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Found 21 papers, 2 papers with code
Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
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.
Large AI Model for Delay-Doppler Domain Channel Prediction in 6G OTFS-Based Vehicular Networks
Channel prediction is crucial for high-mobility vehicular networks, as it enables the anticipation of future channel conditions and the proactive adjustment of communication strategies.
Feedback-Free Resource Scheduling: Towards Flexible Multi-BS Cooperation in FD-RAN
Flexible cooperation among base stations (BSs) is critical to improve resource utilization efficiency and meet personalized user demands.
ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery using Limited Data
Traffic flow estimation (TFE) is crucial for urban intelligent traffic systems.
Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles
The growing number of connected vehicles offers an opportunity to leverage internet of vehicles (IoV) data for traffic state estimation (TSE) which plays a crucial role in intelligent transportation systems (ITS).
Spatial-Temporal Generative AI for Traffic Flow Estimation with Sparse Data of Connected Vehicles
In response, this paper introduces a novel and cost-effective TFE framework that leverages sparse PVD and improves accuracy by applying the spatial-temporal generative artificial intelligence (GAI) framework.
Optimizing 6G Integrated Sensing and Communications (ISAC) via Expert Networks
Integrated Sensing and Communications (ISAC) is one of the core technologies of 6G, which combines sensing and communication functions into a single system.
Coverage Analysis of Downlink Transmission in Multi-Connectivity Cellular V2X Networks
To this end, this paper proposes a framework for analyzing the performance of multi-connectivity in C-V2X downlink transmission, with a focus on the performance indicators of joint distance distribution and coverage probability.
Performance Analysis of Uplink/Downlink Decoupled Access in Cellular-V2X Networks
This paper firstly develops an analytical framework to investigate the performance of uplink (UL) / downlink (DL) decoupled access in cellular vehicle-to-everything (C-V2X) networks, in which a vehicle's UL/DL can be connected to different macro/small base stations (MBSs/SBSs) separately.
Locally Differentially Private In-Context Learning
Large pretrained language models (LLMs) have shown surprising In-Context Learning (ICL) ability.
Performance Analysis for Downlink Transmission in Multi-Connectivity Cellular V2X Networks
With the ever-increasing number of connected vehicles in the fifth-generation mobile communication networks (5G) and beyond 5G (B5G), ensuring the reliability and high-speed demand of cellular vehicle-to-everything (C-V2X) communication in scenarios where vehicles are moving at high speeds poses a significant challenge. Recently, multi-connectivity technology has become a promising network access paradigm for improving network performance and reliability for C-V2X in the 5G and B5G era.
Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse
As highly computerized avatars of Vehicular Metaverse Users (VMUs), the Vehicle Twins (VTs) deployed in edge servers can provide valuable metaverse services to improve driving safety and on-board satisfaction for their VMUs throughout journeys.
Channel-Feedback-Free Transmission for Downlink FD-RAN: A Radio Map based Complex-valued Precoding Network Approach
As the demand for high-quality services proliferates, an innovative network architecture, the fully-decoupled RAN (FD-RAN), has emerged for more flexible spectrum resource utilization and lower network costs.
Towards Scalable Wireless Federated Learning: Challenges and Solutions
The explosive growth of smart devices (e. g., mobile phones, vehicles, drones) with sensing, communication, and computation capabilities gives rise to an unprecedented amount of data.
Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach
To fully leverage the high performance of traditional model-based methods and the low complexity of the NN-based method, a knowledge distillation (KD) based algorithm distillation (AD) method is proposed in this paper to improve the performance and convergence speed of the NN-based method, where traditional SINR optimization methods are employed as ``teachers" to assist the training of NNs, which are ``students", thus enhancing the performance of unsupervised and reinforcement learning techniques.
Spectral Efficiency Analysis of Uplink-Downlink Decoupled Access in C-V2X Networks
The uplink (UL)/downlink (DL) decoupled access has been emerging as a novel access architecture to improve the performance gains in cellular networks.
Spectral Efficiency Analysis of Uplink-Downlink Decoupled Access in C-V2X Networks
Heterogeneous Ultra-Dense Networks with Traffic Hotspots: A Unified Handover Analysis
By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived.
Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning
Based on the convergence analysis, we formulate an optimization problem to minimize the upper bound to enhance the learning performance, followed by proposing an alternating optimization algorithm to facilitate the optimal transceiver design for AirComp-assisted FL.
AI-Native Network Slicing for 6G Networks
Then, network slicing solutions are studied to support emerging AI services by constructing AI instances and performing efficient resource management, i. e., slicing for AI.
Graph-Based Semi-Supervised Learning with Non-ignorable Non-response
Graph-based semi-supervised learning is a very powerful tool in classification tasks, while in most existing literature the labelled nodes are assumed to be randomly sampled.
LSTOD: Latent Spatial-Temporal Origin-Destination prediction model and its applications in ride-sharing platforms
Origin-Destination (OD) flow data is an important instrument in transportation studies.
