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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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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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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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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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ResiComp: Loss-Resilient Image Compression via Dual-Functional Masked Visual Token Modeling

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wang, Sixian Dai, Jincheng Qin, Xiaoqi Yang, Ke Niu, Kai Zhang, Ping Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Ministry of Education Beijing100876 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

Recent advancements in neural image codecs (NICs) are of significant compression performance, but limited attention has been paid to their error resilience. These resulting NICs tend to be sensitive to packet losses, which are prevalent in real-time communications. In this paper, we investigate how to elevate the resilience ability of NICs to combat packet losses. We propose ResiComp, a pioneering neural image compression framework with feature-domain packet loss concealment (PLC). Motivated by the inherent consistency between generation and compression, we advocate merging the tasks of entropy modeling and PLC into a unified framework focused on latent space context modeling. To this end, we take inspiration from the impressive generative capabilities of large language models (LLMs), particularly the recent advances of masked visual token modeling (MVTM). In specific, ResiComp develops a bi-directional masked Transformer to model the contextual dependencies among latents with dual-functionality: 1) iteratively acts as a conditional entropy model to boost compression efficiency;2) operates latent PLC to improve resilience. During training, we integrate MVTM to mirror the effects of packet loss, enabling a dual-functional Transformer to restore the masked latents by predicting their missing values and conditional probability mass functions. Our ResiComp jointly optimizes compression efficiency and loss resilience. Moreover, ResiComp provides flexible coding modes, allowing for explicitly adjusting the efficiency-resilience trade-off in response to varying Internet or wireless network conditions. Extensive experiments demonstrate that ResiComp can significantly enhance the NIC's resilience against packet losses, while exhibits a worthy trade-off between compression efficiency and packet loss resilience. Additionally, packet-level simulations, conducted using diverse network models based on real traces, demonstrate that ResiComp exhibits much better robustness to fluct

关键词： Packet loss

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DiffCom: Channel Received Signal is a Natural Condition to Guide Diffusion Posterior Sampling

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IEEE Journal on Selected Areas in Communications 2025年

作者： Wang, Sixian Dai, Jincheng Tan, Kailin Qin, Xiaoqi Niu, Kai Zhang, Ping Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Ministry of Education Beijing100876 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

End-to-end visual communication systems typically optimize a trade-off between channel bandwidth costs and signal-level distortion metrics. However, under challenging physical conditions, this traditional coding and transmission paradigm often results in unrealistic reconstructions with perceptible blurring and aliasing artifacts, despite the inclusion of perceptual or adversarial losses for optimizing. This issue primarily stems from the receiver's limited knowledge about the underlying data manifold and the use of deterministic decoding mechanisms. To address these limitations, this paper introduces DiffCom, a novel end-to-end generative communication paradigm that utilizes off-the-shelf generative priors and probabilistic diffusion models for decoding, thereby improving perceptual quality without heavily relying on bandwidth costs and received signal quality. Unlike traditional systems that rely on deterministic decoders optimized solely for distortion metrics, our DiffCom leverages raw channel-received signal as a fine-grained condition to guide stochastic posterior sampling. Our approach ensures that reconstructions remain on the manifold of real data with a novel confirming constraint, enhancing the robustness and reliability of the generated outcomes. Furthermore, DiffCom incorporates a blind posterior sampling technique to address scenarios with unknown forward transmission characteristics. Extensive experimental validations demonstrate that DiffCom not only produces realistic reconstructions with details faithful to the original data but also achieves superior robustness against diverse wireless transmission degradations. Collectively, these advancements establish DiffCom as a new benchmark in designing generative communication systems that offer enhanced robustness and generalization superiorities. © 1983-2012 IEEE.

关键词： Bandwidth

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Analysis of Visible Light-Based ISAC Channel Characteristics in a V2V Scenario

Analysis of Visible Light-Based ISAC Channel Characteristics...

引用

IEEE Conference on Wireless Communications and networking

作者： Peiyao Dong Pan Tang Yue Yin Lei Tian Jianhua Zhang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, we investigate the visible light-based integrated sensing and communication (ISAC) channel characteristics in a vehicle-to-vehicle (V2V) scenario, focusing on monostatic sensing and bi-static sensing modes. The ISAC channel is divided into a target channel and a background channel. The channel impulse response (CIR) at different distances, as well as the fading characteristics, multipath correlation, and delay spread of the channel within the effective sensing distance in the two sensing modes are investigated by ray tracing simulation. The results show that with the increase of distance, the target channel received power ratio of mono-static sensing decreases by 30%, while the received power ratio of bi-static sensing is stable at about 1.06%, the target channel fading of mono-static sensing is faster than that of bi-static sensing. In addition, the linear fit coefficient of the root mean square (RMS) delay spread (DS) values for mono-static sensing is positive with a mean value of 2.95 which is greater than the mean value of 0.32 for the RMS DS values for bi-static sensing, while the linear fit coefficient of the RMS DS values for bi-static sensing is negative. Overall, the bi-static sensing system outperforms at longer distances, has a more stable target signal received power ratio, better handling target signals and background noise, reducing signal RMS DS, and improving transmission quality and anti-interference capabilities, indicating the advantages of bi-static sensing in V2V scenarios using visible light-based ISAC.

关键词： Fading channels Correlation Ray tracing Integrated sensing and communication Reflection Delays Background noise Root mean square Channel impulse response Visible light communication

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A zero-fault sample diesel engine early warning and localisation method based on multi-cylinder similarity and memory augmentation

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Expert Systems with Applications 2025年 284卷

作者： Mo, Hangfeng Ji, Shude Chen, Shenxiao Wang, Zijia Jiang, Zhinong Mao, Zhiwei Key Laboratory of Engine Health Monitoring-Control and Networking Ministry of Education Beijing University of Chemical Technology Beijing100029 China State Key Laboratory of High-end Compressor and System Technology Beijing University of Chemical Technology Beijing100029 China Tianjin300400 China State Key Laboratory of Tribology in Advanced Equipment Tsinghua University Beijing10084 China

Diesel engines often present challenges in achieving efficient warning and accurate localisation during actual operation due to complex working conditions and the lack of fault samples. To address this issue, this paper proposes a zero-fault sample anomaly warning and localisation method for diesel engines, which is based on multi-cylinder similarity and memory augmented autoencoder. This approach allows for model training without the use of fault samples, a method that has been scarcely explored in industrial research concerning diesel engine fault diagnosis using only normal samples. The method leverages the similarities in operating states among multiple cylinders and constructs a joint similarity matrix by integrating waveform similarity and impact energy similarity. This approach captures the intrinsic correlation characteristics among the cylinders and effectively mitigates the influence of changes in operating conditions on diagnostic performance. Additionally, a memory augmentation autoencoder is employed to model normal samples. By amplifying the reconstruction error of abnormal data, the method facilitates fault warning and localisation even in the absence of fault samples. Experimental results demonstrate that the proposed method can successfully achieve early fault warning and localisation under both single and cross-operating conditions without requiring fault samples, thereby providing an effective solution for early fault detection and intelligent maintenance of diesel engines. © 2025 Elsevier Ltd

关键词： Industrial research

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

引用

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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Dynamic Data Collection for UAV-Assisted Green Industrial IoT

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IEEE Internet of Things Journal 2025年

作者： Lu, Jiarong Wang, Ying Zhao, Junwei Wu, Wen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China North Automatic Control Technology Institute China Pengcheng Laboratory Frontier Research Center Shenzhen518055 China

Unmanned aerial vehicles (UAVs) can collect data from industrial Internet of Things (IoT) devices that experience poor channel conditions caused by the obstruction of large industrial equipment. However, due to the mobility of UAVs and stochastic industrial data generation, extreme events with significantly high latency may occur during data collection, resulting in unreliable communication. Besides, UAV speed variation brings challenges to achieving green communication and reliable data collection. In this paper, we propose a dynamic UAV-assisted resource allocation scheme to collect data reliably for green industrial IoT. Specifically, the queue tail distribution is adopted to characterize the occurrence probability of extreme events, which indicates the reliability of the queue length. Then, given the impact of UAV speed on energy consumption and queue reliability, we aim to minimize energy consumption constrained by tail distribution and optimize UAV speed to ensure reliable data collection. Furthermore, the device access, bandwidth allocation, power control, and UAV speed are jointly optimized for minimizing the long-term energy consumption of UAVs and industrial IoT devices, constrained by the tail distribution of the queue length. The formulated problem is intractable due to intricately coupled variables and stochastic characteristics. To resolve it, we propose a novel algorithm, namely JDBPS, which can achieve reliable data collection and green communication. Simulation results demonstrate that the proposed JDBPS algorithm can constrain tail distribution while reducing transmit power of industrial IoT devices by 15.7% compared with the fixed UAV speed scheme. © 2014 IEEE.

关键词： Energy utilization

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