arXiv subjects

Yalin Liu

Publications and source records attributed to Yalin Liu.

At least 19 recordsLinked to original sources

arXiv2026

Sensing-Assisted Anti-Blockage Pinching-Antenna Systems For Indoor Immersive Communications

Indoor immersive communication, empowered by millimeter-wave (mmWave) technologies, is a key enabler for 6G-ready Extended Reality (XR) applications. However, mmWave signals are highly susceptible to dynamic obstacles (e.g., moving humans), leading to frequent line-of-sight (LoS) blockages. Moreover, although massive-antenna systems have been widely adopted indoors, their fixed-site deployment limits their effectiveness in wide and dynamic indoor environments such as factories and offices. To address these challenges, we present a sensing-assisted anti-blockage Pinching-Antenna Systems (PASS) solution for indoor immersive communications. By mechanically moving a pinching antenna (PA) along a dielectric waveguide, PASS can flexibly extend the service coverage and re-establish LoS links in wide indoor scenarios. Besides, our solution integrates sensing capabilities into PASS, where the PA executes controlled micro-movements to transmit and receive chirp radar signals, enabling real-time sensing of obstacles. Based on the sensing results, we design an anti-blockage PA movement strategy that detects blocked regions along the waveguide and proactively moves the PA to safe zones. Accordingly, the whole system performs a cycled "sensing-movement-communication" workflow with each cycle completed in a limited frame to ensure both timely sensing and communication. To ensure system efficiency, a closed-form pre-optimized solution of sensing parameters is derived under practical mechanical and sensing constraints. Extensive experiments show that our solution reduces blockage-induced outages to near-zero while sustaining the effective throughput required by indoor immersive applications.

eess.SP↗

arXiv2026

A Preliminary Study on Explaining Risk of Code Changes using LLM-Based Prediction Models

Predictions by machine learning (ML) and artificial intelligence (AI) models are often received skeptically unless they are paired with intelligible explanations. In the context of just-in-time defect prediction, highlighting small portions of a software change (diff) -- beyond rule-based lints -- where risk may be concentrated has not yet been extensively investigated. In this work, we leverage attention weights from an LLM-based Diff Risk Score (DRS) model to highlight parts of a diff that the model focuses on when predicting risk. We aggregate token-level attention into interpretable code units (lines, hunks, and files), and present the top-K units to developers as a lightweight form of guidance during code review. We evaluate our approach using expert-labeled changes that have caused real outages. Results show that the highlighted snippets cover expert-labeled outage-causing change lines 53.85% of the time when highlighting the top-2 hunks, while requiring developers to review 26.28% of the changed lines on average. Because attention is produced during standard model inference, the approach is scalable for large development workflows and can be surfaced in the code review UI with low additional latency.

cs.SE↗

arXiv2026

Augmented Set-membership Affine Projection Algorithm and Its Performance Analysis

The augmented affine projection algorithm (AAPA) has considerably excellent performance for highly colored input signals. However, the direct matrix inversion operation leads to a high computational complexity, especially with high projection order. Inspired by the excellent characteristics of set-membership filtering (SMF), this paper proposes the augmented set-membership affine projection algorithm (ASM-APA), which not only has low computational complexity but also offers improved performance compared with AAPA. Then, the computational complexity and stability of ASM-APA are analyzed, and the condition for maintaining the stability of the algorithm is provided. Finally, in the computer simulation phase, the results of the simulation experiments demonstrated that ASM-APA has superior performance compared to AAPA.

eess.SP↗

arXiv2026

Sky-Ear: An Unmanned Aerial Vehicle-Enabled Victim Sound Detection and Localization System

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in search-and-rescue (SAR) missions, yet continuous and reliable victim detection and localization remain challenging due to on-board hardware constraints. This paper designs an UAV-Enabled Victim Sound Detection and Localization System (called ``Sky-Ear'' for brevity) to achieve energy-efficient acoustic sensing and sound detection for SAR. Sky-Ear enables the ``ear'' of the UAV with a circular-shaped microphone array, and the array conducts continuous audio recordings during the UAV's flight. In Sky-Ear, a two-stage (Sentinel and Responder) audio processing method is developed for energy-consuming and highly reliable sound detection. In the Sentinel stage, a Masking autoencoder (MAE)-based sound detection mechanism is designed to analyze frequency-time acoustic features. For improved precision, a continuous localization method is designed by optimizing detected directions from multiple observations. Extensive simulation experiments are conducted to validate the system's performance in terms of victim detection accuracy and localization error.

eess.AS↗

arXiv2026

FedBiCross: Personalized One-Shot Federated Learning on Medical Images

Data-free knowledge distillation-based one-shot federated learning (OSFL) trains a model in a single communication round without sharing raw data, making OSFL attractive for privacy-sensitive medical applications. However, existing methods aggregate predictions from all clients to form a global teacher. Under non-IID data, conflicting predictions dilute each other during averaging, yielding less informative soft labels that weaken distillation. We propose FedBiCross, a personalized OSFL framework with three stages: (1) clustering clients by model output similarity to form coherent sub-ensembles, (2) bi-level cross-cluster optimization that learns adaptive weights to selectively leverage beneficial cross-cluster knowledge while suppressing negative transfer, and (3) personalized distillation for client-specific adaptation. Experiments on four medical image datasets demonstrate that FedBiCross consistently outperforms state-of-the-art baselines across different non-IID degrees.

cs.LG↗

arXiv2025

Multi-Waveguide Pinching Antenna Placement Optimization for Rate Maximization

Pinching antenna systems (PASS) have emerged as a technology that enables the large-scale movement of antenna elements, offering significant potential for performance gains in next-generation wireless networks. This paper investigates the problem of maximizing the average per-user data rate by optimizing the antenna placement of a multi-waveguide PASS, subject to a stringent physical minimum spacing constraint. To address this complex challenge, which involves a coupled fractional objective and a non-convex constraint, we employ the fractional programming (FP) framework to transform the non-convex rate maximization problem into a more tractable one, and devise a projected gradient ascent (PGA)-based algorithm to iteratively solve the transformed problem. Simulation results demonstrate that our proposed scheme significantly outperforms various geometric placement baselines, achieving superior per-user data rates by actively mitigating multi-user interference.

eess.SP↗

arXiv2025

Content-Aware RSMA-Enabled Pinching-Antenna Systems for Latency Optimization in 6G Networks

The Pinching Antenna System (PAS) has emerged as a promising technology to dynamically reconfigure wireless propagation environments in 6G networks. By activating radiating elements at arbitrary positions along a dielectric waveguide, PAS can establish strong line-of-sight (LoS) links with users, significantly enhancing channel gain and deployment flexibility, particularly in high-frequency bands susceptible to severe path loss. To further improve multi-user performance, this paper introduces a novel content-aware transmission framework that integrates PAS with rate-splitting multiple access (RSMA). Unlike conventional RSMA, the proposed RSMA scheme enables users requesting the same content to share a unified private stream, thereby mitigating inter-user interference and reducing power fragmentation. We formulate a joint optimization problem aimed at minimizing the average system latency by dynamically adapting both antenna positioning and RSMA parameters according to channel conditions and user requests. A Content-Aware RSMA and Pinching-antenna Joint Optimization (CARP-JO) algorithm is developed, which decomposes the non-convex problem into tractable subproblems solved via bisection search, convex programming, and golden-section search. Simulation results demonstrate that the proposed CARP-JO scheme consistently outperforms Traditional RSMA, NOMA, and Fixed-antenna systems across diverse network scenarios in terms of latency, underscoring the effectiveness of co-designing physical-layer reconfigurability with intelligent communication strategies.

eess.SP↗

arXiv2025

Uplink Rate Maximization for Pinching Antenna- Assisted Covert Backscatter Communication

The emerging pinching antenna (PA) technology enables flexible antenna positioning for creating line-of-sight (LoS) links, thus offering substantial potential to facilitate ambient signal-based backscatter communication (BSC). This paper investigates PA-assisted BSC for enhanced communication and covertness in the presence of a randomly distributed eavesdropper. An optimization problem is formulated to maximize the uplink covert transmission rate by jointly optimizing the transmit power and antenna positions while satisfying both communication reliability and covertness constraints. An alternative optimization (AO)-based framework is proposed to solve this problem. Numerical results demonstrate that the proposed PA-BSC effectively mitigates the double near-far problem, where energy harvesting and backscatter transmission degrade simultaneously due to distance disparities, thereby improving downlink energy harvesting and uplink data transmission while maintaining covertness performance under practical deployment scenarios.

eess.SP↗

arXiv2025

A Confidence-Constrained Cloud-Edge Collaborative Framework for Autism Spectrum Disorder Diagnosis

Autism Spectrum Disorder (ASD) diagnosis systems in school environments increasingly relies on IoT-enabled cameras, yet pure cloud processing raises privacy and latency concerns while pure edge inference suffers from limited accuracy. We propose Confidence-Constrained Cloud-Edge Knowledge Distillation (C3EKD), a hierarchical framework that performs most inference at the edge and selectively uploads only low-confidence samples to the cloud. The cloud produces temperature-scaled soft labels and distils them back to edge models via a global loss aggregated across participating schools, improving generalization without centralizing raw data. On two public ASD facial-image datasets, the proposed framework achieves a superior accuracy of 87.4\%, demonstrating its potential for scalable deployment in real-world applications.

cs.NI↗

arXiv2025

Cloud-Fog-Edge Collaborative Computing for Sequential MIoT Workflow: A Two-Tier DDPG-Based Scheduling Framework

The Medical Internet of Things (MIoT) demands stringent end-to-end latency guarantees for sequential healthcare workflows deployed over heterogeneous cloud-fog-edge infrastructures. Scheduling these sequential workflows to minimize makespan is an NP-hard problem. To tackle this challenge, we propose a Two-tier DDPG-based scheduling framework that decomposes the scheduling decision into a hierarchical process: a global controller performs layer selection (edge, fog, or cloud), while specialized local controllers handle node assignment within the chosen layer. The primary optimization objective is the minimization of the workflow makespan. Experiments results validate our approach, demonstrating increasingly superior performance over baselines as workflow complexity rises. This trend highlights the frameworks ability to learn effective long-term strategies, which is critical for complex, large-scale MIoT scheduling scenarios.

cs.LG↗

arXiv2025

Hybrid Satellite-Ground Deployments for Web3 DID: System Design and Performance Analysis

The emerging Web3 has great potential to provide worldwide decentralized services powered by global-range data-driven networks in the future. To ensure the security of Web3 services among diverse user entities, a decentralized identity (DID) system is essential. Especially, a user's access request to Web3 services can be treated as a DID transaction within the blockchain, executed through a consensus mechanism. However, a critical implementation issue arises in the current Web3, i.e., how to deploy network nodes to serve users on a global scale. To address this issue, emerging Low Earth Orbit (LEO) satellite communication systems, such as Starlink, offer a promising solution. With their global coverage and high reliability, these communication satellites can complement terrestrial networks as Web3 deployment infrastructures. In this case, this paper develops three hybrid satellite-ground modes to deploy the blockchain-enabled DID system for Web3 users. Three modes integrate ground nodes and satellites to provide flexible and continuous DID services for worldwide users. Meanwhile, to evaluate the effectiveness of the present hybrid deployment modes, we analyze the complete DID consensus performance of blockchain on three hybrid satellite-ground modes. Moreover, we conduct numerical and simulation experiments to verify the effectiveness of three hybrid satellite-ground modes. The impacts of various system parameters are thoroughly analyzed, providing valuable insights for implementing the worldwide Web3 DID system in real-world network environments.

eess.SY↗

arXiv2025

Pinching-Antenna Systems For Indoor Immersive Communications: A 3D-Modeling Based Performance Analysis

The emerging pinching antenna (PA) technology has high flexibility to reconfigure wireless channels and combat line-of-sight blockage, thus holding transformative potential for indoor immersive applications in 6G. This paper investigates Pinching-antenna systems (PASS) for indoor immersive communications. Our contributions are threefold: (1) we construct a 3D model to characterize the distribution of users, waveguides, and PAs in the PASS; (2) we develop a general theoretical model on downlink performance of PASS by capturing PA-user relationships and system parameters' impacts; and (3) we conduct comprehensive numerical results of the theoretical model and provide implementation guidelines for PASS deployments.

cs.PF↗

arXiv2025

Unified Network Modeling for Six Cross-Layer Scenarios in Space-Air-Ground Integrated Networks

The space-air-ground integrated network (SAGIN) can enable global range and seamless coverage in the future network. SAGINs consist of three spatial layer network nodes: 1) satellites on the space layer, 2) aerial vehicles on the aerial layer, and 3) ground devices on the ground layer. Data transmissions in SAGINs include six unique cross-spatial-layer scenarios, i.e., three uplink and three downlink transmissions across three spatial layers. For simplicity, we call them \textit{six cross-layer scenarios}. Considering the diverse cross-layer scenarios, it is crucial to conduct a unified network modeling regarding node coverage and distributions in all scenarios. To achieve this goal, we develop a unified modeling approach of coverage regions for all six cross-layer scenarios. Given a receiver in each scenario, its coverage region on a transmitter-distributed surface is modeled as a spherical dome. Utilizing spherical geometry, the analytical models of the spherical-dome coverage regions are derived and unified for six cross-layer scenarios. We conduct extensive numerical results to examine the coverage models under varying carrier frequencies, receiver elevation angles, and transceivers' altitudes. Based on the coverage model, we develop an algorithm to generate node distributions under spherical coverage regions, which can assist in testing SAGINs before practical implementations.

eess.SY↗

arXiv2025

Enhancing Mobile Crowdsensing Efficiency: A Coverage-aware Resource Allocation Approach

In this study, we investigate the resource management challenges in next-generation mobile crowdsensing networks with the goal of minimizing task completion latency while ensuring coverage performance, i.e., an essential metric to ensure comprehensive data collection across the monitored area, yet it has been commonly overlooked in existing studies. To this end, we formulate a weighted latency and coverage gap minimization problem via jointly optimizing user selection, subchannel allocation, and sensing task allocation. The formulated minimization problem is a non-convex mixed-integer programming issue. To facilitate the analysis, we decompose the original optimization problem into two subproblems. One focuses on optimizing sensing task and subband allocation under fixed sensing user selection, which is optimally solved by the Hungarian algorithm via problem reformulation. Building upon these findings, we introduce a time-efficient two-sided swapping method to refine the scheduled user set and enhance system performance. Extensive numerical results demonstrate the effectiveness of our proposed approach compared to various benchmark strategies.

cs.NI↗

arXiv2025

3D Stochastic Geometry Model for Aerial Vehicle-Relayed Ground-Air-Satellite Connectivity

Due to their flexibility, aerial vehicles (AVs), such as unmanned aerial vehicles and airships, are widely employed as relays to assist communications between massive ground users (GUs) and satellites, forming an AV-relayed ground-air-satellite solution (GASS). In GASS, the deployment of AVs is crucial to ensure overall performance from GUs to satellites. This paper develops a stochastic geometry-based analytical model for GASS under Matern hard-core point process (MHCPP) distributed AVs. The 3D distributions of AVs and GUs are modeled by considering their locations on spherical surfaces in the presence of high-altitude satellites. Accordingly, we derive an overall connectivity analytical model for GASS, which includes the average performance of AV-relayed two-hop transmissions. Extensive numerical results validate the accuracy of the connectivity model and provide essential insights for configuring AV deployments.

math.NA↗

arXiv2022

Generating and Visualizing Trace Link Explanations

Recent breakthroughs in deep-learning (DL) approaches have resulted in the dynamic generation of trace links that are far more accurate than was previously possible. However, DL-generated links lack clear explanations, and therefore non-experts in the domain can find it difficult to understand the underlying semantics of the link, making it hard for them to evaluate the link's correctness or suitability for a specific software engineering task. In this paper we present a novel NLP pipeline for generating and visualizing trace link explanations. Our approach identifies domain-specific concepts, retrieves a corpus of concept-related sentences, mines concept definitions and usage examples, and identifies relations between cross-artifact concepts in order to explain the links. It applies a post-processing step to prioritize the most likely acronyms and definitions and to eliminate non-relevant ones. We evaluate our approach using project artifacts from three different domains of interstellar telescopes, positive train control, and electronic health-care systems, and then report coverage, correctness, and potential utility of the generated definitions. We design and utilize an explanation interface which leverages concept definitions and relations to visualize and explain trace link rationales, and we report results from a user study that was conducted to evaluate the effectiveness of the explanation interface. Results show that the explanations presented in the interface helped non-experts to understand the underlying semantics of a trace link and improved their ability to vet the correctness of the link.

cs.SE↗

arXiv2021

Traceability Transformed: Generating more Accurate Links with Pre-Trained BERT Models

Software traceability establishes and leverages associations between diverse development artifacts. Researchers have proposed the use of deep learning trace models to link natural language artifacts, such as requirements and issue descriptions, to source code; however, their effectiveness has been restricted by availability of labeled data and efficiency at runtime. In this study, we propose a novel framework called Trace BERT (T-BERT) to generate trace links between source code and natural language artifacts. To address data sparsity, we leverage a three-step training strategy to enable trace models to transfer knowledge from a closely related Software Engineering challenge, which has a rich dataset, to produce trace links with much higher accuracy than has previously been achieved. We then apply the T-BERT framework to recover links between issues and commits in Open Source Projects. We comparatively evaluated accuracy and efficiency of three BERT architectures. Results show that a Single-BERT architecture generated the most accurate links, while a Siamese-BERT architecture produced comparable results with significantly less execution time. Furthermore, by learning and transferring knowledge, all three models in the framework outperform classical IR trace models. On the three evaluated real-word OSS projects, the best T-BERT stably outperformed the VSM model with average improvements of 60.31% measured using Mean Average Precision (MAP). RNN severely underperformed on these projects due to insufficient training data, while T-BERT overcame this problem by using pretrained language models and transfer learning.

cs.SE↗

arXiv2020

Wireless Powering Internet of Things with UAVs: Challenges and Opportunities

Unmanned aerial vehicles (UAVs) have the potential to overcome the deployment constraint of Internet of Things (IoT) in remote or rural area. Wirelessly powered communications (WPC) can address the battery limitation of IoT devices through transferring wireless power to IoT devices. The integration of UAVs and WPC, namely UAV-enabled Wireless Powering IoT (Ue-WPIoT) can greatly extend the IoT applications from cities to remote or rural areas. In this article, we present a state-of-the-art overview of Ue-WPIoT by first illustrating the working flow of Ue-WPIoT and discussing the challenges. We then introduce the enabling technologies in realizing Ue-WPIoT. Simulation results validate the effectiveness of the enabling technologies in Ue-WPIoT. We finally outline the future directions and open issues.

cs.NI↗