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ML-SLD:A message-level stateless design for cloud-native 5G core network

Digital Communications and networks 2023年第3期9卷 743-756页

作者： Keliang Du Luhan Wang Xiangming Wen Yu Liu Haiwen Niu Shaoxin Huang Beijing Laboratory of Advanced Information Networks China Beijing Key Lab of Network System Architecture and Convergence China School of Information and Communication Engineering Beijing Univ.of Posts&TelecomBeijing100876China

The Internet of Things(IoTs)has become an essential component of the 5th Generation(5G)network and beyond,accelerating the transition to digital *** increasing signaling traffic generated by billions of IoT devices has placed significant strain on the 5G Core network(5GC)control *** address this issue,the 3rd Gener-ation Partnership Project(3GPP)first proposed a Service-Based architecture(SBA),intending to create a flexible,scalable,and agile cloud-native ***,considering the coupling of protocol states and functions,there are still many challenges to fully utilize the benefits of the cloud computing and orchestrate the 5GC in a cloud-native *** propose a Message-Level StateLess Design(ML-SLD)to provide a cloud-native 5GC from an architectural standpoint in this ***,we propose an innovative mechanism for servitization of the N2 interface to maintain the connection between Radio Access network(RAN)and the 5GC,avoiding interruptions and dropouts of large-scale user ***,we propose an On-demand Message Forwarding(OMF)al-gorithm to reduce the impact of cloud fluctuations on the performance of cloud-native ***,we create a prototype that is based on the OpenAirInterface(OAI)5G core network projects,with all network Functions(NFs)packaged in dockers and deployed in a kubernetes-based cloud *** experiments have been built with UERANSIM and Chaosblade simulation *** findings demonstrate the viability and efficiency of our proposed methods.

关键词： Cloud-native 5G core network service Based architecture Stateless OpenAirInterface

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A pipelining task offloading strategy via delay-aware multi-agent reinforcement learning in Cybertwin-enabled 6G network

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Digital Communications and networks 2025年第1期11卷 92-105页

作者： Haiwen Niu Luhan Wang Keliang Du Zhaoming Lu Xiangming Wen Yu Liu Beijing Laboratory of Advanced Information Networks Beijing Univ.of Posts&TelecomBeijing 100876China Beijing Key Lab of Network System Architecture and Convergence Beijing Univ.of Posts&TelecomBeijing 100876China School of Information and Communication Engineering Beijing Univ.of Posts&TelecomBeijing100876China

Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G ***-Agent Deep Reinforcement Learning(MADRL)technologies driven by Cybertwins have been proposed for adaptive task offloading ***,the existence of random transmission delay between Cybertwin-driven agents and underlying networks is not considered in related works,which destroys the standard Markov property and increases the decision reaction time to reduce the task offloading strategy *** order to address this problem,we propose a pipelining task offloading method to lower the decision reaction time and model it as a delay-aware Markov Decision Process(MDP).Then,we design a delay-aware MADRL algorithm to minimize the weighted sum of task execution latency and energy ***,the state space is augmented using the lastly-received state and historical actions to rebuild the Markov ***,Gate Transformer-XL is introduced to capture historical actions'importance and maintain the consistent input dimension dynamically changed due to random transmission ***,a sampling method and a new loss function with the difference between the current and target state value and the difference between real state-action value and augmented state-action value are designed to obtain state transition trajectories close to the real *** results demonstrate that the proposed methods are effective in reducing reaction time and improving the task offloading performance in the random-delay Cybertwin-enabled 6G networks.

关键词： Cybertwin Multi-Agent Deep Reinforcement Learning(MADRL) Task offloading Pipelining Delay-aware

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Research on Mobile network High-precision Absolute Time Synchronization based on TAP

arXiv

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arXiv 2024年

作者： Zhang, Chenyu Wen, Xiangming Zheng, Wei Yu, Longdan Lu, Zhaoming Wang, Zhengying The Beijing Laboratory of Advanced Information Networks Beijing Key Lab of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

With the development of mobile communication and industrial internet technologies, the demand for robust absolute time synchronization based on network for diverse scenarios is significantly growing. TAP is a novel network timing method that aims to achieve sub-microsecond synchronization over air interface. This paper investigates the improvement and end-to-end realization of TAP. This paper first analyzes the effectiveness and deficiencies of TAP by establishing an equivalent clock model which evaluates TAP from timing error composition and allan variance. Second, this paper proposes a detailed base station and terminal design and the corresponding improvement of TAP. Both hardware compensation and protocol software design are taken into account so as to minimize timing error and system cost while maximizing compatibility with 3GPP. Finally, this paper presents a TAP end-to-end 5G prototype system developed based on software defined radio base station and COTS baseband module. The field test results show that the proposed scheme effectively solves the problems of TAP in application and robustly achieves 200ns level timing accuracy in various situations. The average accuracy with long observations can reach 1 nanosecond. It is 2∼3 orders of magnitude better than common network timing methods, including NTP, PTP and the original TAP. Copyright © 2024, The Authors. All rights reserved.

关键词： Wireless networks

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TAX-B2BREC: Multi-Stage Cascade Downstream Company Recommender system Based on Taxation Data 7

TAX-B2BREC: Multi-Stage Cascade Downstream Company Recommend...

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7th IEEE International Conference on network Intelligence and Digital Content, IC-NIDC 2021

作者： Wang, Shengmin Chu, Yi Qiao, Zhenhao Ma, Lan Zhang, Hao Wang, Zehao Qiao, Yuanyuan Beijing University of Posts and Telecommunications Beijing Lab. of Adv. Info. Networks Beijing Key Lab. of Network System Architecture and Convergence Beijing100876 China The Technology Research Institute Aisino Corporation Beijing100195 China

ISBN: (纸本)9781665405829

The public crisis triggered by the COVID-19 pandemic has disastrous effects for B2B markets. With the supply chain and trade disrupted, the benefits of the company have been affected to varying degrees. In order to help companies find potential customers and recover the supply chain, we propose a multi-stage cascade downstream company recommender system based on taxation data. The proposed system can recommend potential buyers for upstream companies, which can help upstream companies find new sales channels. This system includes data processing, matching module, ranking module and system deployment. In the match module, we propose a hybrid recall algorithm to generate the candidate enterprises. In the ranking module, we use DCNV2 model to rank the candidate companies. Moreover, the multistage cascade recommendation algorithm achieves better results compared with the traditional algorithm in B2B recommender system. © 2021 IEEE.

关键词： Recommender systems

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On the Impact of Uncertainty and Calibration on Likelihood-Ratio Membership Inference Attacks

arXiv

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arXiv 2024年

作者： Zhu, Meiyi Guo, Caili Feng, Chunyan Simeone, Osvaldo Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China lab Department of Engineering King’s College London LondonWC2R 2LS United Kingdom

In a membership inference attack (MIA), an attacker exploits the overconfidence exhibited by typical machine learning models to determine whether a specific data point was used to train a target model. In this paper, we analyze the performance of the likelihood ratio attack (LiRA) within an information-theoretical framework that allows the investigation of the impact of the aleatoric uncertainty in the true data generation process, of the epistemic uncertainty caused by a limited training data set, and of the calibration level of the target model. We compare three different settings, in which the attacker receives decreasingly informative feedback from the target model: confidence vector (CV) disclosure, in which the output probability vector is released;true label confidence (TLC) disclosure, in which only the probability assigned to the true label is made available by the model;and decision set (DS) disclosure, in which an adaptive prediction set is produced as in conformal prediction. We derive bounds on the advantage of an MIA adversary with the aim of offering insights into the impact of uncertainty and calibration on the effectiveness of MIAs. Simulation results demonstrate that the derived analytical bounds predict well the effectiveness of MIAs. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

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Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction

arXiv

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arXiv 2023年

作者： Zhu, Meiyi Zecchin, Matteo Park, Sangwoo Guo, Caili Feng, Chunyan Simeone, Osvaldo The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China lab Department of Engineering King's College London LondonWC2R 2LS United Kingdom

In this paper, we consider a wireless federated inference scenario in which devices and a server share a pre-trained machine learning model. The devices communicate statistical information about their local data to the server over a common wireless channel, aiming to enhance the quality of the inference decision at the server. Recent work has introduced federated conformal prediction (CP), which leverages devices-to-server communication to improve the reliability of the server's decision. With federated CP, devices communicate to the server information about the loss accrued by the shared pre-trained model on the local data, and the server leverages this information to calibrate a decision interval, or set, so that it is guaranteed to contain the correct answer with a pre-defined target reliability level. Previous work assumed noise-free communication, whereby devices can communicate a single real number to the server. In this paper, we study for the first time federated CP in a wireless setting. We introduce a novel protocol, termed wireless federated conformal prediction (WFCP), which builds on type-based multiple access (TBMA) and on a novel quantile correction strategy. WFCP is proved to provide formal reliability guarantees in terms of coverage of the predicted set produced by the server. Using numerical results, we demonstrate the significant advantages of WFCP against digital implementations of existing federated CP schemes, especially in regimes with limited communication resources and/or large number of devices. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Performance Optimization for Multicast Mm Wave MIMO networks with Mobile Users

Performance Optimization for Multicast Mm Wave MIMO Networks...

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IEEE International Conference on Communications Workshops, ICC

作者： Songling Zhang Mingzhe Chen Wenjing Zhang Zhaohui Yang Danpu Liu Zhilong Zhang Kai-Kit Wong Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Zhejiang Lab Hangzhou China Zhejiang University Hangzhou China Department of Electronic and Electrical Engineering University College London London UK

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In this paper, the problem of maximizing the sum rate of mobile users in a multi-base station (BS) cooperative millimeter-wave (mmWave) multicast communication system is studied. In the considered model, due to the real-time mobility of users, the users being served by a given BS and beamforming of BSs and users are dynamic. Multiple BSs must cooperate to serve dynamic requests of multiple mobile users. This problem is posed as an optimization framework whose goal is to maximize the sum rate of all mobile users by jointly optimizing the number of users served by all BSs and beamforming matrices of both BSs and users. To solve this non-convex optimization problem, we first introduce a value decomposition based reinforcement learning (VD- RL) algorithm to determine the users to be served by each BS. Then, we use the block diagonalization method to obtain the fully digital transmit beamforming matrices of all BSs as well as the receive beamforming matrices of the users. Finally, a fast optimization algorithm is used to optimize the hybrid beamforming matrices of both BSs and users. Simulation results show that, the proposed algorithm can achieve up to 51 % gain in terms of the sum rate of all mobile users compared to baseline multi-agent algorithms.

关键词： Multicast algorithms Array signal processing Heuristic algorithms Simulation Conferences Multicast communication Real-time systems

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Combination of Polarization and OAM Multiplexing by a Single Reflective Metasurface in Terahertz Regime 9

Combination of Polarization and OAM Multiplexing by a Single...

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9th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2020

作者： He, Xiaoyuan Deng, Li Beijing University of Posts and Telecommunications Beijing Lab. of Advanced Information Networks Beijing Key Lab. of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China

ISBN: (数字)9781728198057

ISBN: (纸本)9781728198057

For achieving more multiplexing degrees, a method to generate and multiplex dual-polarized electromagnetic waves carrying orbital angular momentum(OAM) by a single reflective metasurface is proposed in this paper. Base on this method, a dual-polarized reflective metasurface that can generate eight OAM beams combined into a coaxial beam in terahertz regime is properly designed and simulated to verify this method. According to the simulation results, the proposed method provides an efficient way for a metasurface to generate and multiplex dual-polarized electromagnetic waves carrying OAM. © 2020 IEEE.

关键词： Electromagnetic wave polarization

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3D-Printed Sub-Terahertz Lens for Manipulation of Deflective Quasi-Non-Diffractive OAM Waves

3D-Printed Sub-Terahertz Lens for Manipulation of Deflective...

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Asia-Pacific Conference on Microwave

作者： Xiaoyuan He Li Deng Yang Yang Beijing Laboratory of Advanced Information Networks & Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China School of Electrical and Data Engineering Tech Lab University of Technology Sydney Australia

A 3D-printed sub-terahertz lens for manipulating quasi-non-diffractive orbital angular momentum (OAM) waves is proposed in this paper. The proposed lens comprises all-dielectric elements that impose desired phase profiles on the incident waves by varying their height. Based on the concept of the Bessel beam launchers and the beam deflectors, quasi-non-diffractive OAM waves with a pre-defined deflection angle can be produced by the proposed lens. For verification, two lenses were 3D printed for producing OAM waves with a non-diffractive depth of 55.58λ c (λ c is the free-space wavelength at 140 GHz). Measured by a terahertz imaging camera, the desired quasi-non-diffractive OAM waves were observed behind the proposed lenses with expected non-diffractive performance and a large deflection angle of up to 47°.

关键词： Microwave measurement Terahertz wave imaging Three-dimensional displays Wavelength measurement Extraterrestrial measurements Cameras Orbits

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DQ-RM: Deep Reinforcement Learning-based Route Mutation Scheme for Multimedia Services 16

DQ-RM: Deep Reinforcement Learning-based Route Mutation Sche...

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16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020

作者： Zhang, Tao Xu, Changqiao Zhang, Bingchi Kuang, Xiaohui Wang, Yue Yang, Shujie Muntean, Gabriel-Miro State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of International Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Science and Technology on Information System Security Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Performance Engineering Lab. School of Electronic Engineering Dublin City University Dublin Ireland

ISBN: (纸本)9781728131290

Increasingly growing various multimedia services (e.g., interactive live video and so on) have brought tremendous pressure on existing static defense techniques. To cope with inherent drawback of static defense techniques, network Moving Target Defense (NMTD) such as route mutation (RM) was proposed. What's more, applying reinforcement learning (RL) into RM has been proved feasible in our previous work. But two main problems still need to be considered in this combination of RL with RM: 1) It lacks the consideration of multiple flows situation. 2) With the state-action space grow larger, current solution can't handle efficiently. In this paper, we propose a deep Q-learning method for RM (DQ-RM) to solve above two problems. Firstly, benefited from the satisfiability module theory, we formalize RM space considering single flow and multiple flows concurrently. Then we further propose a deep reinforcement learning-based RM scheme based on our previous work, which is suitable for large-scale state-action space. Finally, extensive experimental results highlight the improvement of DQ-RM in defense performance and convergence speed compared to the representative solution. © 2020 IEEE.

关键词： Reinforcement learning

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