pwc-archive/papers-with-abstracts huggingface dataset:
Transferable Foundation Models for Geometric Tasks on Point Cloud Representations: Geometric Neural Operators
Authors: Blaine Quackenbush, Paul J. Atzberger
Date: 2025-03-06 00:00:00

We introduce methods for obtaining pretrained Geometric Neural Operators (GNPs) that can serve as basal foundation models for use in obtaining geometric features. These can be used within data processing pipelines for machine learning tasks and numerical methods. We show how our GNPs can be trained to learn robust latent representations for the differential geometry of point-clouds to provide estimates of metric, curvature, and other shape-related features. We demonstrate how our pre-trained GNPs can be used (i) to estimate the geometric properties of surfaces of arbitrary shape and topologies with robustness in the presence of noise, (ii) to approximate solutions of geometric partial differential equations (PDEs) on manifolds, and (iii) to solve equations for shape deformations such as curvature driven flows. We release codes and weights for using GNPs in the package geo_neural_op. This allows for incorporating our pre-trained GNPs as components for reuse within existing and new data processing pipelines. The GNPs also can be used as part of numerical solvers involving geometry or as part of methods for performing inference and other geometric tasks.
Damping Identification Sensitivity in Flutter Speed Estimation
Authors: Gabriele Dessena, Alessandro Pontillo, Marco Civera, Dmitry I. Ignatyev, James F. Whidborne, Luca Zanotti Fragonara
Date: 2025-03-06 00:00:00
Tasks: Sensitivity

Predicting flutter remains a key challenge in aeroelastic research, with certain models relying on modal parameters, such as natural frequencies and damping ratios. These models are particularly useful in early design stages or for the development of small Unmanned Aerial Vehicles (maximum take-off mass below 7 kg). This study evaluates two frequency-domain system identification methods, Fast Relaxed Vector Fitting (FRVF) and the Loewner Framework (LF), for predicting the flutter onset speed of a flexible wing model. Both methods are applied to extract modal parameters from Ground Vibration Testing data, which are subsequently used to develop a reduced-order model with two degrees of freedom. Results indicate that FRVF and LF-informed models provide reliable flutter speed, with predictions deviating by no more than 3% (FRVF) and 5% (LF) from the N4SID-informed benchmark. The findings highlight the sensitivity of flutter speed predictions to damping ratio identification accuracy and demonstrate the potential of these methods as computationally efficient alternatives for preliminary aeroelastic assessments.
Determinant Estimation under Memory Constraints and Neural Scaling Laws
Authors: Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Fred Roosta, Michael W. Mahoney
Date: 2025-03-06 00:00:00

Calculating or accurately estimating log-determinants of large positive semi-definite matrices is of fundamental importance in many machine learning tasks. While its cubic computational complexity can already be prohibitive, in modern applications, even storing the matrices themselves can pose a memory bottleneck. To address this, we derive a novel hierarchical algorithm based on block-wise computation of the LDL decomposition for large-scale log-determinant calculation in memory-constrained settings. In extreme cases where matrices are highly ill-conditioned, accurately computing the full matrix itself may be infeasible. This is particularly relevant when considering kernel matrices at scale, including the empirical Neural Tangent Kernel (NTK) of neural networks trained on large datasets. Under the assumption of neural scaling laws in the test error, we show that the ratio of pseudo-determinants satisfies a power-law relationship, allowing us to derive corresponding scaling laws. This enables accurate estimation of NTK log-determinants from a tiny fraction of the full dataset; in our experiments, this results in a $\sim$100,000$\times$ speedup with improved accuracy over competing approximations. Using these techniques, we successfully estimate log-determinants for dense matrices of extreme sizes, which were previously deemed intractable and inaccessible due to their enormous scale and computational demands.
An optimal Petrov-Galerkin framework for operator networks
Authors: Philip Charles, Deep Ray, Yue Yu, Joost Prins, Hugo Melchers, Michael R. A. Abdelmalik, Jeffrey Cochran, Assad A. Oberai, Thomas J. R. Hughes, Mats G. Larson
Date: 2025-03-06 00:00:00

The optimal Petrov-Galerkin formulation to solve partial differential equations (PDEs) recovers the best approximation in a specified finite-dimensional (trial) space with respect to a suitable norm. However, the recovery of this optimal solution is contingent on being able to construct the optimal weighting functions associated with the trial basis. While explicit constructions are available for simple one- and two-dimensional problems, such constructions for a general multidimensional problem remain elusive. In the present work, we revisit the optimal Petrov-Galerkin formulation through the lens of deep learning. We propose an operator network framework called Petrov-Galerkin Variationally Mimetic Operator Network (PG-VarMiON), which emulates the optimal Petrov-Galerkin weak form of the underlying PDE. The PG-VarMiON is trained in a supervised manner using a labeled dataset comprising the PDE data and the corresponding PDE solution, with the training loss depending on the choice of the optimal norm. The special architecture of the PG-VarMiON allows it to implicitly learn the optimal weighting functions, thus endowing the proposed operator network with the ability to generalize well beyond the training set. We derive approximation error estimates for PG-VarMiON, highlighting the contributions of various error sources, particularly the error in learning the true weighting functions. Several numerical results are presented for the advection-diffusion equation to demonstrate the efficacy of the proposed method. By embedding the Petrov-Galerkin structure into the network architecture, PG-VarMiON exhibits greater robustness and improved generalization compared to other popular deep operator frameworks, particularly when the training data is limited.
Federated Learning in NTNs: Design, Architecture and Challenges
Authors: Amin Farajzadeh, Animesh Yadav, Halim Yanikomeroglu
Date: 2025-03-10 00:00:00
Tasks: Federated Learning, Management

Non-terrestrial networks (NTNs) are emerging as a core component of future 6G communication systems, providing global connectivity and supporting data-intensive applications. In this paper, we propose a distributed hierarchical federated learning (HFL) framework within the NTN architecture, leveraging a high altitude platform station (HAPS) constellation as intermediate distributed FL servers. Our framework integrates both low-Earth orbit (LEO) satellites and ground clients in the FL training process while utilizing geostationary orbit (GEO) and medium-Earth orbit (MEO) satellites as relays to exchange FL global models across other HAPS constellations worldwide, enabling seamless, global-scale learning. The proposed framework offers several key benefits: (i) enhanced privacy through the decentralization of the FL mechanism by leveraging the HAPS constellation, (ii) improved model accuracy and reduced training loss while balancing latency, (iii) increased scalability of FL systems through ubiquitous connectivity by utilizing MEO and GEO satellites, and (iv) the ability to use FL data, such as resource utilization metrics, to further optimize the NTN architecture from a network management perspective. A numerical study demonstrates the proposed framework's effectiveness, with improved model accuracy, reduced training loss, and efficient latency management. The article also includes a brief review of FL in NTNs and highlights key challenges and future research directions.
Task-Oriented Connectivity for Networked Robotics with Generative AI and Semantic Communications
Authors: Peizheng Li, Adnan Aijaz
Date: 2025-03-09 00:00:00
Tasks: Anomaly Detection, Semantic Communication

The convergence of robotics, advanced communication networks, and artificial intelligence (AI) holds the promise of transforming industries through fully automated and intelligent operations. In this work, we introduce a novel co-working framework for robots that unifies goal-oriented semantic communication (SemCom) with a Generative AI (GenAI)-agent under a semantic-aware network. SemCom prioritizes the exchange of meaningful information among robots and the network, thereby reducing overhead and latency. Meanwhile, the GenAI-agent leverages generative AI models to interpret high-level task instructions, allocate resources, and adapt to dynamic changes in both network and robotic environments. This agent-driven paradigm ushers in a new level of autonomy and intelligence, enabling complex tasks of networked robots to be conducted with minimal human intervention. We validate our approach through a multi-robot anomaly detection use-case simulation, where robots detect, compress, and transmit relevant information for classification. Simulation results confirm that SemCom significantly reduces data traffic while preserving critical semantic details, and the GenAI-agent ensures task coordination and network adaptation. This synergy provides a robust, efficient, and scalable solution for modern industrial environments.
Intelligent Spectrum Sharing in Integrated TN-NTNs: A Hierarchical Deep Reinforcement Learning Approach
Authors: Muhammad Umer, Muhammad Ahmed Mohsin, Ali Arshad Nasir, Hatem Abou-zeid, Syed Ali Hassan
Date: 2025-03-09 00:00:00
Tasks: Deep Reinforcement Learning, Management

Integrating non-terrestrial networks (NTNs) with terrestrial networks (TNs) is key to enhancing coverage, capacity, and reliability in future wireless communications. However, the multi-tier, heterogeneous architecture of these integrated TN-NTNs introduces complex challenges in spectrum sharing and interference management. Conventional optimization approaches struggle to handle the high-dimensional decision space and dynamic nature of these networks. This paper proposes a novel hierarchical deep reinforcement learning (HDRL) framework to address these challenges and enable intelligent spectrum sharing. The proposed framework leverages the inherent hierarchy of the network, with separate policies for each tier, to learn and optimize spectrum allocation decisions at different timescales and levels of abstraction. By decomposing the complex spectrum sharing problem into manageable sub-tasks and allowing for efficient coordination among the tiers, the HDRL approach offers a scalable and adaptive solution for spectrum management in future TN-NTNs. Simulation results demonstrate the superior performance of the proposed framework compared to traditional approaches, highlighting its potential to enhance spectral efficiency and network capacity in dynamic, multi-tier environments.
Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security
Authors: Zifan Zhang, Minghong Fang, Dianwei Chen, Xianfeng Yang, Yuchen Liu
Date: 2025-03-08 00:00:00
Tasks: Federated Learning, Reinforcement Learning (RL)

Digital network twins (DNTs) are virtual representations of physical networks, designed to enable real-time monitoring, simulation, and optimization of network performance. When integrated with machine learning (ML) techniques, particularly federated learning (FL) and reinforcement learning (RL), DNTs emerge as powerful solutions for managing the complexities of network operations. This article presents a comprehensive analysis of the synergy of DNTs, FL, and RL techniques, showcasing their collective potential to address critical challenges in 6G networks. We highlight key technical challenges that need to be addressed, such as ensuring network reliability, achieving joint data-scenario forecasting, and maintaining security in high-risk environments. Additionally, we propose several pipelines that integrate DNT and ML within coherent frameworks to enhance network optimization and security. Case studies demonstrate the practical applications of our proposed pipelines in edge caching and vehicular networks. In edge caching, the pipeline achieves over 80% cache hit rates while balancing base station loads. In autonomous vehicular system, it ensure a 100% no-collision rate, showcasing its reliability in safety-critical scenarios. By exploring these synergies, we offer insights into the future of intelligent and adaptive network systems that automate decision-making and problem-solving.
Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models
Authors: Xubin Wang, Zhiqing Tang, Jianxiong Guo, Tianhui Meng, Chenhao Wang, Tian Wang, Weijia Jia
Date: 2025-03-08 00:00:00
Tasks: Edge-computing, Model Compression, Survey

The rapid advancement of artificial intelligence (AI) technologies has led to an increasing deployment of AI models on edge and terminal devices, driven by the proliferation of the Internet of Things (IoT) and the need for real-time data processing. This survey comprehensively explores the current state, technical challenges, and future trends of on-device AI models. We define on-device AI models as those designed to perform local data processing and inference, emphasizing their characteristics such as real-time performance, resource constraints, and enhanced data privacy. The survey is structured around key themes, including the fundamental concepts of AI models, application scenarios across various domains, and the technical challenges faced in edge environments. We also discuss optimization and implementation strategies, such as data preprocessing, model compression, and hardware acceleration, which are essential for effective deployment. Furthermore, we examine the impact of emerging technologies, including edge computing and foundation models, on the evolution of on-device AI models. By providing a structured overview of the challenges, solutions, and future directions, this survey aims to facilitate further research and application of on-device AI, ultimately contributing to the advancement of intelligent systems in everyday life.
Evaluation of 3D Terrestrial and Aerial Spectrum Sharing with Massive MIMO Systems
Authors: Achiel Colpaert, Zhuangzhuang Cui, Sofie Pollin
Date: 2025-03-07 00:00:00
Tasks: Scheduling

Connecting aerial and terrestrial users with a single base station (BS) is increasingly challenging due to the rising number of aerial users like unmanned aerial vehicles (UAVs). Traditional BSs, designed with down-tilted beams, focus mainly on ground users, but massive MIMO (mMIMO) systems can significantly enhance coverage in low-altitude airspace. This paper analyzes how a mMIMO BS serves both aerial and terrestrial users in a 3D spectrum-sharing scheme. Using Semi-orthogonal User Selection (SUS) and random scheduling, we assess the spectral efficiency and performance limits of these systems. Results reveal that mMIMO effectively supports more terrestrial users, influenced by channel characteristics and user scheduling strategies, providing key insights for future 3D aerial-terrestrial networks.
ORANSight-2.0: Foundational LLMs for O-RAN
Authors: Pranshav Gajjar, Vijay K. Shah
Date: 2025-03-07 00:00:00
Tasks: Code Generation, Marketing, RAG, Retrieval-augmented Generation

Despite the transformative impact of Large Language Models (LLMs) across critical domains such as healthcare, customer service, and business marketing, their integration into Open Radio Access Networks (O-RAN) remains limited. This gap is primarily due to the absence of domain-specific foundational models, with existing solutions often relying on general-purpose LLMs that fail to address the unique challenges and technical intricacies of O-RAN. To bridge this gap, we introduce ORANSight-2.0 (O-RAN Insights), a pioneering initiative aimed at developing specialized foundational LLMs tailored for O-RAN. Built on 18 LLMs spanning five open-source LLM frameworks, ORANSight-2.0 fine-tunes models ranging from 1 to 70B parameters, significantly reducing reliance on proprietary, closed-source models while enhancing performance for O-RAN. At the core of ORANSight-2.0 is RANSTRUCT, a novel Retrieval-Augmented Generation (RAG) based instruction-tuning framework that employs two LLM agents to create high-quality instruction-tuning datasets. The generated dataset is then used to fine-tune the 18 pre-trained open-source LLMs via QLoRA. To evaluate ORANSight-2.0, we introduce srsRANBench, a novel benchmark designed for code generation and codebase understanding in the context of srsRAN, a widely used 5G O-RAN stack. We also leverage ORANBench13K, an existing benchmark for assessing O-RAN-specific knowledge. Our comprehensive evaluations demonstrate that ORANSight-2.0 models outperform general-purpose and closed-source models, such as ChatGPT-4o and Gemini, by 5.421% on ORANBench and 18.465% on srsRANBench, achieving superior performance while maintaining lower computational and energy costs. We also experiment with RAG-augmented variants of ORANSight-2.0 LLMs and thoroughly evaluate their energy characteristics, demonstrating costs for training, standard inference, and RAG-augmented inference.
Cross-Layer-Optimized Link Selection for Hologram Video Streaming over Millimeter Wave Networks
Authors: Yiming Jiang, Yanwei Liu, Jinxia Liu, Antonios Argyriou, Yifei Chen, Wen Zhang
Date: 2025-03-07 00:00:00

Holographic-type communication brings an immersive tele-holography experience by delivering holographic contents to users. As the direct representation of holographic contents, hologram videos are naturally three-dimensional representation, which consist of a huge volume of data. Advanced multi-connectivity (MC) millimeter-wave (mmWave) networks are now available to transmit hologram videos by providing the necessary bandwidth. However, the existing link selection schemes in MC-based mmWave networks neglect the source content characteristics of hologram videos and the coordination among the parameters of different protocol layers in each link, leading to sub-optimal streaming performance. To address this issue, we propose a cross-layer-optimized link selection scheme for hologram video streaming over mmWave networks. This scheme optimizes link selection by jointly adjusting the video coding bitrate, the modulation and channel coding schemes (MCS), and link power allocation to minimize the end-to-end hologram distortion while guaranteeing the synchronization and quality balance between real and imaginary components of the hologram. Results show that the proposed scheme can effectively improve the hologram video streaming performance in terms of PSNR by 1.2dB to 6.4dB against the non-cross-layer scheme.
Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
Authors: Adnan Shahid, Adrian Kliks, Ahmed Al-Tahmeesschi, Ahmed Elbakary, Alexandros Nikou, Ali Maatouk, Ali Mokh, Amirreza Kazemi, Antonio De Domenico, Athanasios Karapantelakis, Bo Cheng, Bo Yang, Bohao Wang, Carlo Fischione, Chao Zhang, Chaouki Ben Issaid, Chau Yuen, Chenghui Peng, Chongwen Huang, Christina Chaccour, Christo Kurisummoottil Thomas, Dheeraj Sharma, Dimitris Kalogiros, Dusit Niyato, Eli de Poorter, Elissa Mhanna, Emilio Calvanese Strinati, Faouzi Bader, Fathi Abdeldayem, Fei Wang, Fenghao Zhu, Gianluca Fontanesi, Giovanni Geraci, Haibo Zhou, Hakimeh Purmehdi, Hamed Ahmadi, Hang Zou, Hongyang Du, Hoon Lee, Howard H. Yang, Iacopo Poli, Igor Carron, Ilias Chatzistefanidis, Inkyu Lee, Ioannis Pitsiorlas, Jaron Fontaine, Jiajun Wu, Jie Zeng, Jinan Li, Jinane Karam, Johny Gemayel, Juan Deng, Julien Frison, Kaibin Huang, Kehai Qiu, Keith Ball, Kezhi Wang, Kun Guo, Leandros Tassiulas, Lecorve Gwenole, Liexiang Yue, Lina Bariah, Louis Powell, Marcin Dryjanski, Maria Amparo Canaveras Galdon, Marios Kountouris, Maryam Hafeez, Maxime Elkael, Mehdi Bennis, Mehdi Boudjelli, Meiling Dai, Merouane Debbah, Michele Polese, Mohamad Assaad, Mohamed Benzaghta, Mohammad Al Refai, Moussab Djerrab, Mubeen Syed, Muhammad Amir, Na Yan, Najla Alkaabi, Nan Li, Nassim Sehad, Navid Nikaein, Omar Hashash, Pawel Sroka, Qianqian Yang, Qiyang Zhao, Rasoul Nikbakht Silab, Rex Ying, Roberto Morabito, Rongpeng Li, Ryad Madi, Salah Eddine El Ayoubi, Salvatore D'Oro, Samson Lasaulce, Serveh Shalmashi, Sige Liu, Sihem Cherrared, Swarna Bindu Chetty, Swastika Dutta, Syed A. R. Zaidi, Tianjiao Chen, Timothy Murphy, Tommaso Melodia, Tony Q. S. Quek, Vishnu Ram, Walid Saad, Wassim Hamidouche, Weilong Chen, Xiaoou Liu, Xiaoxue Yu, Xijun Wang, Xingyu Shang, Xinquan Wang, Xuelin Cao, Yang Su, Yanping Liang, Yansha Deng, Yifan Yang, Yingping Cui, Yu Sun, Yuxuan Chen, Yvan Pointurier, Zeinab Nehme, Zeinab Nezami, Zhaohui Yang, Zhaoyang Zhang, Zhe Liu, Zhenyu Yang, Zhu Han, Zhuang Zhou, Zihan Chen, Zirui Chen, Zitao Shuai
Date: 2025-03-06 00:00:00
Tasks: Management

This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions and user experiences, especially in the context of 6G systems. It highlights the development and deployment of Large Telecom Models (LTMs), which are tailored AI models designed to address the complex challenges faced by modern telecom networks. The paper covers a wide range of topics, from the architecture and deployment strategies of LTMs to their applications in network management, resource allocation, and optimization. It also explores the regulatory, ethical, and standardization considerations for LTMs, offering insights into their future integration into telecom infrastructure. The goal is to provide a comprehensive roadmap for the adoption of LTMs to enhance scalability, performance, and user-centric innovation in telecom networks.
A Survey on Semantic Communications in Internet of Vehicles
Authors: Sha Ye, Qiong Wu, Pingyi Fan, Qiang Fan
Date: 2025-03-03 00:00:00
Tasks: Autonomous Driving, Management, Semantic Communication, Survey

Internet of Vehicles (IoV), as the core of intelligent transportation system, enables comprehensive interconnection between vehicles and their surroundings through multiple communication modes, which is significant for autonomous driving and intelligent traffic management. However, with the emergence of new applications, traditional communication technologies face the problems of scarce spectrum resources and high latency. Semantic communication, which focuses on extracting, transmitting, and recovering some useful semantic information from messages, can reduce redundant data transmission, improve spectrum utilization, and provide innovative solutions to communication challenges in the IoV. This paper systematically reviews state of art of semantic communications in the IoV, elaborates the technical background of IoV and semantic communications, and deeply discusses key technologies of semantic communications in IoV, including semantic information extraction, semantic communication architecture, resource allocation and management, and so on. Through specific case studies, it demonstrates that semantic communications can be effectively employed in the scenarios of traffic environment perception and understanding, intelligent driving decision support, IoV service optimization, and intelligent traffic management. Additionally, it analyzes the current challenges and future research directions. This survey reveals that semantic communications has broad application prospects in IoV, but it is necessary to solve the real existing problems by combining advanced technologies to promote its wide application in IoV and contributing to the development of intelligent transportation system.