arXiv subjects

Yan Xin

Publications and source records attributed to Yan Xin.

At least 19 recordsLinked to original sources

arXiv2026

Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis

Root cause analysis (RCA) is a critical task in telecom network operations, but diagnosing performance degradations in modern 5G and emerging 6G networks remains challenging due to complex cross-layer dependencies. While large language models (LLMs) offer promising capabilities for reasoning and knowledge integration, directly applying vanilla LLMs to telecom RCA often leads to hallucination, unstable reasoning, and poor alignment with structured network evidence. This work first reviews the evolution of telecom RCA from rule-based and machine learning (ML) approaches to emerging LLM-enabled techniques, and provides an overview of recent paradigms, including structured reasoning, retrieval-augmented knowledge grounding, agentic orchestration, and verifiable reasoning. Building upon these insights, we propose a structured reasoning framework for LLM-enabled telecom RCA that aligns diagnostic reasoning with telecom-specific evidence and domain knowledge. The proposed approach first organizes heterogeneous network telemetry into canonical contexts, and then enforces decision-path reasoning during diagnosis, and finally generates evidence-grounded explanations for reliable fault identification. Experimental results on two 5G RCA datasets, TeleLogs and TelecomTS, demonstrate that the proposed framework consistently improves diagnostic accuracy and decision consistency compared with baseline techniques. These cross-dataset results highlight the importance of structured reasoning design for practical LLM-based RCA systems in next-generation telecom networks.

cs.AI↗

arXiv2026

ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering

Telecom question answering (QA) is a challenging setting for retrieval-augmented generation (RAG): evidence is fragmented across standards, papers, encyclopedic resources, and web documents, and answers often hinge on technical tables, equations, and specialized protocol language. In low-resource subdomains, generator fine-tuning can over-specialize and degrade general capability, making query-side retriever adaptation an attractive alternative. To this end, we ask whether a fixed-generator, query-adapted RAG system can outperform generator-side adaptation, and which retriever objectives best support that setting. We motivate retrieval, rather than generator fine-tuning, as the adaptation target through a capacity comparison: under bounded-parameter and soft-retrieval assumptions, query-encoder tuning can have a smaller estimation term than supervised fine-tuning when its effective dimension is smaller. We identify two particularly relevant objectives -- the latent-document RAG likelihood, which optimizes generation utility, and the InfoNCE contrastive objective, which improves semantic retrieval geometry -- and leverage them jointly through a retriever optimization method targeting downstream QA performance in the telecom domain. Specifically, we introduce ARMOR, Adaptive Regularized Mixture Optimization for Retrievers, which learns separate temperatures for the RAG retrieval distribution and InfoNCE softmax and regularizes the adapted query encoder toward the frozen base query encoder. Across telecom-specific retrieval and generative QA benchmarks, we show that ARMOR improves evidence retrieval and answer generation in several in-domain settings. Code is available at https://github.com/heshandevaka/ARMOR.git.

cs.IR↗

arXiv2026

Enhancing Large Language Models (LLMs) for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation

Large language models (LLMs) have shown strong potential across a variety of tasks, but their application in the telecom field remains challenging due to domain complexity, evolving standards, and specialized terminology. Therefore, general-domain LLMs may struggle to provide accurate and reliable outputs in this context, leading to increased hallucinations and reduced utility in telecom operations.To address these limitations, this work introduces KG-RAG-a novel framework that integrates knowledge graphs (KGs) with retrieval-augmented generation (RAG) to enhance LLMs for telecom-specific tasks. In particular, the KG provides a structured representation of domain knowledge derived from telecom standards and technical documents, while RAG enables dynamic retrieval of relevant facts to ground the model's outputs. Such a combination improves factual accuracy, reduces hallucination, and ensures compliance with telecom specifications.Experimental results across benchmark datasets demonstrate that KG-RAG outperforms both LLM-only and standard RAG baselines, e.g., KG-RAG achieves an average accuracy improvement of 14.3% over RAG and 21.6% over LLM-only models. These results highlight KG-RAG's effectiveness in producing accurate, reliable, and explainable outputs in complex telecom scenarios.

cs.AI↗

arXiv2025

Observation of REBCO delamination in the resistive insulation nested coils

The REBCO coated conductor has the potential to be widely used in ultrahigh field magnets. It is well known, however, that it is not mechanically strong against delamination in the direction normal to its surface due to its intrinsic layered structure. Therefore, conductor delamination is one of the major design challenges for REBCO magnet coils. As a part of the development of the 40 T all-superconducting magnet at the National High Magnetic Field Laboratory, USA (NHMFL), a dry-wound resistive-insulation-nested-coils (RINC) was designed to reach 25.8 T. It used surface-treated stainless-steel tape as a co-wind to control the turn-to-turn contact resistance, and was fabricated and tested in a liquid helium bath. During the test, two of the double pancake modules exhibited resistive transitions at a current significantly lower than the designed value. The postmortem inspection of the REBCO conductor of these modules by reel-to-reel magnetization at 77 K found sections of very low critical current. Further investigations of one section by chemical etching, visual inspection, and electron microscopy revealed that conductor of this section was delaminated. We present the detailed findings of these postmortem characterizations. The implication of this type of delamination for future magnet designs will be discussed.

cond-mat.supr-con↗

arXiv2025

Sim2Field: End-to-End Development of AI RANs for 6G

Following state-of-the-art research results, which showed the potential for significant performance gains by applying AI/ML techniques in the cellular Radio Access Network (RAN), the wireless industry is now broadly pushing for the adoption of AI in 5G and future 6G technology. Despite this enthusiasm, AI-based wireless systems still remain largely untested in the field. Common simulation methods for generating datasets for AI model training suffer from "reality gap" and, as a result, the performance of these simulation-trained models may not carry over to practical cellular systems. Additionally, the cost and complexity of developing high-performance proof-of-concept implementations present major hurdles for evaluating AI wireless systems in the field. In this work, we introduce a methodology which aims to address the challenges of bringing AI to real networks. We discuss how detailed Digital Twin simulations may be employed for training site-specific AI Physical (PHY) layer functions. We further present a powerful testbed for AI-RAN research and demonstrate how it enables rapid prototyping, field testing and data collection. Finally, we evaluate an AI channel estimation algorithm over-the-air with a commercial UE, demonstrating that real-world throughput gains of up to 40% are achievable by incorporating AI in the physical layer.

cs.NI↗

arXiv2025

Ramp Josephson junctions of Al/Ti/Sr2RuO4: Observation of single-domain quantum oscillations and the detection of chiral edge current

The determination of how the phase of the superconducting order parameter in a superconductor varies with the spatial direction, which can be done only through the Josephson-effect-based phase-sensitive measurements, is crucial for the establishment of the precise pairing symmetry of the superconductor. So far, such measurements have been done on high-Tc cuprate superconductors but only at a couple of directions for Sr2RuO4 because of the difficulty in preparing Josephson junctions between Sr2RuO4 and an s-wave superconductor with a chosen mutual orientation. Another long-standing issue in Sr2RuO4, which was shown previously to feature a spontaneously broken time-reversal symmetry by muon spin rotation and other measurements, is that the expected presence of chiral surface currents, domains, and domain walls is yet to be explicitly shown experimentally. To address these issues, we have long sought the preparation of high-quality Josephson junctions between Sr2RuO4 and a conventional s-wave with a controllable orientation relative to symmetry axes in Sr2RuO4. We report in this article the successful fabrication of ramp Josephson junctions of Al/Ti/Sr2RuO4 on thin single crystals of Sr2RuO4 obtained by mechanical exfoliation. These junctions were found to show high-quality quantum oscillations consistent with a single-domain Josephson coupling. The normal junction resistance was found to depend extremely sensitively on the supercurrent flowing in the Sr2RuO4 crystal on which the Josephson junction was made. This finding was used in the present work to provide an estimate of the size of the chiral surface current, which is shown to agree with its upper bound established previously.

cond-mat.supr-con↗

arXiv2025

MoE-CE: Enhancing Generalization for Deep Learning based Channel Estimation via a Mixture-of-Experts Framework

Reliable channel estimation (CE) is fundamental for robust communication in dynamic wireless environments, where models must generalize across varying conditions such as signal-to-noise ratios (SNRs), the number of resource blocks (RBs), and channel profiles. Traditional deep learning (DL)-based methods struggle to generalize effectively across such diverse settings, particularly under multitask and zero-shot scenarios. In this work, we propose MoE-CE, a flexible mixture-of-experts (MoE) framework designed to enhance the generalization capability of DL-based CE methods. MoE-CE provides an appropriate inductive bias by leveraging multiple expert subnetworks, each specialized in distinct channel characteristics, and a learned router that dynamically selects the most relevant experts per input. This architecture enhances model capacity and adaptability without a proportional rise in computational cost while being agnostic to the choice of the backbone model and the learning algorithm. Through extensive experiments on synthetic datasets generated under diverse SNRs, RB numbers, and channel profiles, including multitask and zero-shot evaluations, we demonstrate that MoE-CE consistently outperforms conventional DL approaches, achieving significant performance gains while maintaining efficiency.

eess.SP↗

arXiv2025

Probing ice-rule-breaking transition in $\rm{Dy_2Ti_2O_7}$ thin film by proximitized transport and magnetic torque

While the spin ice state of bulk pyrochlores such as $\rm{Dy_2Ti_2O_7}$ and $\rm{Ho_2Ti_2O_7}$ has been extensively studied in the last several decades due to its unique degenerate ground state and emergent monopole excitation, whether it survives in the thin-film form remains a mystery. The limited volume of thin-film sample makes it challenging to study the intrinsic magnetic properties. Here, we synthesized 18nm-thick $\rm{Dy_2Ti_2O_7}$ thin film on YSZ (Yttria-stabilized Zirconia with 9.5 mol% $\rm{Y_2O_3}$) substrate and capped it by a thin conductive $\rm{Bi_2Ir_2O_7}$ layer, and performed the proximitized magnetoresistance measurements. Our study found that the ice-rule-breaking phase transition survives but with a modified effective nearest-neighbor interaction ($\rm{J_{eff}}=$ 1.054 K) and distorted Ising spin axes ($\rm{\epsilon}=+0.051)$ compared to the bulk crystal. The results are supported by the simultaneously measured capacitive torque magnetometry. Our study demonstrates that proximitized transport is an effective tool for thin films of insulating frustrated magnets.

cond-mat.mtrl-sci↗

arXiv2025

Disorder driven crossover between anomalous Hall regimes in Fe$_3$GaTe$_2$

The large anomalous Hall conductivity (AHC) of the Fe$_3$(Ge,Ga)Te$_2$ compounds has attracted considerable attention. Here, we expose the intrinsic nature of AHC in Fe$_3$GaTe$_2$ crystals characterized by high conductivities, which show disorder-independent AHC with a pronounced value $\sigma_{xy}^{\text{c}}\approx$ 420 $\Omega^{-1}$cm$^{-1}$. In the low conductivity regime, we observe the scaling relation $\sigma_{xy}\propto\sigma_{xx}^{1.6}$, which crosses over to $\sigma_{xy} \simeq \sigma_{xy}^{\text{c}}$ as $\sigma_{xx}$ increases. Disorder in low-conductivity crystals is confirmed by the broadening of a first-order transition between ferromagnetism and the ferrimagnetic ground state. Through density functional theory (DFT) calculations, we reveal that the dominant sources of Berry curvature are located a few hundred meV below the Fermi energy around the $\Gamma$-point. Therefore, Fe$_3$GaTe$_2$ clearly exposes the disorder-induced crossover among distinct AHC regimes, previously inferred from measurements on different ferromagnets located in either side of the crossover region.

cond-mat.mtrl-sci↗

arXiv2025

Local Inversion Symmetry Breaking and Thermodynamic Evidence for Ferrimagnetism in Fe3GaTe2

The layered compound Fe3GaTe2 is attracting attention due to its high Curie temperature, low dimensionality, and the presence of topological spin textures above room temperature, making Fe$_3$GaTe$_2$ a good candidate for applications in spintronics. Here, we show, through transmission electron microscopy (TEM) techniques, that Fe$_3$GaTe$_2$ single crystals break local inversion symmetry while maintaining global inversion symmetry according to X-ray diffraction. Coupled to the observation of N\'{e}el skyrmions via Lorentz-TEM, our structural analysis provides a convincing explanation for their presence in centrosymmetric materials. Magnetization measurements as a function of the temperature displays a sharp first-order thermodynamic phase-transition leading to a reduction in the magnetic moment. This implies that the ground state of Fe$_3$GaTe$_2$ is globally ferrimagnetic and not a glassy magnetic state composed of ferrimagnetic, and ferromagnetic domains as previously claimed. Neutron diffraction studies indicate that the ferromagnetic to ferrimagnetic transition upon reducing the external magnetic field is associated with a change in the magnetic configuration/coupling between Fe1 and Fe2 moments. We observe a clear correlation between the hysteresis observed in both the skyrmion density and the magnetization of Fe$_3$GaTe$_2$. This indicates that its topological spin textures are affected by the development of ferrimagnetism upon cooling. Observation, via magnetic force microscopy, of magnetic bubbles at the magnetic phase boundary suggests skyrmions stabilized by the competition among magnetic phases and distinct exchange interactions. Our study provides an explanation for the observation of N\'eel skyrmions in centrosymmetric systems, while exposing a correlation between the distinct magnetic phases of Fe$_3$GaTe$_2$ and topological spin textures.

cond-mat.mtrl-sci↗

arXiv2025

Hierarchical Debate-Based Large Language Model (LLM) for Complex Task Planning of 6G Network Management

6G networks have become increasingly complicated due to novel network architecture and newly emerging signal processing and transmission techniques, leading to significant burdens to 6G network management. Large language models (LLMs) have recently been considered a promising technique to equip 6G networks with AI-native intelligence. Different from most existing studies that only consider a single LLM, this work involves a multi-LLM debate-based scheme for 6G network management, where multiple LLMs can collaboratively improve the initial solution sequentially. Considering the complex nature of 6G domain, we propose a novel hierarchical debate scheme: LLMs will first debate the sub-task decomposition, and then debate each subtask step-by-step. Such a hierarchical approach can significantly reduce the overall debate difficulty by sub-task decomposition, aligning well with the complex nature of 6G networks and ensuring the final solution qualities. In addition, to better evaluate the proposed technique, we have defined a novel dataset named 6GPlan, including 110 complex 6G network management tasks and 5000 keyword solutions. Finally, the experiments show that the proposed hierarchical debate can significantly improve performance compared to baseline techniques, e.g. more than 30% coverage rate and global recall rate improvement.

eess.SY↗

arXiv2025

Understanding 6G through Language Models: A Case Study on LLM-aided Structured Entity Extraction in Telecom Domain

Knowledge understanding is a foundational part of envisioned 6G networks to advance network intelligence and AI-native network architectures. In this paradigm, information extraction plays a pivotal role in transforming fragmented telecom knowledge into well-structured formats, empowering diverse AI models to better understand network terminologies. This work proposes a novel language model-based information extraction technique, aiming to extract structured entities from the telecom context. The proposed telecom structured entity extraction (TeleSEE) technique applies a token-efficient representation method to predict entity types and attribute keys, aiming to save the number of output tokens and improve prediction accuracy. Meanwhile, TeleSEE involves a hierarchical parallel decoding method, improving the standard encoder-decoder architecture by integrating additional prompting and decoding strategies into entity extraction tasks. In addition, to better evaluate the performance of the proposed technique in the telecom domain, we further designed a dataset named 6GTech, including 2390 sentences and 23747 words from more than 100 6G-related technical publications. Finally, the experiment shows that the proposed TeleSEE method achieves higher accuracy than other baseline techniques, and also presents 5 to 9 times higher sample processing speed.

cs.CL↗

arXiv2025

Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation

Large language models (LLMs) have made significant progress in general-purpose natural language processing tasks. However, LLMs are still facing challenges when applied to domain-specific areas like telecommunications, which demands specialized expertise and adaptability to evolving standards. This paper presents a novel framework that combines knowledge graph (KG) and retrieval-augmented generation (RAG) techniques to enhance LLM performance in the telecom domain. The framework leverages a KG to capture structured, domain-specific information about network protocols, standards, and other telecom-related entities, comprehensively representing their relationships. By integrating KG with RAG, LLMs can dynamically access and utilize the most relevant and up-to-date knowledge during response generation. This hybrid approach bridges the gap between structured knowledge representation and the generative capabilities of LLMs, significantly enhancing accuracy, adaptability, and domain-specific comprehension. Our results demonstrate the effectiveness of the KG-RAG framework in addressing complex technical queries with precision. The proposed KG-RAG model attained an accuracy of 88% for question answering tasks on a frequently used telecom-specific dataset, compared to 82% for the RAG-only and 48% for the LLM-only approaches.

cs.CL↗

arXiv2024

Study of delamination in REBCO coated conductor by transmission electron microscopy

Delamination strength of REBCO is very important for its applications in large magnet projects. This work presented the transmission electron microscopy (TEM) investigation of the microstructures of the REBCO coated conductor to understand its delamination property. We found that the low delamination strength is associated with nano-voids formed at the IBAD MgO/Y2O3 interface.

cond-mat.supr-con↗

arXiv2024

Super-Bandgap Electroluminescence from Cesium Lead Bromide

Halide perovskites is a new class of semiconductors with exceptional optoelectronic properties. Among many advantages offered by halide perovskites, the bandgap energy can be tuned in a much broader range than what was possible in conventional semiconductors. This was commonly achieved in previous research by mixing different species of halides into solid solutions. The tuned bandgap using this method, however, often underwent an energy shift under optical or electrical stimuli due to halide segregation. In this work, we discovered an alternative approach to achieve super-bandgap electroluminescence from CsPbBr3. The peak energy of the light emission can be 0.7 eV higher than the reported bandgap energy. Evidence pointed to the radiative recombination at the perovskite-PEDOT:PSS interface being responsible for the unexpected blueshift of electroluminescence. We speculated that perovskite nanocrystals were formed therein and produced higher-energy photons due to quantum confinement. The results suggested an alternative strategy to manipulate and stabilize the color of electroluminescence and achieve particularly blue emission in perovskite-based LEDs.

physics.app-ph↗

arXiv2024

Thermal conductivity of REBCO tapes with different stabilizers from 4.2 to 200 K

REBCO coated conductor is a high temperature superconductor that has a wide range of applications, one of which is the current leads of magnet systems. In the design of current leads, it is crucial to minimize their thermal conduction while maintain stable electrical conduction. Therefore, thermal conductivity of various REBCO tapes need to be characterized and analyzed. In this research, we measured thermal conductivity of REBCO tapes in the longitudinal direction from 4.2 to 200 K. Samples with Cu, Ag and Ag-3at%Au stabilizers of various thicknesses were measured. The electrical conductivity of these stabilizers was also characterized by residual resistance ratio (RRR) measurements and correlated with the thermal conductivity results. We showed that in samples with 10 micron or less Cu stabilizer, thermal conduction is dominated by that of the Cu which has much higher thermal conductivity than the Hastelloy substrate and the superconductor layer. In addition, the sample with 3 micron Ag-3at%Au stabilizer has significantly lower thermal conductivity than that with 3 micron silver stabilizer. It is concluded that REBCO with Ag-3at%Au stabilizer is promising for current lead applications.

cond-mat.supr-con↗

arXiv2024

Residual resistance ratio of Cu stabilizer in commercial REBCO tapes

Residual resistance ratio (RRR) of Cu stabilizer in REBCO coated conductor is an important design parameter for REBCO magnets. In this work, we measured RRR of electroplated Cu stabilizer in commercial REBCO tapes. Over 130 samples were measured for the quality assurance programs of REBCO magnet projects at the National High Magnetic Field Laboratory, USA (NHMFL). The average RRR value was above 50. In order to investigate the factors that influence RRR, several samples were analyzed by using scanning electron microscopy, secondary ion mass spectroscopy, and inductively coupled plasma mass spectroscopy. We found that, in our samples, RRR was strongly correlated with the grain size. We demonstrated that RRR was primarily determined by grain boundary resistivity. Lower RRR was also strongly correlated with higher concentration of chlorine impurity. This is explained by that higher chlorine impurity hindered the grain growth in the room temperature self annealing process resulting smaller grain. Smaller grain resulted in lower RRR. In addition, thermal annealing significantly enhanced RRR. An activation energy of 0.4 eV was obtained from the annealing experiment which corresponds to the activation of Cu grain growth.

cond-mat.supr-con↗

arXiv2024

Plug-in UL-CSI-Assisted Precoder Upsampling Approach in Cellular FDD Systems

Acquiring downlink channel state information (CSI) is crucial for optimizing performance in massive Multiple Input Multiple Output (MIMO) systems operating under Frequency-Division Duplexing (FDD). Most cellular wireless communication systems employ codebook-based precoder designs, which offer advantages such as simpler, more efficient feedback mechanisms and reduced feedback overhead. Common codebook-based approaches include Type II and eType II precoding methods defined in the 3GPP standards. Feedback in these systems is typically standardized per subband (SB), allowing user equipment (UE) to select the optimal precoder from the codebook for each SB, thereby reducing feedback overhead. However, this subband-level feedback resolution may not suffice for frequency-selective channels. This paper addresses this issue by introducing an uplink CSI-assisted precoder upsampling module deployed at the gNodeB. This module upsamples SB-level precoders to resource block (RB)-level precoders, acting as a plug-in compatible with existing gNodeB or base stations.

eess.SP↗