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GUEST EDITORIAL

China Communications 2023年第2期 3-6页

作者： Yuan Wu Xu Chen Feng Lyu Xianfu Chen Xumin Huang Jie Gao Yueyue Dai Tony Q.S.Quek Yan Zhang the State Key Laboratory of Internet of Things for Smart City University of Macau the Department of Computer and Information Science University of Macau Sun Yat-sen University Sun Yat-sen University Institute of Advanced Networking and Computing Systems Sun Yat-sen University National and Local Joint Engineering Laboratory the School of Computer Science and Engineering Central South University the VTT Technical Research Centre of Finland the School of Automation Guangdong University of Technology the School of Information Technology Carleton University Huazhong University of Science and Technology Singapore University of Technology and Design(SUTD) the IEEE Communications Society Technical Committee on Green Communications and Computing(TCGCC)

With the growing maturity of the advanced edge-cloud collaboration and integrated sensing-communication-computing systems, edge intelligence has been envisioned as one of the enabling technologies for ubiquitous and l...

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Wireless Hallucination in Generative AI-enabled Communications: Concepts, Issues, and Solutions

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arXiv 2025年

作者： Wang, Xudong Wang, Jiacheng Feng, Lei Niyato, Dusit Zhang, Ruichen Kang, Jiawen Xiong, Zehui Du, Hongyang Mao, Shiwen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn United States

Generative AI (GenAI) is driving the intelligence of wireless communications. Due to data limitations, random generation, and dynamic environments, GenAI may generate channel information or optimization strategies that violate physical laws or deviate from actual real-world requirements. We refer to this phenomenon as wireless hallucination, which results in invalid channel information, spectrum wastage, and low communication reliability but remains underexplored. To address this gap, this article provides a comprehensive concept of wireless hallucinations in GenAI-driven communications, focusing on hallucination mitigation. Specifically, we first introduce the fundamental, analyze its causes based on the GenAI workflow, and propose mitigation solutions at the data, model, and post-generation levels. Then, we systematically examines representative hallucination scenarios in GenAI-enabled communications and their corresponding solutions. Finally, we propose a novel integrated mitigation solution for GenAI-based channel estimation. At the data level, we establish a channel estimation hallucination dataset and employ generative adversarial networks (GANs)-based data augmentation. Additionally, we incorporate attention mechanisms and large language models (LLMs) to enhance both training and inference performance. Experimental results demonstrate that the proposed hybrid solutions reduce the normalized mean square error (NMSE) by 0.19, effectively reducing wireless hallucinations. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

Joining Edge-Enabled Metaverse Services with Network Externality: A Stackelberg Game Approach

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Joining Edge-Enabled Metaverse Services with Network Externa...

Metaverse Computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Yuna Jiang Jiawen Kang Dusit Niyato Xiaohu Ge Zehui Xiong Minrui Xu Ruilong Deng School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

The metaverse is defined as a three-dimensional virtual-real fusion network focused on social connection. Edge computing can empower the metaverse by providing computing resources to realize real-time motion tracking, visual or auditory rendering and virtual scene dynamic change at the edge of networks. Users enjoy the immersive experience provided by the Metaverse Service Provider (MSP), and interact through digital avatars in the virtual world by requesting metaverse services. In this paper, we formulate the edge computing resources pricing, metaverse service rendering bitrate pricing and request problem in the metaverse. Especially, we model the interactions among edge servers, the MSP and Metaverse Service Users (MSUs) as a hierarchical three-stage Stackelberg game. The problem of joining edge-enabled metaverse services is also studied with the consideration of network externality, including the network effect and congestion effect. Based on the backward induction method, we derive the optimal computing resources pricing of edge servers, rendering bitrate pricing of the MSP, and rendering bitrate requests of MSUs. We also prove the existence and uniqueness of the Stackelberg game equilibrium. Simulation results show that the network effect can bring more positive utilities to entities when the social tie coefficient among MSUs is higher. Besides, all entities in the edge-enabled metaverse are affected negatively by the congestion effect.

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Mixture of Experts for Network Optimization: A Large Language Model-enabled Approach

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arXiv 2024年

作者： Du, Hongyang Liu, Guangyuan Lin, Yijing Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLM-enabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems. © 2024, CC BY.

关键词： Reinforcement learning

Slicing4Meta: An Intelligent Integration Framework with Multi-dimensional Network Resources for Metaverse-as-a-Service in Web 3.0

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arXiv 2022年

作者： Liu, Yi-Jing Du, Hongyang Niyato, Dusit Feng, Gang Kang, Jiawen Xiong, Zehui The School of Computer Science and Engineering Nanyang Technological University Singapore The National Laboratory of Communications University of Electronic Science and Technology of China China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

As the next-generation Internet paradigm, Web 3.0 encapsulates the expectations of user-centric immersion and interaction experiences in a decentralized manner. Metaverse, a virtual world interacting with the physical world, is becoming one of the most potential technology to push forward with Web 3.0. In the Metaverse, users expect to tailor immersive and interactive experiences by customizing real-time Metaverse services (e.g., augmented/virtual reality and digital twin) in the three-dimensional virtual world. Nevertheless, there are still no unified solutions for the Metaverse services in terms of orchestration and management. It is calling for a continuous and seamless evolution of mobile systems to support Metaverse services, and thus bring Metaverse into reality. In this paper, to provide scalable subscription solutions for Metaverse services, we propose a new concept, named Metaverse-as-a-Service (MaaS), in which various physical-virtual components and technologies in the Metaverse can be delivered as services. Furthermore, to unify the orchestration and management of MaaS, we propose a novel framework, called Slicing4Meta, to customize Metaverse services by intelligently integrating MaaS models and the associated multi-dimensional resources on the components and technologies. Additionally, we propose the classification for typical Metaverse services based on the quality of experience (QoE) requirements and illustrate how to fulfill the QoE requirements under the Slicing4Meta framework. We then illustrate a virtual travel study case, in which we examine the relationship between the QoE and the multi-dimensional resources by quantitatively modeling the QoE of Metaverse users. Finally, we discuss some open challenges of Slicing4Meta and propose potential solutions to address the challenges. © 2022, CC BY.

关键词： Virtual reality

A Cross-Domain Information Modeling Method for Health Management of Aircraft Hydraulic systems

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A Cross-Domain Information Modeling Method for Health Manage...

Chinese automation Congress (CAC)

作者： Haoyuan Zhang Yang Li Zehua Ge Yaoxing Shang School of Automation Science and Electrical Engineering Beihang University Beijing China State Key Laboratory of Fluid Power & Mechatronic Systems Hangzhou China Research Institute for Frontier Science Beihang University Beijing China Tianmushan Laboratory Hangzhou China Shenyang Aircraft Design and Research Institute Shenyang China Ningbo Institute of Technology Beihang University Ningbo China

Facing the development trend of airborne system integration, intelligence, and networking, in order to improve the level of airborne system health management and information interconnection, and solve the problems of data standardization description and cross-system integration, a cross-domain information modeling method for health management of aircraft hydraulic systems is proposed. The standardized representation of hydraulic component information is designed, the type definition and constraint rules of typical components are described, and the information model of a typical mainline aircraft hydraulic system is established. A320 aircraft braking system as an example to build the information model for verification, through the software tool to build the information model and generate XML files, developed a server to load the information model and use client access to realize the information model read and write, to verify the feasibility and effectiveness of the information model.

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Multi-objective clustering analysis via combinatorial pigeon inspired optimization

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Science China(Technological Sciences) 2020年第7期63卷 1302-1313页

作者： CHEN Lin DUAN HaiBin FAN YanMing WEI Chen Bio-inspired Autonomous Flight Systems Research Group School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100083China Peng Cheng Laboratory Shenzhen 518000China Shenyang Aircraft Design and Research Institute Aviation Industry Corporation of China(AV1C)Shenyang 110035China

Multi-objective data clustering is an important issue in data mining, and the realization of data clustering using the multiobjective optimization technique is a significant topic. A combinatorial multi-objective pigeon inspired optimization(CMOPIO)with ring topology is proposed to solve the clustering problem in this paper. In the CMOPIO, a delta-locus based coding approach is employed to encode the pigeons. Thus, the length of pigeon representation and the dimension of the search space are significantly reduced. Thereby, the computational load can be effectively depressed. In this way, the pigeon inspired optimization(PIO) algorithm can be discretized with an auxiliary vector to address data clustering. Moreover, an index-based ring topology with the ability of contributing to maintain flock diversity is adopted to improve the CMOPIO performance. Comparative simulation results demonstrate the feasibility and effectiveness of our proposed CMOPIO for solving data clustering problems.

关键词： multi-objective data clustering combinatorial multi-objective pigeon inspired optimization delta-locus based coding pigeon representation

Urban trip generation forecasting based on gradient boosting algorithm 1

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Urban trip generation forecasting based on gradient boosting...

1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Li, Zhishuai Xiong, Gang Zhang, Yu Zheng, Meng Dong, Xisong Lv, Yisheng Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Municipal Institute of City Planning and Design Traffic Planning Department Beijing China

ISBN: (纸本)9781665433372

The four-step transportation model plays an important role in urban planning. The quality of the first phase, i.e. trip generation, determines the performance of the global course. The majority of trip generation forecasting models highly rely on mathematical derivation and have many predictor variables during the prediction, which leads to low accuracy of results and requires laboriously hand-crafted design of input vectors. This paper is the first to introduce the gradient boosting decision tree (GBDT) algorithm for trip generation prediction, and harmonizes such a powerful machine learning method with traditional urban planning requirements to achieve better prediction performance. Unlike the commonly used linear regression method, GBDT can automatically perform feature selection and model the non-linear relationships between input and output variables. Experimental results on real-world residential travel census in Beijing prove that the GBDT model significantly outperforms the baseline and can forecast the trip generation more accurately. © 2021 IEEE.

关键词： Decision trees

Embedding Reliability Verification Constraints into Generation Expansion Planning

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arXiv 2025年

作者： Liu, Peng Cheng, Lian Omell, Benjamin P. Burgard, Anthony P. School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China The Institute for Design of Advanced Energy Systems National Energy Technology Laboratory Pittsburgh United States

Generation planning approaches face challenges in managing the incompatible mathematical structures between stochastic production simulations for reliability assessment and optimization models for generation planning, which hinders the integration of reliability constraints. This study proposes an approach to embedding reliability verification constraints into generation expansion planning by leveraging a weighted oblique decision tree (WODT) technique. For each planning year, a generation mix dataset, labeled with reliability assessment simulations, is generated. An WODT model is trained using this dataset. Reliability-feasible regions are extracted via depth-first search technique and formulated as disjunctive constraints. These constraints are then transformed into mixed-integer linear form using a convex hull modeling technique and embedded into a unit commitment-integrated generation expansion planning model. The proposed approach is validated through a long-term generation planning case study for the Electric Reliability Council of Texas (ERCOT) region, demonstrating its effectiveness in achieving reliable and optimal planning solutions. © 2025, CC BY-NC-SA.

关键词： Stochastic models