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From Earth to Orbit: Launch Sequence Optimization for LEO Mega-Constellations

IEEE Transactions on Mobile Computing 2025年

作者： Huang, Tianze Li, Qing Xu, Chenren Xu, Mengwei Wang, Shangguang Huang, Gang Liu, Xuanzhe Peking University School of Computer Science Beijing China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

The recent emergence of Low Earth Orbit (LEO) mega-constellations, designed for high-speed broadband connections with low latency, has introduced new deployment challenges. Efficient launch sequence planning is crucial for rapid service rollout, performance enhancement, and service promotion. However, existing research predominantly focuses on the design and performance analysis of fully-deployed constellations and overlooks the evolving process from a partially-deployed constellation to a fully-deployed one. This paper explores the launch sequence optimization problem for mega-constellations, tailored to expedite service delivery and adapt to changing performance demands. To this end, (1) we identify critical network performance metrics for the constellation evolving process and construct a simulation toolchain capable of simulating and evaluating these metrics for any potential partially-deployed constellation. (2) Drawing upon three key observations on network availability, the number of visible satellites, and latency, we propose an algorithm that can construct a launch sequence for an arbitrary mega-constellation topology. Evaluation results show that this algorithm enables the early provision of services and maximizes network performance gains at each launch batch while catering to different user demands. For instance, our algorithm can achieve network performance nearly equivalent to that of Starlink when it initiated its service, without losing redundancy, while using 55% fewer satellites. © 2002-2012 IEEE.

关键词： Satellite communication systems

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Learnable Residual-Based Latent Denoising in Semantic Communication

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IEEE Wireless Communications Letters 2025年第5期14卷 1376-1380页

作者： Xu, Mingkai Wu, Yongpeng Shi, Yuxuan Xia, Xiang-Gen Zhang, Wenjun Zhang, Ping Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Shanghai Jiao Tong University School of Cyber and Engineering Shanghai200240 China University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology 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. © 2012 IEEE.

关键词： Noise reduction Semantics Transformers Signal to noise ratio Iterative methods Iterative decoding Decoding Training Noise measurement Merging

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Redactable Blockchain Supporting Rewriting Authorization Without Trapdoor Exposure

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Wei Duan, Junke Wang, Licheng Peng, Haipeng Zhu, Liehuang Li, Lixiang Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China

Blockchain technology, known for its decentralization and immutability, has been widely applied across various domains. However, this immutability reveals limitations in adapting to rapidly changing legal environments and preventing malicious misuse. To introduce a degree of flexibility, various transaction-level redactable blockchain solutions have been proposed. Yet, current schemes grant modifiers redaction privileges by providing access to the trapdoor, potentially posing risks of malicious dissemination and abuse of the trapdoor. In this paper, we first propose an RSA-based threshold chameleon hash (TCH) construction, allowing the distribution of the trapdoor among a group of authorities. Building on TCH, we develop a threshold policy-based chameleon hash (TPCH). Compared to the Policy-Based Chameleon Hash (PCH) proposed by Derler at NDSS'19, our TPCH supports authorization without exposing the trapdoor. Furthermore, leveraging TPCH, we introduce a novel transaction-level redactable blockchain (TPRB). TPRB supports decentralized authorization without trapdoor exposure and fine-grained rewriting control. Finally, through implementation and evaluation, we demonstrate the practicality and efficiency of our TCH and TPCH schemes. © 2004-2012 IEEE.

关键词： Decentralized control

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Prospect of C-V2X-Based Intelligent Road Side System

Prospect of C-V2X-Based Intelligent Road Side System

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Zhang, Jie Gao, Tian Wu, Tilong Bi, Haizhou Shi, Yan Chen, Shanzhi State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China CICT Connected and Intelligent Technologies Company Ltd. Chongqing400039 China School of Software and Microelectronics Peking University Beijing100871 China Beijing100191 China

ISBN: (纸本)9789819639601

This article offers an in-depth outlook on the development of C-V2X-based Intelligent Roadside Systems (IRSS), elucidating the applications and advantages of C-V2X in IRSS. It underscores how C-V2X enhances active safety by providing real-time traffic information, optimizes traffic flow through dynamic management, enriches information services for drivers, and facilitates autonomous driving by offering low-latency, high-reliability communication. The article examines the functional requirements of IRSS, including all-weather, full-element perception and highly adaptive local dynamic traffic organization, which are essential for the system’s robust performance. The system architecture of C-IRSS is designed to leverage the capabilities of C-V2X, which includes two communication modes: Uu for vehicle-to-cloud communication and PC5 for vehicle-to-vehicle and vehicle-to-infrastructure communication. These modes enable the exchange of critical data between vehicles, infrastructure, and cloud platforms, ensuring a comprehensive and coordinated approach to traffic management. The principles of tiered deployment for C-IRSS are also explored, with recommendations for prioritizing intersections with traffic signals, traffic guidance, or traffic control facilities in urban areas, and key nodes such as interchanges and service areas on highways. The article discusses the challenges faced in the large-scale deployment of C-IRSS, such as the need for algorithm generalization, the complexity of engineering deployment, and ensuring data quality and privacy. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Traffic signals

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Decoupled Mean Filtering for Calibrated MUSIC Spectrum with Phase-Rotation Precoding under Cooperative ISAC Systems – From Interference Suppression Perspective

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IEEE Transactions on Signal Processing 2025年

作者： Liu, Xiqing Li, Jingwen Gong, Jialong Yin, Meiyu Liu, Yuanwei Peng, Mugen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong

Integrated sensing and communication (ISAC) represents a critical scenario in the sixth-generation (6G) mobile communication, requiring systems to deliver both excellent communication and high-accuracy sensing capabilities. The requirement for high-accuracy sensing presents a major concern in scenarios with a single ISAC base station (BS). While a multi-BS collaborative ISAC system offers substantial improvements in sensing accuracy, mutual interference among BSs introduces a critical issue for implementation. In this work, we model the mutual interference in the process of multi-BS collaborative sensing and investigate the impact of these interferences on sensing accuracy across different modulation schemes. Based on this, we present a decoupled mean filtering (DMF) algorithm to mitigate interference, which performs effectively at low signal-to-interference-plus-noise ratios (SINRs) but shows limited effectiveness at high SINRs. Consequently, we propose an enhanced DMF integrated with the phase rotation precoding (PRP) tailored to various modulation types. The simulation results demonstrate that the proposed DMF-PRP algorithm effectively suppresses the sensing mutual interference and exhibits superior sensing accuracy compared to the existing schemes. © 1991-2012 IEEE.

关键词： Wiener filtering

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Advancing End-to-End Programmable Networks: Exploring the Interplay of Generative AI with Opportunities and Challenges

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IEEE Network 2025年

作者： He, Wenji Yao, Haipeng Ren, Xiaoxu Liu, Yuanling Xiong, Zehui Niyato, Dusit Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Singapore University of Technology and Design Singapore Nanyang Technological University Singapore

The rapid proliferation of Internet of Things (IoT) devices and the continuous evolution of network communications are reshaping the global network landscape, demanding advanced management techniques to address the growing complexity of data and service requirements. Generative Artificial Intelligence (GAI) has emerged as a transformative solution, enabling multi-modal data analytics and decision-making. However, traditional network architectures lack the scalability and flexibility required to accommodate the diverse resources and compute-intensive demands of GAI. To address these challenges, we propose a novel end-to-end programmable network framework that provides scalable, flexible support for enhanced storage, computation, and transmission capabilities. This architecture empowers compute-intensive and data-intensive applications by leveraging specialized GAI models designed for network optimization, dynamically enhancing resource allocation and operational efficiency. Furthermore, we implement a GAI-assisted framework to improve DDoS detection within programmable networks. By using diffusion models for synthetic data generation, the framework addresses data scarcity in privacy-constrained environments and simplifies GAI model deployment through knowledge distillation. Simulation results validate the proposed framework’s effectiveness, demonstrating significant improvements in detection latency and detection accuracy. © 2025 IEEE. All rights reserved.

关键词： Generative adversarial networks

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Trusted access control mechanism for data with blockchain-assisted attribute encryption

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High-Confidence Computing 2025年第2期5卷

作者： Liu, Chang Wang, Dong Li, Da Guo, Shaoyong Li, Wenjing Qiu, Xuesong State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China State Grid Digital Technology Holdings Co. Ltd Beijing100053 China

In the growing demand for data sharing, how to realize fine-grained trusted access control of shared data and protect data security has become a difficult problem. Ciphertext policy attribute-based encryption (CP-ABE) model is widely used in cloud data sharing scenarios, but there are problems such as privacy leakage of access policy, irrevocability of user or attribute, key escrow, and trust bottleneck. Therefore, we propose a blockchain-assisted CP-ABE (B-CP-ABE) mechanism for trusted data access control. Firstly, we construct a data trusted access control architecture based on the B-CP-ABE, which realizes the automated execution of access policies through smart contracts and guarantees the trusted access process through blockchain. Then, we define the B-CP-ABE scheme, which has the functions of policy partial hidden, attribute revocation, and anti-key escrow. The B-CP-ABE scheme utilizes Bloom filter to hide the mapping relationship of sensitive attributes in the access structure, realizes flexible revocation and recovery of users and attributes by re-encryption algorithm, and solves the key escrow problem by joint authorization of data owners and attribute authority. Finally, we demonstrate the usability of the B-CP-ABE scheme by performing security analysis and performance analysis. © 2024 The Author(s)

关键词： Ciphertext

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Adaptive Cross-Modal Super-Resolution Semantic Communication for Mobile AI-Generated Panoramic Video

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IEEE Transactions on Cognitive Communications and networking 2025年

作者： Gao, Haixiao Sun, Mengying Xu, Xiaodong Cheng, Xiqi Han, Shujun Zhang, Ping Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Peng Cheng Laboratory Department of Broadband Communication Guangdong Shenzhen518066 China

The development of artificial intelligence-generated panoramic video (AIGPV) provides a potential solution for the diverse demands of users in immersive communication scenarios of 6G. However, ensuring users’ quality of experience (QoE) under resource-constrained conditions remains a significant challenge. In this paper, we introduce a cross-modal semantic communication transmission scheme for mobile-AIGPV. This scheme generates low-resolution panoramic videos on the edge server and transmits them via cross-modal semantic communication. Furthermore, to better ensure users’ QoE, we propose a panoramic video super-resolution transmission framework based on deep joint source-channel coding (JSCC), named PVSR-JSCC. This framework leverages neural networks (NNs) and attention mechanisms to achieve semantic extraction and adaptive variable-length coding, and performs feature cyclic enhancement based on the motion and context, thereby achieving the panoramic video super-resolution task. Additionally, we design a cross-modal swin transformer block (CMSTB) to enhance the deep JSCC performance. The CMSTB integrates self-attention and cross-attention mechanisms, enabling deep JSCC to capture the intrinsic semantic correlation between text and video. Simulation results demonstrate that, compared to the semantic communication scheme directly transmitting high-resolution videos, our proposed PVSR-JSCC not only reduces bandwidth consumption by 36.7% but also lowers computational complexity of the semantic model by 42.8%. Moreover, it effectively prevents the "cliff effect" caused by the decrease of signal-to-noise ratio (SNR) in traditional communication systems. © 2015 IEEE.

关键词： Channel coding

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Community-oriented Duplex Privacy Amplification and Active Poisoning Resistance for Heterogeneous Federated Learning

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhou, Zan Zhao, Jun Yang, Shujie Li, Hongjing Ma, Tengchao Xu, Changqiao Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University School of Computer Science and Engineering China

Privacy protection and poisoning resilience are important concerns for federated learning (FL). During a relatively long period, the corresponding solutions are regarded as orthogonal and investigated separately. Unfortunately, due to the increasingly complex structure and ever-growing parameter dimensions of the models to be trained, the forthright coupling of existing differential privacy and Byzantine resilience techniques has been proved incompatible with FL. This emerging problem prompts us to give serious thought to jointly guaranteeing data privacy and model integrity. Besides, worse still, the multi-task characteristic and data imbalance of heterogeneous FL inevitably introduce huge variances, which make privacy-preserving under acceptable accuracy loss even more complicated, not to mention efficient and agile poisoning resistance. Against this bothersome situation, we propose a community-oriented secure heterogeneous FL (CoS-HFL) framework to provide guaranteed privacy protection and significant model robustness simultaneously. CoS-HFL includes two parts: community-oriented duplex privacy amplification and credit-based poisoning resistance. The former copes with potential leakage threats with both uplink and downlink obfuscations. The latter further actively thwarts poisoning attacks based on credibility evaluation. Furthermore, we conduct experiments on benchmark datasets to highlight the performance of CoS-HFL in terms of privacy amplification, poisoning resistance, and learning accuracy under adversarial and heterogeneous environments. © 2004-2012 IEEE.

关键词： Federated learning

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Graph Attentional Based Agglomerative Cluster for UAV Swarm Networks

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IEEE Transactions on Network Science and Engineering 2025年

作者： Huang, Shan Yao, Haipeng Wang, Xiaoman Mai, Tianle Wang, Zunliang Guo, Song Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Hong Kong Polytechnic University Department of Computing Hong Kong

In recent years, UAVs have been widely employed in the construction of post-disaster emergency communication networks due to their flexible mobility and self-organizing capabilities, enabling them to quickly and spontaneously establish communication links. Post-disaster rescue efforts, restoring connectivity and communication, as well as providing disaster information, all require strong support from network applications. However, an essential element in the development of effective UAV swarm-based applications is the network system. Compared to the fixed networks, UAV swarm networks present unique challenges in design and implementation due to its characteristics of high mobility nodes, unstable links, and dynamic topology. Recently, clustering technology has gained recognition as an effective approach to constructing stable UAV swarm networks. In this paper, for UAV swarm networks, we propose a graph attention-based agglomerative clustering algorithm. This algorithm allows UAV nodes to learn similarity relationships through a graph attention network. By considering the mobility similarity between UAV nodes, each UAV node can merge with its adjacent nodes. Furthermore, we also design a cluster head selection algorithm based on mixed strategy games. The algorithm's effectiveness and accuracy were demonstrated by simulation results, which showed a 80.04% increase in network lifetime compared to baseline algorithms. © 2013 IEEE.

关键词： Disasters

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