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32nd International Joint Conference on Artificial Intelligence (IJCAI)

作者： Wu, Jinming Qi, Qi Wang, Jingyu Sun, Haifeng Wu, Zhikang Zhuang, Zirui Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781956792034

Recent graph-based methods achieve significant success in multivariate time series modeling and forecasting due to their ability to handle relationships among time series variables. However, only pairwise relationships are considered in most existing works. They ignore beyond-pairwise relationships and their potential categories in practical scenarios, which leads to incomprehensive relationship learning for multivariate time series forecasting. In this paper, we present ReMo, a Relational Modeling-based method, to promote fine-grained relational learning among multivariate time series data. Firstly, by treating time series variables and complex relationships as nodes and hyperedges, we extract multi-view hypergraphs from data to capture beyond-pairwise relationships. Secondly, a novel hypergraph message passing strategy is designed to characterize both nodes and hyperedges by inferring the potential categories of relationships and further distinguishing their impacts on time series variables. By integrating these two modules into the time series forecasting framework, ReMo effectively improves the performance of multivariate time series forecasting. The experimental results on seven commonly used datasets from different domains demonstrate the superiority of our model.

关键词： Time series

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Near-term quantum algorithm for solving the MaxCut problem with fewer resources

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2024年 648卷

作者： Zhao, Xiumei Li, Yongmei Li, Jing Wang, Shasha Wang, Song Qin, Sujuan Gao, Fei Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Comprehens Res Ctr Elect Informat Technol MIIT Weihai 264200 Peoples R China

MaxCut is an NP-hard combinatorial optimization problem in graph theory. The quantum approximate optimization algorithms (QAOAs) offer new methods for solving such problems, which are potentially better than classical schemes. However, the requirement of N qubits for solving graphs with N-vertex in QAOA, coupled with the large-N N trainability issue due to barren plateaus, poses a substantial challenge for noisy intermediate-scale quantum (NISQ) devices. Recently, a hybrid quantum-classical algorithm has been proposed for solving semidefinite programs (SDPs), named NISQ SDP Solver (NSS). In this paper, we study the performance of the NSS for solving MaxCut problem via the introduction of a near-term quantum algorithm and execution of experimental simulations. Since the MaxCut problem admits a relaxation into an SDP formulation, the SDP can be solved using NSS, with a subsequent classical post-processing step converting the hybrid density matrix into a MaxCut solution. After performing these steps, the near-term quantum algorithm will also inherit the advantages of NSS. We analyze the algorithm as compared to QAOAs and find that the depth of the quantum circuit is independent of the number of edges on the graph. Our algorithm requires logN N qubits and has O (1) circuit depth. This implies that it uses exponentially fewer qubits compared to QAOAs while also requiring a significantly reduced circuit depth to solve the MaxCut problem. To demonstrate the effectiveness of the NSS, we focused on 3-regular graphs, 9-regular graphs, and ER graphs. Numerical results indicate that the quantum algorithm achieves a comparable approximation ratio with classical methods by solving the original high dimensional problem within a lower dimensional ansatz space. Analysis under various initial states shows that the algorithm exhibits a remarkably stable performance.

关键词： Quantum computing Near-term quantum algorithms Semidefinite programs MaxCut problem

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Joint optimization of task scheduling and computing resource allocation for VR video services in 5G-advanced networks

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2024年第1期35卷

作者： Huang, Zhenglei Du, Yupeng Yang, Shujie Xiao, Han Wang, Dan Sun, Tao China Mobile Res Inst Dept Basic Network Technol Res Beijing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

With the development of 5G networks and related hardware equipment, the demand for various VR video services including panoramic video live broadcast and immersive game is increasing day by day. Due to the explosive growth of data volume and the trend toward lightweight VR equipment, traditional local computing architecture faces problems of insufficient computing power and high cost. As an emerging computing paradigm, the remote computing architecture of cloud-edge collaboration frees users from cumbersome local restrictions. However, edge computing resources are often limited, and the remote cloud has inconsistent network latency. Therefore, how to adaptively schedule and allocate computing resources is the key to determining VR service quality. Based on an in-depth analysis of the characteristics of VR video services and network environments, this article first builds a cloud-edge-network coorperation computing and transmission architecture for VR video services. This architecture can adaptively offload concurrent computing tasks and allocate computing power to them in VR scenes based on network conditions provided by the 5G-Advanced networks via network information exposure interface. This paper proposes a general optimization problem to maximize user experience. And a single-slot delay optimization model was constructed. Combining convex optimization theory and deep learning algorithms, this paper designs a task scheduling and computing power allocation strategy based on deep reinforcement learning. The policy gradually learns optimal task scheduling and resource allocation decisions to maximize delay-related rewards in the long run. This article further introduces the graph neural network under the attention mechanism to solve the problem of changes in the number of concurrent tasks. Finally, through Cloudsim simulation, this article verifies the superiority of task scheduling and computing power allocation strategies based on deep reinforcement learning compa

关键词： Computing power

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Channel Characterization and Comparison in Industrial Scenario from Sub-6 GHz to Visible Light Bands for 6G

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PHOTONICS 2025年第3期12卷

作者： Yin, Yue Tang, Pan Zhang, Jianhua Hu, Zheng Jiang, Tao Xia, Liang Liu, Guangyi Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China China Mobile Res Inst Beijing 100053 Peoples R China

The industrial scenario is indispensable for ubiquitous 6G coverage, which demands hyper-reliable and low-latency communication for full automation, control, and operation. To meet these demands, it is widely believed that it is necessary to introduce not only the conventional sub-6 GHz bands but also high-frequency technologies, such as millimeter wave (mmWave), terahertz (THz), and visible light bands. In this paper, we conduct a channel characterization and comparison in the industrial scenario from the sub-6 GHz to visible light bands. The channel characteristics, including the path loss (PL), root mean square (RMS) delay spread (DS), and angle spread (AS), were analyzed with respect to the frequency dependence and the distance dependence. On the one hand, the visible light band exhibited significant differences in channel characteristics compared to the electronic wave band. Due to the line-of-sight transmission of VLC, the visible light band had a higher path loss, and the path loss exponent reached 3.84. Due to the Lambertian radiation pattern, which has a wide range of reflection angles, the AS of the visible light band was much larger than that of the electronic wave band, which were 1.73 and 0.80 for the visible light and THz bands, respectively. On the other hand, the blockage effect of the metal instruments in the industrial scenario will greatly affect the channel characteristics. As the transceiver distance grows large, signals from both sides of the receiver will be blocked by metal instruments, resulting in a decreasing trend in the RMS DS for the electronic wave band. Moreover, the statistical characteristics of the channel properties were modeled and compared with the 3GPP TR 38.901 standard. It was found that the height of the receiver caused the difference between the proposed model and the 3GPP model and needs to be taken into account when modeling. Furthermore, we extended the 3GPP model to the THz and VLC bands and provided the statistical par

关键词： channel characteristics comparison industrial scenario VLC mmWave THz sub-6 GHz

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Cooperative Content Caching and Distribution for Satellite CDNs 31

Cooperative Content Caching and Distribution for Satellite C...

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31st IEEE International Conference on Network Protocols (ICNP)

作者： Chen, Yuwen Ma, Xiao Zhou, Ao Wang, Shangguang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9798350303223

Content distribution networks play a crucial role in the services provided by streaming applications, but current terrestrial CDNs have uneven coverage and poor performance in some remote and rural areas. Satellite communications have emerged as an effective way to address these issues. However, the satellite environment is highly dynamic, and how to reasonably deploy CDNs in the satellite constellation to realize efficient content caching and distribution is an urgent problem to be solved. To this end, this paper proposes a content caching and content distribution approach for satellite CDNs. The approach uses Lyapunov optimization, Gibbs sampling, and matching theory to control the traffic cost of satellite-cached content copies while reducing the content distribution latency. A cost-effective content cache deployment and distribution strategy is proposed for large low-orbiting satellite constellations operating at high speeds. Extensive simulation results show that the algorithm can quickly converge to favorable values compared to the reference algorithm, effectively reducing latency for end users while maintaining low deployment traffic costs.

关键词： Low Earth Orbit Constellations Content Distribution Networks Content Caching

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Dual-band dynamic reconfigurable intelligent surface for signal enhancement in millimeter wave

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Science China(Technological Sciences) 2024年第4期67卷 1133-1138页

作者： YAO HuiMing DU XinRun WANG HengBin GAO Song PANG YaChen ZHENG HaiRong GONG HuiWen LI Yi XU JianChun ZHANG JianHua BI Ke State Key Laboratory of Information Photonics and Optical Communications School of ScienceBeijing University of Posts and TelecommunicationsBeijing 100876China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

Millimeter wave with large bandwidth,high transmission rate,and low delay is considered a reliable alternative to cope with the spectrum ***,the fast attenuation and narrow beam characteristics make it difficult to achieve long-distance or wide-range ***,a 1-bit dual-band reflective reconfigurable intelligent surface(RIS)for signal enhancement in millimeter wave with 16×16 elements is designed,fabricated,and *** from most existent RIS,dynamic programming is realized at two separate frequency bands by integrating the PIN diodes and field-programmable gate array(FPGA).Particularly,the beam deflection,dual-beam,and multi-beam are created based on the coding theory and convolution operation,proving the effectiveness of wavefront ***,the far-field patterns and signal power with different coding sequences are measured and *** is indicated that the received signal power is 6–7 dB stronger than that without coding,which shows good agreement with the desired *** proposed reconfigurable metasurface exhibits great potential in beam forming,making it a promising candidate for progressive wireless communication applications.

关键词： dual-band millimeter wave reconfigurable intelligent surface signal enhancement

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Cross-Shard Blockchain-based Asynchronous Federated Learning: Stackelberg Game-based Efficient Resource Allocation 16

Cross-Shard Blockchain-based Asynchronous Federated Learning...

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International Conferences on Internet of Things (iThings) / Green Computing and Communications (GreenCom) / Cyber, Physical and Social Computing (CPSCom) / Smart Data, SmartData and Congress on Cybermatics (Cybermatics)

作者： Gao, Zhipeng Lin, Qigeng Lin, Yijing Yang, Yang Rui, Lanlan Beijing Univ Posts & Telecommun Beijing Peoples R China State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9798350309461

The blockchain-based federated learning (FL) framework adeptly mitigates the concerns of a single point of failure and insecure data sharing, which are inherent to centralized FL systems. Nonetheless, the blockchain-based FL (BlockFL) encounters certain limitations, including: (i) escalated communication costs of blockchain and increased latency of synchronous FL as the quantity of edge devices expands, and (ii) the resource heterogeneity among edge devices, characterized by disparities in computing capabilities and data resources. To address the aforementioned challenges, we introduce a cross-shard based BlockFL framework, called CometChain, which aims to reduce costs and latency through the integration of blockchain shards and asynchronous FL. Based on the validated accuracy and training latency within the proposed framework, we also employ Stackelberg Equilibrium to optimize resource allocation to solve resource heterogeneity of edge devices. Experimental results show that our proposed framework and mechanisms can support efficient, accurate, reliable, and highly scalable FL tasks for edge devices.

关键词： Blockchain Shards Federated Learning Resource Allocation

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A Collaborative Allocation Algorithm of Communicating, Caching and Computing Resources in Local Power Wireless Communication Network

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INFORMATION 2024年第6期15卷

作者： Tang, Jiajia Shao, Sujie Guo, Shaoyong Wang, Ye Wu, Shuang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China State Grid Ningxia Elect Power Co Ltd Yinchuan 750001 Peoples R China

With the rapid development of new power systems, diverse new power services have imposed stricter requirements on network resources and performance. However, the traditional method of transmitting request data to the IoT management platform for unified processing suffers from large delays due to long transmission distances, making it difficult to meet the delay requirements of new power services. Therefore, to reduce the transmission delay, data transmission, storage and computation need to be performed locally. However, due to the limited resources of individual nodes in the local power wireless communication network, issues such as tight coupling between devices and resources and a lack of flexible allocation need to be addressed. The collaborative allocation of resources among multiple nodes in the local network is necessary to satisfy the multi-dimensional resource requirements of new power services. In response to the problems of limited node resources, inflexible resource allocation, and the high complexity of multi-dimensional resource allocation in local power wireless communication networks, this paper proposes a multi-objective joint optimization model for the collaborative allocation of communication, storage, and computing resources. This model utilizes the computational characteristics of communication resources to reduce the dimensionality of the objective function. Furthermore, a mouse swarm optimization algorithm based on multi-strategy improvements is proposed. The simulation results demonstrate that this method can effectively reduce the total system delay and improve the utilization of network resources.

关键词： resource allocation RSO new power system differential evolution

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CEL: Cost-Aware Edge-Assisted Livecast via Optimization With Shapley Value

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IEEE INTERNET OF THINGS JOURNAL 2024年第5期11卷 7805-7816页

作者： Wang, Yizong Zhao, Dong Guo, Zixuan Gao, Teng Ma, Huadong Du, Yang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

The increasingly prevalent livecast streaming causes expensive bandwidth costs and delivery capacity challenges for the content delivery network (CDN) service. As an emerging paradigm, edge computing offers new opportunities to address this issue. The existing works are limited to the data volume pricing model. In contrast, we focus on the 95th-percentile pricing model, which is adopted by many large-scale livecast systems. We propose a Cost-aware Edge-assisted Livecast system (CEL) to minimize the bandwidth cost, consisting of two components: 1) the Shapley values are leveraged to model the actual bandwidth costs for the CDN and edge servers in different time slots, together with acceleration technologies for fast Shapley value estimation and 2) a greedy request scheduling algorithm with theoretical guarantees is proposed to solve the online request scheduling problem, which is NP-hard. Based on real-world data from an operational livecast system, our experiments demonstrate that CEL is time-efficient and achieves at least 14.81% bandwidth cost savings compared with four state-of-the-art methods.

关键词： 95th-percentile pricing model content delivery network (CDN) edge computing livecast system Shapley value

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Unsupervised Microservice Log Anomaly Detection Method Based on Graph Neural Network 15th

Unsupervised Microservice Log Anomaly Detection Method Based...

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15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Liang, Xue Li, Lixiang Peng, Haipeng Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Informat Secur Ctr Beijing 100876 Peoples R China

ISBN: (纸本)9789819771837;9789819771844

The widespread adoption of microservice architecture within the industrial internet domain has led to a substantial increase in system complexity and failure risk. Microservice logs records service and user behavior information in detail. Consequently, microservice logs anomaly detection is crucial for identifying potential issues and maintaining system stability and reliability. In view of the problems of data labeling and difficulty in dealing with timing cross data, this paper proposes an unsupervised microservice logs anomaly detection method based on graph neural network. Based on the optimization of the log parsing process, the proposed method effectively captures the intricate dependencies and temporal relationships in microservice logs using graph structures. Moreover, by learning the graph representation of log sequences in normal mode, this method establishes judgment boundaries and ultimately achieves unsupervised anomaly detection. The experimental results show that the proposed method can effectively extract the features that distinguish log sequences and performs well in accuracy and recall metrics compared with DeepLog, LogAnomaly and DeepTraLog algorithms.

关键词： Graph Neural Networks Unsupervised Learning Microservice Log Anomaly Detection

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