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Communication-Computing Built-in-Design in Next-Generation Radio Access Networks: Architecture and key technologies

IEEE NETWORK 2024年第3期38卷 100-108页

作者： Guo, Fengxian Peng, Mugen Li, Nan Sun, Qi Li, Xiang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China China Mobile Res Inst Beijing 100053 Peoples R China

The edge computing paradigm presents a promising solution for low-latency and high-throughput data processing, which is critical for future life-changing applications, such as autonomous driving and metaverse. Concurrently, the current radio access network (RAN) is evolving toward virtualization, intelligence, and flexibility, making it an ideal edge computing platform. However, the full potential of such a synergistic system has not yet been unleashed due to the lack of coordination between communication and computing. To better support diverse applications, this article proposes a communication-computing co-design approach for the next-generation RAN to embrace computing in a built-in manner. At a high level, we overview the features of communication and computing, identify the efforts and limitations of the current system, and introduce the designed architecture. From the perspective of communication and computing coordination, key technologies are introduced, including internetworking for computing terminals, traffic steering, and mobility management. A use case referring to mobility management is studied to verify the effectiveness of the built-in approach. We outline some critical challenges, thereby indicating future potential research directions in future RAN.

关键词： Cloud computing Edge computing Costs Computer architecture Quality of service Delays Backhaul networks Radio access networks Next generation networking Virtualization Metaverse Low latency communication Internetworking Data processing Autonomous vehicles

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Priority-Aware Access Strategy for GF-NOMA System in IIoT: The Device-Specific Allocation Approach

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IEEE INTERNET OF THINGS JOURNAL 2024年第2期11卷 2152-2165页

作者： Wu, Di Zhang, Zhi Huang, Yuzhen Qin, Xiaoqi Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Acad Mil Sci PLA Beijing 100091 Peoples R China

To support large-scale connectivity of massive machine-type communication (mMTC) devices in Industrial Internet of Things (IIoT) scenario, a grant-free (GF) transmission aided nonorthogonal multiple access (NOMA) system is considered in this article, where the devices with diverse behavior characteristics coexist. In order to reflect the diversity of IIoT scenario, multiple types of MTC devices are taken into account, and the network environment is divided into stationary mode and overload mode based on the differences in behavior characteristics of devices. Further, the successful access probability maximization problem is established to obtain the priority-aware access strategy. To solve this problem, we propose a novel distributed Q -learning algorithm and an adaptive update (AdaUpdate) aided priority-aware deep Q network (PA-DQN) algorithm for the stationary mode and the overload mode, respectively. Simulation results demonstrate that the proposed algorithms achieve better performance than that of traditional reinforcement learning algorithms and the random access algorithm in terms of overall access efficiency and satisfying the access requirements of emergency devices preferentially.

关键词： Grant-free (GF) transmission nonorthogonal multiple access (NOMA) reinforcement learning (RL)

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Anomaly Detection on Interleaved Log Data With Semantic Association Mining on Log-Entity Graph

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2025年第2期51卷 581-594页

作者： Chu, Guojun Wang, Jingyu Qi, Qi Sun, Haifeng Zhuang, Zirui He, Bo Jing, Yuhan Zhang, Lei Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100083 Peoples R China China Unicom Network Commun Co Ltd Beijing 100032 Peoples R China

Logs record crucial information about runtime status of software system, which can be utilized for anomaly detection and fault diagnosis. However, techniques struggle to perform effectively when dealing with interleaved logs and entities that influence each other. Although manually specifying a grouping field for each dataset can handle the single grouping scenario, the problems of multiple and heterogeneous grouping still remain unsolved. To break through these limitations, we first design a log semantic association mining approach to convert log sequences into Log-Entity Graph, and then propose a novel log anomaly detection model named Lograph. The semantic association can be utilized to implicitly group the logs and sort out complex dependencies between entities, which have been overlooked in existing literature. Also, a Heterogeneous Graph Attention Network is utilized to effectively capture anomalous patterns of both logs and entities, where Log-Entity Graph serves as a data management and feature engineering module. We evaluate our model on real-world log datasets, comparing with nine baseline models. The experimental results demonstrate that Lograph can improve the accuracy of anomaly detection, especially on the datasets where entity relationships are intricate and grouping strategies are not applicable.

关键词： Semantics Sun Software systems Analytical models Accuracy Runtime Vectors Transformers Telecommunications Log analysis anomaly detection content association knowledge discovery heterogeneous graph heterogeneous graph

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DAmpADF: A framework for DNS amplification attack defense based on Bloom filters and NAmpKeeper

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COMPUTERS & SECURITY 2024年 139卷

作者： Dai, Yunwei Huang, Tao Wang, Shuo Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

Domain Name System (DNS) amplification attacks exploit botnets and open recursive DNS servers to launch Distributed Denial of Service (DDoS) attacks. During an attack, the attacker leverages infected computers (bots) to perpetually send small spoofed DNS queries to numerous open recursive DNS servers. The servers, in turn, respond with lots of large DNS responses, which are reflected back to the victim. Such responses are usually several times larger than the original queries, and could exhaust the resources of CPUs, memory, and network bandwidth, rendering them unavailable for benign users. However, most in -network DDoS mitigation systems today inevitably cause normal DNS responses to be discarded while scrubbing traffic, as they do not distinguish between legitimate and malicious responses. To address this issue, some existing solutions employ Bloom filters to filter out unsolicited DNS responses, utilizing the "one-to-one mapping" relationship between DNS queries and responses. In this work, we present a framework called DAmpADF, designed to defend against DNS amplification attacks. The framework employs Bloom filters at the edge or core routers of Internet Service Providers (ISPs) or organizations to filter out malicious DNS responses. To reduce the false positives of the Bloom filters, we propose a novel data structure called the Non -amplifier Keeper (NAmpKeeper), which maintains the most frequently queried DNS servers that are not DNS amplifiers. By excluding queries to non -amplifiers from the Bloom filters, the false positives of Bloom filters are decreased significantly. Experimental results show that DAmpADF outperforms previous methods and achieves a superior filtration ratio of illegitimate DNS responses. Furthermore, the proposed approach incurs small, constant processing and memory overhead, enabling support for high line rates.

关键词： Network security DDoS Bloom filter NAmpKeeper DNS Amplification attack

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An Enhanced Dynamic Ray Tracing Architecture for Channel Prediction Based on Multipath Bidirectional Geometry and Field Extrapolation

An Enhanced Dynamic Ray Tracing Architecture for Channel Pre...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Miao, Yinghe Yu, Li Zhang, Yuxiang Xing, Hongbo Zhang, Jianhua Beijing University of Posts and Telecommunications State Key Lab of Networking and Switching Technology Beijing100876 China

ISBN: (纸本)9798350351255

With the development of sixth generation (6G) networks toward digitalization and intelligentization of communications, rapid and precise channel prediction is crucial for the network potential release. Interestingly, a dynamic ray tracing (DRT) approach for channel prediction has recently been proposed, which utilizes the results of traditional RT to extrapolate the multipath geometry evolution. However, both the priori environmental data and the regularity in multipath evolution can be further utilized. In this work, an enhanced-dynamic ray tracing (E-DRT) algorithm architecture based on multipath bidirectional extrapolation has been proposed. In terms of accuracy, all available environment information is utilized to predict the birth and death processes of multipath components (MPCs) through bidirectional geometry extrapolation. In terms of efficiency, bidirectional electric field extrapolation is employed based on the evolution regularity of the MPCs' electric field. The results in a Vehicle-to-Vehicle (V2V) scenario show that E-DRT improves the accuracy of the channel prediction from 68.3% to 94.8% while reducing the runtime by 7.2% compared to DRT. © 2024 IEEE.

关键词： Extrapolation

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PoSE: Suppressing Perceptual Noise in Embodied Agents for Enhanced Semantic Navigation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第2期9卷 963-970页

作者： Zhuang, Benhui Zhang, Chunhong Hu, Zheng Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China

Embodied agents navigating unknown environments face the challenge of optimizing exploration based on semantic information. Conventional methods, reliant on collected data or pre-defined rules, have limitations in scalability and applicability, while pretrained language models based methods focusing on textual modality encounter perceptual noise, which affects decision-making. To mitigate these problems, this paper presents Prompt-based Vision Context Semantic Exploration (PoSE), an innovative method that leverages prior knowledge from vision-language models (VLMs) to suppress perceptual noise. Through its unique design of prompts that use existential logic, PoSE reduces misidentifications of target objects within the observed environment. It also introduces a unique exploration map to translate target locations into exploration coordinates. The proposed methodology is evaluated on the ALFRED benchmark, demonstrating improved performance against previous rule-based and task-specific data-driven exploration policies. Furthermore, PoSE's semantic exploration policy offers enhancements over pretrained language-model based exploration methods that focus on text modality, showcasing its effectiveness and generality.

关键词： Vision-based navigation cognitive modeling neurorobotics

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Multilayered Fault Detection and Localization With Transformer for Microservice Systems

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IEEE TRANSACTIONS ON RELIABILITY 2024年第3期73卷 1502-1515页

作者： Wang, Jingyu Li, Yuewei Qi, Qi Lu, Yan Wu, Bo Beijing Univ Posts Telecommun State Key Lab Networking Switching Technol Beijing 100088 Peoples R China China Mobile Res Inst Beijing 100053 Peoples R China

Software architecture is undergoing a transition from monolithic architecture to microservices to achieve resilience, agility, and scalability in the software life cycle. The complex dependability of these microservices may lead to unexpected failures, which becomes a major concern on reliability for application providers. The existing fault detection and localization algorithms for microservice systems only focus on the relationship within microservices and cannot achieve finer granularity from a layered system perspective, including microservices, containers, physical machines, and networks. To tackle this problem, we propose a multilayered method that deconstructs cloud-based microservices and connects the information from various layers to enhance the precision of fault detection and localization. The proposed Transformer encoder model can detect anomalies of containers in the resource layer, and by decomposing and analyzing invocation latency, anomalies in the service layer can be detected. After determining the faulty area of the resource layer based on the above anomalies, a multifactor root cause score is used to sort root cause metrics in the faulty area for localization. Evaluations were performed on three datasets: the Sock-Shop dataset we collected from an actual microservice system, the AIOps2020 preliminary dataset, and the SMD. Empirical investigations conducted on these datasets show that our models enhance the F1 score by approximately 0.25 for anomaly detection and improve the mean average precision by up to 0.54 for root cause localization, which underscores the utility of our models in effectively managing microservice systems in practical scenarios.

关键词： Containers Measurement Microservice architectures Location awareness Anomaly detection Transformers Data models artificial intelligence for IT operations fault localization microservice systems transformer

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Service migration with edge collaboration: Multi-agent deep reinforcement learning approach combined with user preference adaptation

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 165卷

作者： Chen, Shiyou Rui, Lanlan Gao, Zhipeng Yang, Yang Qiu, Xuesong Guo, Shaoyong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China China Mobile Res Inst JUTIAN Team Beijing 100053 Peoples R China

Multi-access edge computing provides proximate intelligent services for distributed users. Due to the user's mobility and highly dynamic network, edge servers with limited coverage cannot ensure continuity of running services and maintain high-level Quality of Service. To tackle this issue, an effective service migration strategy is of paramount importance. However, the current approach ignores the cooperation between multiple edge servers and independent users. In this article, we study service migration with edge collaboration to realize lightweight migration by layer-sharing framework of containers, saving redundant transmissions of migration. Then, we formalize the migration decision problem as maximizing the migration utility problem. To obtain efficient online decisions, we proposed a dynamic service migration strategy (MA-DSM) based on multi-agent proximal policy optimization (MAPPO) algorithm, which leverages a flexible multi-policy framework to achieve user preference adaptation. Specifically, we improve the basic MAPPO by devising a context-aware grouping method to cluster agents with user's mobility patterns and service preferences. Parameter sharing is introduced into the actor-critic network to learn customized policies for different clusters, facilitating cooperation among users in the same cluster. Extensive experiments demonstrate that our proposed approach outperforms baselines in terms of convergence, latency and migration utility.

关键词： Edge computing Service migration Multi-agent reinforcement learning Mobility

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Mirror complementary transformer network for RGB-thermal salient object detection

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IET COMPUTER VISION 2024年第1期18卷 15-32页

作者： Jiang, Xiurong Hou, Yifan Tian, Hui Zhu, Lin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Beijing Inst Technol Sch Comp Sci Beijing Peoples R China

Conventional RGB-T salient object detection treats RGB and thermal modalities equally to locate the common salient regions. However, the authors observed that the rich colour and texture information of the RGB modality makes the objects more prominent compared to the background;and the thermal modality records the temperature difference of the scene, so the objects usually contain clear and continuous edge information. In this work, a novel mirror-complementary Transformer network (MCNet) is proposed for RGB-T SOD, which supervise the two modalities separately with a complementary set of saliency labels under a symmetrical structure. Moreover, the attention-based feature interaction and serial multiscale dilated convolution (SDC)-based feature fusion modules are introduced to make the two modalities complement and adjust each other flexibly. When one modality fails, the proposed model can still accurately segment the salient regions. To demonstrate the robustness of the proposed model under challenging scenes in real world, the authors build a novel RGB-T SOD dataset VT723 based on a large public semantic segmentation RGB-T dataset used in the autonomous driving domain. Extensive experiments on benchmark and VT723 datasets show that the proposed method outperforms state-of-the-art approaches, including CNN-based and Transformer-based methods. The code and dataset can be found at .

关键词： image segmentation object detection

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Multimodal Multitask Control Plane Verification Framework

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第6期21卷 6684-6702页

作者： Dai, Yuqi Zhang, Hua Wang, Jingyu Liao, Jianxin Southeast Univ Natl Mobile Commun Res Lab Nanjing 211111 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Modern networks are susceptible to configuration errors, such as misconfigurations and policy conflicts due to the complex interactions of diverse devices through various protocols. Control plane verification offers an effective solution to prevent these errors. However, existing tools face several challenges: (i) prolonged verification times, (ii) the verification of only specific policies, and (iii) poor robustness against node and link failures. To address these issues, we propose a control plane verification framework based on a multimodal multitask learning model. This framework enables simultaneous verification of multiple policies directly from various network configuration files. The learning model utilizes modality fusion techniques to capture both topology-related and traffic-related network features. It is trained on datasets augmented with the failure model to enhance robustness against failures. We compare our framework with three state-of-the-art verification tools: Minesweeper, Hoyan, and Tiramisu. Our evaluation shows that our framework is 2600 times faster than Minesweeper, twice as fast as Hoyan, and 19 times faster than Tiramisu, while maintaining 100% verification accuracy. Furthermore, our framework excels in verifying traffic-related network policies and remains effective even under node and link failures.

关键词： Robustness Scalability Accuracy Task analysis Routing protocols Feature extraction Cognition Control plane verification configuration verification multimodal multitask learning

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