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arXiv 2024年

作者： Wei, Zhiqiang Xu, Dongfang Li, Shuangyang Song, Shenghui Ng, Derrick Wing Kwan Caire, Giuseppe The School of Mathematics and Statistics Xi'an Jiaotong University Xi'An710049 China The Division of Integrative Systems and Design The Hong Kong University of Science and Technology Clear Water Bay 999077 Hong Kong The Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Berlin10587 Germany The School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia

Multiple access is the cornerstone technology for each generation of wireless cellular networks, which fundamentally determines the method of radio resource sharing and significantly influences both the system performance and transceiver complexity. Meanwhile, resource allocation design plays a crucial role in multiple access, as it can manage both encompassing radio resources and interference, and it is critical for providing high-speed and reliable communication services to multiple users. Given that resource allocation design is intrinsically scenario-specific and the optimization tools for resource allocation design are typically varied, in this paper, we present a comprehensive tutorial overview for junior researchers in this field, aiming to offer a foundational guide for resource allocation design in the context of next-generation multiple access (NGMA). Our discussion spans a broad range of fundamental topics: from typical system models, through intriguing problem formulation in resource allocation design, to the exploration of various potential optimization solution methodologies. Initially, we identify three types of channels in future wireless cellular networks over which NGMA will be implemented, namely: natural channels, reconfigurable channels, and functional channels. Natural channels are traditional uplink and downlink communication channels;reconfigurable channels are defined as channels that can be proactively reshaped via emerging platforms or techniques, such as intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and movable/fluid antenna (M/FA);and functional channels support not only communication but also other functionalities simultaneously, with typical examples including integrated sensing and communication (ISAC) and joint computing and communication (JCAC) channels. Then, we introduce NGMA models applicable to these three types of channels that cover most of the practical communication scenarios of future wireless communi

关键词： Resource allocation

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Mm-Wave to Sub-THz Frequencies for Non-Invasive Melamine Detection in Food Safety

Mm-Wave to Sub-THz Frequencies for Non-Invasive Melamine Det...

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IEEE Conference on Antenna Measurements & Applications (CAMA)

作者： Nguyen Ngoc Mai-Khanh Shitaro Takada Keizo Inagaki Ahmad Alkasimi Anh-Vu Pham Akio Higo Bich-Yen Nguyen Tetsuya Kawanishi Systems Design Lab (d.lab) The University of Tokyo Tokyo Japan Faculty of Science and Engineering Waseda University Tokyo Japan National Inst. Of Information and Conmmunications Technology (NICT) Tokyo Japan Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Electrical and Computer Engineering University of California Davis California U.S. Soitec France Waseda University Tokyo Japan

This paper presents the exploration of the detection of melamine, a harmful substance, across a wide mm-wave frequency range for food safety applications. The experiment investigates the presence of melamine impurities in rice and milk powders. To conduct the experiment, two different approaches are utilized: a 77-GHz radar approach and a vector network analyzer connected to frequency extenders and standard horn antennas. These approaches cover two frequency bands, namely 220-330 GHz and then 330-500 GHz. The substances involved in the measurements are melamine, milk, rice, and their mixtures. In addition, we propose a technique for the quantitative determination of melamine in mixtures. This method was validated through measurements taken mm-wave frequency range. The results demonstrate the feasibility of determining melamine concentrations in mixtures across diverse mm-wave and sub-THz frequency bands.

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Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission

arXiv

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arXiv 2024年

作者： Zhang, Ruichen Du, Hongyang Liu, Yinqiu Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Kim, Dong In The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

In response to the needs of 6G global communications, satellite communication networks have emerged as a key solution. However, the large-scale development of satellite communication networks is constrained by the complex system models, whose modeling is challenging for massive users. Moreover, transmission interference between satellites and users seriously affects communication performance. To solve these problems, this paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) approach to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm and utilize retrieval-augmented generation (RAG) to extract satellite expert knowledge that supports mathematical modeling. Afterward, by integrating the expertise of multiple specialized components, we propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem. Each expert can optimize the optimization variables at which it excels through specialized training through its own network and then aggregates them through the gating network to perform joint optimization. The simulation results validate the accuracy and effectiveness of employing a generative agent for problem formulation. Furthermore, the superiority of the proposed MoE-ppo approach over other benchmarks is confirmed in solving the formulated problem. The adaptability of MoE-PPO to various customized modeling problems has also been demonstrated. © 2024, CC BY.

关键词： Satellite communication systems

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Energy-Efficient Data Offloading for Earth Observation Satellite Networks

Energy-Efficient Data Offloading for Earth Observation Satel...

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IEEE Conference on Vehicular Technology (VTC)

作者： Lijun He Ziye Jia Juncheng Wang Feng Wang Erick Lansard Chau Yuen School of Software Northwestern Polytechnical University China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics China Department of Computer Science Hong Kong Baptist University China Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In Earth Observation Satellite Networks (EOSNs) with a large number of battery-carrying satellites, proper power allocation and task scheduling are crucial to improving the data offloading efficiency. As such, we jointly optimize power allocation and task scheduling to achieve energy-efficient data offloading in EOSNs, aiming to balance the objectives of reducing the total energy consumption and increasing the sum weights of tasks. First, we derive the optimal power allocation solution to the joint optimization problem when the task scheduling policy is given. Second, leveraging the conflict graph model, we transform the original joint optimization problem into a maximum weight independent set problem when the power allocation strategy is given. Finally, we utilize the genetic framework to combine the above special solutions as a two-layer solution for the joint optimization problem. Simulation results demonstrate that our proposed solution can properly balance the sum weights of tasks and the total energy consumption, thus achieving superior system performance over the current best alternatives.

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Energy-Efficient Data Offloading for Earth Observation Satellite Networks

arXiv

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arXiv 2024年

作者： He, Lijun Jia, Ziye Wang, Juncheng Wang, Feng Lansard, Erick Yuen, Chau School of Software Northwestern Polytechnical University China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics China Department of Computer Science Hong Kong Baptist University China Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Electrical and Electronics Engineering Nanyang Technological University Singapore

In Earth Observation Satellite Networks (EOSNs) with a large number of battery-carrying satellites, proper power allocation and task scheduling are crucial to improving the data offloading efficiency. As such, we jointly optimize power allocation and task scheduling to achieve energy-efficient data offloading in EOSNs, aiming to balance the objectives of reducing the total energy consumption and increasing the sum weights of tasks. First, we derive the optimal power allocation solution to the joint optimization problem when the task scheduling policy is given. Second, leveraging the conflict graph model, we transform the original joint optimization problem into a maximum weight independent set problem when the power allocation strategy is given. Finally, we utilize the genetic framework to combine the above special solutions as a two-layer solution for the joint optimization problem. Simulation results demonstrate that our proposed solution can properly balance the sum weights of tasks and the total energy consumption, achieving superior system performance over the current best alternatives. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy utilization

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

arXiv

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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

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GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations

arXiv

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arXiv 2023年

作者： Ranasinghe, Nisal Senanayake, Damith Seneviratne, Sachith Premaratne, Malin Halgamuge, Saman AI Optimization and Pattern Recognition Research Group Dept. of Mechanical Eng. University of Melbourne Australia Melbourne School of Design University of Melbourne Australia Department of Electrical and Computer Systems Engineering Monash University Australia

Traditional machine learning is generally treated as a black-box optimization problem and does not typically produce interpretable functions that connect inputs and outputs. However, the ability to discover such interpretable functions is desirable. In this work, we propose GINN-LP, an interpretable neural network to discover the form and coefficients of the underlying equation of a dataset, when the equation is assumed to take the form of a multivariate Laurent Polynomial. This is facilitated by a new type of interpretable neural network block, named the "power-term approximator block", consisting of logarithmic and exponential activation functions. GINN-LP is end-to-end differentiable, making it possible to use backpropagation for training. We propose a neural network growth strategy that will enable finding the suitable number of terms in the Laurent polynomial that represents the data, along with sparsity regularization to promote the discovery of concise equations. To the best of our knowledge, this is the first model that can discover arbitrary multivariate Laurent polynomial terms without any prior information on the order. Our approach is first evaluated on a subset of data used in SRBench, a benchmark for symbolic regression. We first show that GINN-LP outperforms the state-of-the-art symbolic regression methods on datasets generated using 48 real-world equations in the form of multivariate Laurent polynomials. Next, we propose an ensemble method that combines our method with a high-performing symbolic regression method, enabling us to discover non-Laurent polynomial equations. We achieve state-of-the-art results in equation discovery, showing an absolute improvement of 7.1% over the best contender, by applying this ensemble method to 113 datasets within SRBench with known ground-truth equations. © 2023, CC BY-NC-ND.

关键词： Polynomials

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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Effects of Air Exposure on the Interfacial Capacitance of Laser-Induced Graphene (LIG) with and without Silver (Ag) Nanoparticle Loading

Effects of Air Exposure on the Interfacial Capacitance of La...

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2023 IEEE International Conference on Sensors and Nanotechnology, SENNANO 2023

作者： Goh, Rou Xuan Sultan, Suhana Mohamed Pu, Suan Hui Leow, Pei Ling Lee, Hing Wah Fishlock, Sam J. Chong, Harold M. H. Ling, Ting Yang University of Southampton Malaysia Smart Manufacturing and Systems Research Group Johor Iskandar Puteri79100 Malaysia Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Skudai81310 Malaysia MIMOS Berhad Technology Park Malaysia Centre for Semiconductor & Thin Film Research Kuala Lumpur57000 Malaysia TEDI-London The Engineering & Design Institute LondonSE16 7LG United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

ISBN: (纸本)9798350333312

Research on laser-induced graphene (LIG) and LIG with silver nanoparticles loading (Ag-LIG) are increasing by the day as more of its potential are uncovered each day. With the LIG fabrication looking to increase further in the future, it will be useful to confirm and verify any changes in the quality of LIG when it is exposed to the ambient air. In normal laboratory conditions, researchers store samples in petri dishes or any other covered storage, but inevitably the air within the storage will still be in contact with the samples. In this study, we left LIG and Ag-LIG samples in a lidded petri dish after its fabrication and measured the interfacial capacitance of each sample using a standard three-electrode electrochemical setup with a potential range from 0 V to 0.4 V on different days. Results show that the interfacial capacitance of both LIG and Ag-LIG drops as it is left in the storage container longer after its fabrication, with Ag-LIG showing a greater decrease after 10 days compared to LIG. This indicates that the silver nanoparticles played an important part during the oxidation process, making Ag-LIG more sensitive to its environment during prolonged unuse. The findings from this study shows the potential usage of LIG and Ag-LIG as a low-cost single-use indicator for air exposure. © 2023 IEEE.

关键词： Graphene

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One-Shot Action Recognition via Multi-Scale Spatial-Temporal Skeleton Matching

arXiv

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arXiv 2023年

作者： Yang, Siyuan Liu, Jun Lu, Shijian Hwa, Er Meng Kot, Alex C. Rapid-Rich Object Search Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore Singapore Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore School of Computer Science & Engineering Nanyang Technological University Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

One-shot skeleton action recognition, which aims to learn a skeleton action recognition model with a single training sample, has attracted increasing interest due to the challenge of collecting and annotating large-scale skeleton action data. However, most existing studies match skeleton sequences by comparing their feature vectors directly which neglects spatial structures and temporal orders of skeleton data. This paper presents a novel one-shot skeleton action recognition technique that handles skeleton action recognition via multi-scale spatialtemporal feature matching. We represent skeleton data at multiple spatial and temporal scales and achieve optimal feature matching from two perspectives. The first is multi-scale matching which captures the scale-wise semantic relevance of skeleton data at multiple spatial and temporal scales simultaneously. The second is cross-scale matching which handles different motion magnitudes and speeds by capturing sample-wise relevance across multiple scales. Extensive experiments over three large-scale datasets (NTU RGB+D, NTU RGB+D 120, and PKU-MMD) show that our method achieves superior one-shot skeleton action recognition, and outperforms SOTA consistently by large margins. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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