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User Grouping-Based Hierarchical Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Assisted Downlink Communication

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IEEE Transactions on Cognitive Communications and networking 2025年

作者： Yang, Zhixiang Feng, Lei Zhou, Fanqin Xie, Kunyi Qiu, Xuesong Xiong, Zehui Yuen, Chau Han, Zhu Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore487372 Singapore Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The joint optimization of rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) offers a mutually beneficial solution. In this paper, we study a RIS-assisted downlink user grouping-based hierarchical RSMA communication network, where the base station transmits the common streams and private streams simultaneously to serve multiple users. The sum rate (SR) optimization problem is constructed to jointly optimize user grouping, beamforming, and rate splitting. To tackle the NP-hard problem, a user grouping algorithm is first provided to form the hierarchical RSMA model. Next, an equivalent weighted minimum mean-square error (WMMSE) problem is constructed and a WMMSE-based block coordinate descent (BCD) algorithm is designed to update the remaining variables in an iterated way. Specifically, the optimization of transmit beamforming and the rate splitting matrix is reduced to solving a typical convex quadratically constrained quadratic program problem. The solution for passive beamforming is recovered from the intermediate result obtained by the manifold optimization method. Finally, the process of the BCD algorithm is presented. The numerical results indicate that the proposed scheme evidently surpasses one-layer RSMA scheme, NOMA scheme, SDMA scheme, and no RIS scheme, especially in overloaded scenarios. The proposed scheme further increases the SR of common steams compared to the hierarchical RSMA scheme and significantly reduces computational complexity compared to the generalized RSMA scheme. © 2015 IEEE.

关键词： Mean square error

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Demand-Aware Beam Hopping and Power Allocation for Load Balancing in Digital Twin empowered LEO Satellite Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Zhao, Ruili Cai, Jun Luo, Jiangtao Gao, Junpeng Ran, Yongyi Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing400065 China Department of Electrical and Computer Engineering MontrealQCH3G 1M8 Canada Chongqing University of Posts and Telecommunications Electronic Information and Networking Research Institute Chongqing400065 China

Low-Earth orbit (LEO) satellites utilizing beam hopping (BH) technology offer extensive coverage, low latency, high bandwidth, and significant flexibility. However, the uneven geographical distribution and temporal variability of ground traffic demands, combined with the high mobility of LEO satellites, present significant challenges for efficient beam resource utilization. Traditional BH methods based on GEO satellites fail to address issues such as satellite interference, overlapping coverage, and mobility. This paper explores a Digital Twin (DT)-based collaborative resource allocation network for multiple LEO satellites with overlapping coverage areas. A two-tier optimization problem, focusing on load balancing and cell service fairness, is proposed to maximize throughput and minimize inter-cell service delay. The DT layer optimizes the allocation of overlapping coverage cells by designing BH patterns for each satellite, while the LEO layer optimizes power allocation for each selected service cell. At the DT layer, an Actor-Critic network is deployed on each agent, with a global critic network in the cloud center. The A3C algorithm is employed to optimize the DT layer. Concurrently, the LEO layer optimization is performed using a Multi-Agent Reinforcement Learning algorithm, where each beam functions as an independent agent. The simulation results show that this method reduces satellite load disparity by about 72.5% and decreases the average delay to 12ms. Additionally, our approach outperforms other benchmarks in terms of throughput, ensuring a better alignment between offered and requested data. © 2025 IEEE. All rights reserved.

关键词： Satellite communication systems

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DialogueAgents: A Hybrid Agent-Based Speech Synthesis Framework for Multi-Party Dialogue

arXiv

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arXiv 2025年

作者： Li, Xiang Pan, Duyi Xiao, Hongru Han, Jiale Tang, Jing Ma, Jiabao Wang, Wei Cheng, Bo State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Department of Computer Science and Engineering Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong

Speech synthesis is crucial for human-computer interaction, enabling natural and intuitive communication. However, existing datasets involve high construction costs due to manual annotation and suffer from limited character diversity, contextual scenarios, and emotional expressiveness. To address these issues, we propose DialogueAgents, a novel hybrid agent-based speech synthesis framework, which integrates three specialized agents—a script writer, a speech synthesizer, and a dialogue critic—to collaboratively generate dialogues. Grounded in a diverse character pool, the framework iteratively refines dialogue scripts and synthesizes speech based on speech review, boosting emotional expressiveness and paralinguistic features of the synthesized dialogues. Using DialogueAgent, we contribute MultiTalk, a bilingual, multi-party, multi-turn speech dialogue dataset covering diverse topics. Extensive experiments demonstrate the effectiveness of our framework and the high quality of the MultiTalk dataset. We release the dataset and code1 to facilitate future research on advanced speech synthesis models and customized data generation. Copyright © 2025, The Authors. All rights reserved.

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An Adaptive Service Function Chains Mapping With Multi-Task Deep Reinforcement Learning

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IEEE Transactions on Network Science and engineering 2025年

作者： Wei, Wenting Wang, Qingyi Gu, Huaxi Zheng, Danyang Zhang, Ning Wu, Celimuge Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Southwest Jiaotong University School of Computing and Artificial Intelligence China University of Windsor Department of Electrical and Computer Engineering Canada University of Electro-Communications Meta-Networking Research Center Japan

Network function virtualization (NFV) facilitates different virtual network functions (VNF) to be dynamically chained in sequence to offer new services in a flexible, scalable, and cost-effective manner. Recent years have witnessed the increasing diverse service demands from the ever-increasing new applications, which has posed significant challenges to the efficient and sequential execution of VNFs to achieve specific objectives, especially under conditions of shared resources. To address these challenges, substantial efforts have been dedicated to enhancing resource utilization and minimizing the costs associated with service function chains (SFCs), while maintaining high quality of service. However, an overemphasis on cost reduction can sometimes result in network congestion, which ultimately degrades both network performance and service quality. Given the time-varying and unpredictable characteristics of SFCs, it is essential to leverage their temporal features, along with those of network states, for adaptive SFC mapping. In this paper, we introduce an adaptive online SFC mapping algorithm to reduce operational costs and alleviate network congestion. This is achieved through the adaptive allocation of VNFs and the control of traffic routing between them. Our approach incorporates multi-task deep reinforcement learning to manage the coexistence of multiple SFC requests with varying resource requirements. Specifically, we integrate a long short-term memory (LSTM) layer into our model to capture the temporal dynamics of network states and resource demands, thereby enabling more effective long-term planning. To address the issue of reward sparsity, we implement a hierarchical reward mechanism and reward shaping techniques. Experimental results demonstrate that our algorithm achieves near-optimal performance in optimizing service delay, bandwidth consumption, and network congestion, while also ensuring a high acceptance rate for user requests. © 2013 IEEE.

关键词： Network function virtualization

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User-Centric Multi-Static Sensing for Joint User and Target Tracking in Mobile Wireless Systems

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IEEE Transactions on Wireless Communications 2025年

作者： Hu, Boyang Tian, Hui Ni, Wei Fan, Shaoshuai Hossain, Ekram Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Technology Faculty of Engineering and Information Technology SydneyNSW2007 Australia University of Manitoba Department of Electrical and Computer Engineering WinnipegMBR3T 2N2 Canada

This paper presents a novel user-centric sensing framework, where a user equipment (UE) acts as the receiver of a multi-static radar sensing system and utilizes the communication signals emitted by base stations (BSs) and scattered by the targets for joint UE and target tracking. Specifically, we propose to locate the UE using the least squares (LS) estimator with a one-dimensional (1D) search and then develop a two-dimensional (2D) target identification approach using the estimated target location and motion of each path based on mean-shift clustering. After the motion parameters of the UE and targets are estimated, a joint UE and target tracking algorithm is designed based on the analysis of the localization and motion estimation errors. Extensive simulations corroborate the ability of our approach to estimate target parameters and cluster and identify targets. Specifically, the average estimation error of the target number is only 0.18. The speed and heading accuracy of the UE and targets is [0.121 m/s, 3.879°] and [0.199 m/s, 4.669°], respectively. In joint UE and target tracking, our scheme outperforms the benchmarks of the extended Kalman filter (EKF) and belief propagation (BP) by at least 33.16% and 10.29%, respectively, even though the EKF and BP require a-priori knowledge of the motion parameters and target identification. © 2002-2012 IEEE.

关键词： Aircraft detection

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Learnable Residual-based Latent Denoising in Semantic Communication

arXiv

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arXiv 2025年

作者： Xu, Mingkai Wu, Yongpeng Shi, Yuxuan Xia, Xiang-Gen Zhang, Wenjun Zhang, Ping Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Cyber and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

A latent denoising semantic communication (SemCom) framework is proposed for robust image transmission over noisy channels. By incorporating a learnable latent denoiser into the receiver, the received signals are preprocessed to effectively remove the channel noise and recover the semantic information, thereby enhancing the quality of the decoded images. Specifically, a latent denoising mapping is established by an iterative residual learning approach to improve the denoising efficiency while ensuring stable performance. Moreover, channel signal-to-noise ratio (SNR) is utilized to estimate and predict the latent similarity score (SS) for conditional denoising, where the number of denoising steps is adapted based on the predicted SS sequence, further reducing the communication latency. Finally, simulations demonstrate that the proposed framework can effectively and efficiently remove the channel noise at various levels and reconstruct visual-appealing images. Copyright © 2025, The Authors. All rights reserved.

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Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions

arXiv

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arXiv 2025年

作者： Yi, Jingyuan Xu, Zeqiu Huang, Tianyi Yu, Peiyang Carnegie Mellon University Information Networking Institute 4616 Henry Street PittsburghPA15213 United States Carnegie Mellon University Information Networking Institute 5000 Forbes Avenue PittsburghPA15213 United States University of California Berkeley Department of Electrical Engineering and Computer Sciences 253 Cory Hall BerkeleyCA94720 United States

The pervasiveness of the dissemination of fake news through social media platforms poses critical risks to the trust of the general public, societal stability, and democratic institutions. This challenge calls for novel methodologies in detection, which can keep pace with the dynamic and multi-modal nature of misinformation. Recent works include powering the detection using large language model advances in multimodal frameworks, methodologies using graphs, and adversarial training in the literature of fake news. Based on the different approaches which can bring success, some key highlights will be underlined: enhanced LLM-improves accuracy through more advanced semantics and cross-modality fusion for robust detections. The review further identifies critical gaps in adaptability to dynamic social media trends, real-time, and cross-platform detection capabilities, as well as the ethical challenges thrown up by the misuse of LLMs. Future directions underline the development of style-agnostic models, cross-lingual detection frameworks, and robust policies with a view to mitigating LLM-driven misinformation. This synthesis thus lays a concrete foundation for those researchers and practitioners committed to reinforcing fake news detection systems with complications that keep on growing in the digital landscape. © 2025, CC BY.

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Inference-Aware State Reconstruction for Industrial Metaverse under Synchronous/Asynchronous Short-Packet Transmission

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IEEE Transactions on Communications 2025年

作者： Xiong, Qinqin Cao, Jie Zhu, Xu Jiang, Yufei Pappas, Nikolaos Harbin Institute of Technology School of Electronic and Information Engineering Shenzhen518055 China The Shenzhen Key Laboratory of Wireless AloT Communications Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen Guangdong China Linkoping University Department of Computer and Information Science Linköping58183 Sweden

We consider a real-time state reconstruction system for industrial metaverse. The time-varying physical process states in real space are captured by multiple sensors via wireless links, and then reconstructed in virtual space. In this paper, we use the spatial-temporal correlation of the sensor data of interest to infer the real-time data of the target sensor to reduce the mean squared error (MSE) of reconstruction for industrial metaverse under short-packet transmission (SPT). Both synchronous and asynchronous transmission modes for multiple sensors are considered. It is proved that the average reconstruction MSE and average block error probability (BLEP) have a positive correlation under inference with synchronous transmission scheme, whereas they have a negative correlation under inference with asynchronous transmission scheme in certain conditions. Additionally, the average reconstruction MSE decreases monotonically with the mean squared spatial correlation (MSSC), which characterizes the global spatial correlation level. With a high BLEP or long transmission period, even under weak MSSC, the inference scheme still significantly reduces the average reconstruction MSE compared to the no inference case. Moreover, closed-form MSSC thresholds are derived for the superiority regions of the inference with synchronous transmission and inference with asynchronous transmission schemes, respectively. Adaptations of blocklength and time shift of asynchronous transmission are conducted to minimize the average reconstruction MSE. Simulation results show that the two inference schemes outperform the no inference case, with an average MSE reduction of more than 50%. © 1972-2012 IEEE.

关键词： Packet networks

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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： B Shoba T Raghunathan S.Sam Peter Karthick K Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tirunelveli Department of Artificial Intelligence and Data Science Dr.NG.p.Institute of Technology Coimbatore Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore Department of Information Technology Sona College of Technology Salem Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles.

关键词： Adaptation models Pedestrians Heuristic algorithms Feature extraction Real-time systems Vehicle dynamics Collision avoidance Autonomous vehicles Optimization Logistics

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Joint Precoding and Reflection Optimization for IRS-Assisted MU-MIMO-OTFS Downlink Systems

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IEEE Transactions on Vehicular Technology 2025年

作者： Zou, Tingting Wang, Fengyu Xu, Wenjun Zhang, Yimeng Pan, Miao Han, Zhu Beijing University of Posts and Telecommunications State Key Laboratory of Network and Switching Technology Beijing100876 China Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing100876 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Mobile Research Institute Beijing100876 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Orthogonal time frequency space (OTFS) modulation has been proposed as a promising technique to achieve high-reliability communications in high-mobility scenarios. In this paper, we consider a multi-user (MU) multiple-input multiple-output (MIMO)-OTFS downlink communication scenario, where an intelligent reflecting surface (IRS) is deployed to enhance the performance. Specifically, the delay- Doppler (DD) domain input-output relationship of such a system is first derived, based on which, the joint optimization of the precoding matrices and IRS reflection phase is studied to maximize the weighted sum rate (WSR) considering user fairness. Note that the corresponding optimization problem is non-convex. To tackle this challenge, we first reformulate the problem into a more traceable equivalent form by the weighted mean minimum square error (WMMSE) approach. Utilizing the characteristics of the DD domain signal relationship, we then decouple the reformulated problem and further transform it into two subproblems, which are solved by the Lagrangian function method and the project-descent-retracting (PDR) algorithm, respectively. The precoding matrices and reflection phase are jointly optimized by alternative optimization to maximize WSR. Simulation results demonstrate that, compared with benchmark schemes, the proposed WMMSE-ALT can improve the rate more than 85% in IRS-assisted MU-MIMO-OTFS systems. © 1967-2012 IEEE.

关键词： Mean square error

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