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arXiv 2022年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Jiujiu Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

Deep learning enabled semantic communication has been studied to improve communication efficiency while guaranteeing intelligent task performance. Different from conventional communications systems, the resource allocation in semantic communications no longer just pursues the bit transmission rate, but focuses on how to better compress and transmit semantic to complete subsequent intelligent tasks. This paper aims to appropriately allocate the bandwidth and power for artificial intelligence (AI) task-oriented semantic communication and proposes a joint compressiom ratio and resource allocation (CRRA) algorithm. We first analyze the relationship between the AI task’s performance and the semantic information. Then, to optimize the AI task’s perfomance under resource constraints, a bandwidth and power allocation problem is formulated. The problem is first separated into two subproblems due to the non-convexity. The first subproblem is a compression ratio optimization problem with a given resource allocation scheme, which is solved by a enumeration algorithm. The second subproblem is to find the optimal resource allocation scheme, which is transformed into a convex problem by successive convex approximation method, and solved by a convex optimization method. The optimal semantic compression ratio and resource allocation scheme are obtained by iteratively solving these two subproblems. Simulation results show that the proposed algorithm can efficiently improve the AI task’s performance by up to 30% comprared with baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Bandwidth

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Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification

arXiv

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arXiv 2021年

作者： Hu, Zheng Zhu, Chuang He, Gang Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

Unsupervised person re-identification (Re-ID) is a promising and very challenging research problem in computer vision. Learning robust and discriminative features with unlabeled data is of central importance to Re-ID. Recently, more attention has been paid to unsupervised ReID algorithms based on clustered pseudo-label. However, the previous approaches did not fully exploit information of hard samples, simply using cluster centroid or all instances for contrastive learning. In this paper, we propose a Hard-sample Guided Hybrid Contrast Learning (HHCL) approach combining cluster-level loss with instance-level loss for unsupervised person Re-ID. Our approach applies cluster centroid contrastive loss to ensure that the network is updated in a more stable way. Meanwhile, introduction of a hard instance contrastive loss further mines the discriminative information. Extensive experiments on two popular large-scale Re-ID benchmarks demonstrate that our HHCL outperforms previous state-of-the-art methods and significantly improves the performance of unsupervised person Re-ID. The code of our work and dataset are available soon at https://***/bupt-ai-cz/HHCL-ReID. Copyright © 2021, The Authors. All rights reserved.

关键词： Benchmarking

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Adaptable Semantic Compression and Resource Allocation for Task-Oriented Communications

arXiv

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arXiv 2022年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Jiang, Nan The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

Task-oriented communication is a new paradigm that aims at providing efficient connectivity for accomplishing intelligent tasks rather than the reception of every transmitted bit. In this paper, a deep learning-based task-oriented communication architecture is proposed where the user extracts, compresses and transmits semantics in an end-to-end (E2E) manner. Furthermore, an approach is proposed to compress the semantics according to their importance relevant to the task, namely, adaptable semantic compression (ASC). Assuming a delay-intolerant system, supporting multiple users indicates a problem that executing with the higher compression ratio requires fewer channel resources but leads to the distortion of semantics, while executing with the lower compression ratio requires more channel resources and thus may lead to a transmission failure due to delay constraint. To solve the problem, both compression ratio and resource allocation are optimized for the task-oriented communication system to maximize the success probability of tasks. Specifically, due to the nonconvexity of the problem, we propose a compression ratio and resource allocation (CRRA) algorithm by separating the problem into two subproblems and solving iteratively to obtain the convergent solution. Furthermore, considering the scenarios where users have various service levels, a compression ratio, resource allocation, and user selection (CRRAUS) algorithm is proposed to deal with the problem. In CRRAUS, users are adaptively selected to complete the corresponding intelligent tasks based on branch and bound method at the expense of higher algorithm complexity compared with CRRA. Simulation results show that the proposed ASC approach can reduce the size of transmitted data by up to 80% without reducing task success, and the proposed CRRA and CRRAUS algorithms can obtain at least 15% and 10% success gains over baseline algorithms, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Resource allocation

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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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Time Error Compensation with Neural network for Time Synchronization in TSN

Time Error Compensation with Neural Network for Time Synchro...

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International Conference on Computer and Communications (ICCC)

作者： Haoze Yu Guochu Shou Xiaodong Zhang Ledong Hu Yaqiong Liu Yihong Hu Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665409513

Time-sensitive networking featured with precise time synchronization, is significant in modern industrial systems for real-time guarantee. However, some industrial scenarios, such as smart grids and 5G fronthaul networks, have much higher requirements on time synchronization. This paper proposes a method to improve the synchronization performance of TSN with a neural network model. A time error compensation algorithm including timestamp data processing, neural network training, and error compensation is designed. Comparative experiment results show that this algorithm improves the stability and accuracy of time synchronization.

关键词： Training Time-frequency analysis Neural networks Error compensation Power system stability Stability analysis Real-time systems

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Multi-domain Time Synchronization Model and Performance Evaluation in TSN

Multi-domain Time Synchronization Model and Performance Eval...

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International Conference on Computer and Communications (ICCC)

作者： Ledong Hu Guochu Shou Xiaodong Zhang Yaqiong Liu Yihong Hu Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665409513

Time-Sensitive networking (TSN) has precise time synchronization capabilities and can provide reliable time-deterministic services for industrial communications and other fields. IEEE 802.1AS is used as the time synchronization protocol in TSN and has support for multiple gPTP domains, which further expands the application range of TSN and enhances the synchronization capabilities of TSN. Industrial automation and other use cases also have multi-domain time synchronization requirements of TSN, but there is still a lack of research in this field. Therefore, this paper studies the multi-domain synchronization mechanism in TSN and proposes the multi-domain time synchronization model according to actual application requirements. Based on the multi-domain model, this paper analyzes the time errors in multi-domain time synchronization, introduces the SDN paradigm, and proposes a software-defined time compensation method to compensate the time errors centrally. Further, the time synchronization experiment environment is built to analyze synchronization performance. The results verify the effectiveness of the multi-domain time synchronization model, demonstrating that the multi-domain TSN network can provide high-precision time synchronization services.

关键词： Performance evaluation Analytical models Automation Protocols Industrial communication Synchronization Reliability

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Time-Aware Traffic Scheduling with Virtual Queues in Time-Sensitive networking

Time-Aware Traffic Scheduling with Virtual Queues in Time-Se...

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IFIP/IEEE International Symposium on Integrated network Management

作者： Junli Xue Guochu Shou Yaqiong Liu Yihong Hu Zhigang Guo Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Time-Sensitive networking is a promising technology that supports deterministic services with bounded delay, low packet delay variation, and low packet loss. Time-Sensitive networking consists of several components, in which traffic scheduling plays an important role to provide the delay guarantee. The traffic scheduling on the level of flow is challenging for dense traffic demands. In this paper, we propose a time-aware traffic scheduling scheme based on network virtualization to schedule a large number of flows. In the scheme, the physical queues of a network node are abstracted as virtual queues, which isolate each flow with a time offset parameter. A scheduling algorithm is designed into two stages of virtual queues mapping and schedule computation. The simulation results in a use case of industrial automation show the effectiveness of the proposed scheme.

关键词： Schedules Job shop scheduling Automation Scheduling algorithms Simulation Packet loss Delays

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Joint Latency and Energy Consumption Optimization with Deep Reinforcement Learning for Proximity Detection in Road networks

Joint Latency and Energy Consumption Optimization with Deep ...

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International Conference on Computer and Communications (ICCC)

作者： Tongyu Zhao Yaqiong Liu Guochu Shou Yihong Hu Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665409513

Artificial intelligence and the automobile business have grown significantly in recent years, and autonomous driving has progressively been the industry's focus. The problem of proximity detection in road networks refers to determining if two moving objects are close to one other in real time. However, in the real world, mobile devices' battery life and computing capabilities are restricted, resulting in excessive latency and energy usage. As a result, determining the proximity relationship between mobile users with low latency and energy usage is a difficult task. We formalize the joint latency and energy consumption optimization problem for proximity detection in road networks into a constrained optimization problem (COP) and solve it using a deep Q network (DQN). The simulation results show that DQN can ensure the desired sum cost under a variety of important factors, effectively reducing latency and energy usage.

关键词： Energy consumption Roads Computational modeling Simulation Reinforcement learning Real-time systems Mobile handsets

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An Offloading Scheme Leveraging on Neighboring Node Resources for Edge Computing over Fiber-Wireless (FiWi) Access networks

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China Communications 2019年第11期16卷 107-119页

作者： Wei Chang Yihong Hu Guochu Shou Yaqiong Liu Zhigang Guo Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications

The computation resources at a single node in Edge Computing(EC)are commonly limited,which cannot execute large scale computation *** face the challenge,an Offloading scheme leveraging on NEighboring node Resources(ONER)for EC over Fiber-Wireless(FiWi)access networks is proposed in this *** the ONER scheme,the FiWi network connects edge computing nodes with fiber and converges wireless and fiber connections seamlessly,so that it can support the offloading transmission with low delay and wide *** on the ONER scheme supported by FiWi networks,computation tasks can be offloaded to edge computing nodes in a wider range of area without increasing wireless hops(e.g.,just one wireless hop),which achieves low ***,an efficient Computation Resource Scheduling(CRS)algorithm based on the ONER scheme is also proposed to make offloading *** results show that more offloading requests can be satisfied and the average completion time of computation tasks decreases significantly with the ONER scheme and the CRS ***,the ONER scheme and the CRS algorithm can schedule computation resources at neighboring edge computing nodes for offloading to meet the challenge of large scale computation tasks.

关键词： edge computing offloading Fiber-wireless access networks delay

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Dual path nets with a new multi-scale backbone architecture for pulmonary nodule detection

Dual path nets with a new multi-scale backbone architecture ...

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2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020

作者： Maozhi, Gong Muqing, Wu Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Acknowledgment Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing China

ISBN: (纸本)9781728152240

This article mainly completed the detection of lung nodules using deep neural network and built a complete end-to-end detection system. U-net structure has significant advantages in image segmentation. We propose a dual-path network with a U-shaped structure that combines the advantages of residual learning and intensive learning for the detection of lung nodules. And on this basis, the residual module of Resnet was modified. The feature image fed into the residual module is divided into multiple blocks for convolution. Smaller residual blocks are added to each part to broaden the receptive field of each layer. So, it has the ability of extracting multi-scale feature. This network structure is called Res2net. The processed feature images are fused. We also added the SE module to focus on the relationship between feature information channels. It can strengthen the role of useful channels and suppress the output of useless channels. These changes significantly improve the detection effect of lung nodules. We used the position regression of lung nodules to return the predicted position of lung nodules and achieved good results. The detection indicator FROC is significantly better than other detection projects. © 2020 IEEE.

关键词： Image segmentation

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