any analytical PDF. Meanwhile, a multi-level wavelet convolutional neural network (MWCNN) based preprocessing network is used to mitigate the negative effect of outliers due to the IN. Compared with conventional approaches and existing end-to-end systems, extensive simulation results verify that our proposed novel end-to-end communication system can achieve better performance in terms of bit-error rate (BER) under MGIN environments.

eess.SP↗

arXiv2022

Parameter Estimation of Mixed Gaussian-Impulsive Noise: An U-net++ Based Method

In many scenarios, the communication system suffers from both Gaussian white noise and non-Gaussian impulsive noise. In order to design optimal signal detection method, it is necessary to estimate the parameters of mixed Gaussian-impulsive noise. Even though this issue can be well tackled with respect to pure mixed noise, it is quite challenging based on the received single-channel signal including both transmitting signal and mixed noise. To mitigate the negative impact of transmitting signal, we propose a parameter estimation method by utilizing a neural network, namely U-net++, to separate the mixed noise from the received single-channel signal. Compared with existing blind source separation based methods, simulation results show that our proposed method can obtain rather better performance in terms of estimation accuracy and robustness under various scenarios.

eess.SP↗

arXiv2013

Voltage-triggered Ultra-fast Metal-insulator Transition in Vanadium Dioxide Switches

Electrically driven metal-insulator transition in vanadium dioxide (VO2) is of interest in emerging memory devices, neural computation, and high speed electronics. We report on the fabrication of out-of-plane VO2 metal-insulator-metal (MIM) structures and reproducible high-speed switching measurements in these two-terminal devices. We have observed a clear correlation between electrically-driven ON/OFF current ratio and thermally-induced resistance change during metal-insulator transition. It is also found that sharp metal-insulator transition could be triggered by external voltage pulses within 2 ns at room temperature and the achieved ON/OFF ratio is greater than two orders of magnitude with good endurance.

cond-mat.mes-hall↗