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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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A Liquid Refractive Index Sensor Based On Perforated Structure 24

A Liquid Refractive Index Sensor Based On Perforated Structu...

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8th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Qian, Xiqin Lan, Chuwen Gao, Zehua Beijing Laboratory of Advanced Information Networks Beijing Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing Beijing China

ISBN: (纸本)9798400710094

This article proposes a rectangular cavity resonator that can achieve liquid refractive index sensing. Place a square solid dielectric block inside the cavity, drill uniformly distributed holes in front of and on top of the dielectric block, and observe the small changes in the refractive index of the liquid by measuring the changes in resonant frequency and microwave transmission coefficient S21. After analyzing various structural parameters, the article achieved a sensitivity of 4122MHz/RIU near 26.3GHz, which is 2.37 times higher than the sensitivity of solid structure sensors. This result also provides a new approach for the design and performance improvement of future high-sensitivity refractive index sensors. © 2024 Copyright held by the owner/author(s).

关键词： Cavity resonators

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Spatial Geometry Theory-Based Algorithm for Accurate Positioning With Single Vision Beacon

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 13139-13149页

作者： Yang, Boyu Yang, Yang Guo, Caili Zhu, Zhiyu 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 Shanxi University College of Physics and Electronic Engineering Shanxi Taiyuan030006 China

Quick response (QR) code is one of the most popular beacons for indoor positioning. Existing QR code-based algorithms typically require several QR codes for precise positioning, which may need intense QR code deployment or have limited coverage. This paper proposes a visual positioning algorithm based on feature points and triangular structures on a QR code (V-FTQ), which can achieve precise position and orientation estimation using only a monocular camera and a single QR code beacon. In the considered system, a few QR codes are placed on the ceiling, and a user holds a camera in hand to capture the QR code for positioning. In particular, we first propose an efficient matching mechanism to match the feature points of the captured QR beacon to their projections in the image plane of the camera. Then, based on the geometry relations between the feature points and their projections, V-FTQ can calculate the coordinates of the feature points in the camera coordinate. Finally, the orientation and position of the user are estimated based on linear algebra and spatial geometry. Moreover, since V-FTQ may have a few ambiguous solutions, a singular solution elimination scheme is further proposed based on plane geometry and linear programming, which can effectively eliminate the incorrect solutions. Both simulation and experimental results demonstrate that V-FTQ can achieve a positioning accuracy within 10 cm with only one captured beacon, outperforming baseline algorithms in terms of precision and robustness to noise. © 2004-2012 IEEE.

关键词： Geometry

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Path Optimization Method Under UAV Charging Scheduling Network 24th

Path Optimization Method Under UAV Charging Scheduling Netwo...

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24th International Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2024

作者： Yang, Tingting Wang, Yiqian Zhu, Jie Chang, Shuyu Huang, Haiping Nanjing University of Posts and Telecommunications Nanjing210003 China State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Academy of Sciences Beijing100864 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210003 China

ISBN: (纸本)9789819615278

Unmanned Aerial Vehicles (UAVs) have emerged as integral components in logistics systems, where their potential for efficient delivery services is being explored. However, the limited battery capacity of UAVs poses a significant challenge for long-distance delivery applications. In this paper, we investigate a hybrid problem involving UAV trajectory planning and charging scheduling for long-distance logistics systems with a charging network. In the problem under study, multiple UAVs are assigned long-distance delivery tasks. A UAV can stop at any charging station in the charging network to charge if it is in a low-energy state. An Ant Colony Optimization-based UAV Trajectory Planning and Charging Scheduling (ACO-TPCS) algorithm is proposed to minimize task completion time by optimizing UAV trajectory and charging plans. The main framework of the ACO-TPCS algorithm consists of several key components, including reachable graph construction, candidate flight path generation, pheromone matrix construction, ant generation, feasible solution generation, and pheromone update method. All these components are delicately designed. Through extensive experimentation and comparison with baseline algorithms, we demonstrate the effectiveness of the ACO-TPCS algorithm in addressing the problem under study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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An Effective Load-Balancing Approach for Multicast IPTV Traffic in Multihoming networks

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Concurrency and Computation: Practice and Experience 2025年第12-14期37卷

作者： Benaouda Chaht, Afaf Bencheikh, Mohammed Zouaoui, Chakib Bounoua, Abdennacer Taleb, Nasreddine Naimi, Mohamed Communication Networks Architecture and Multimedia Laboratory Department of Electronic Engineering Djillali Liabes University Sidi Bel Abbes Algeria

Traditional multicast streams are typically sent using unicast routing plans, which follow a single path or, at best, multiple paths of the same cost. However, this method is not efficient for modern networks with various access points. To improve this, we propose a new approach that combines multipath and multicast streams with a load-balancing model. This method aims to enhance multicast traffic transmission in a multipath environment. Our approach separates the Control Plan (CP) from the User Plan (UP). The CP uses Multipath TCP functions, while the UP handles IPTV (Internet Protocol Television) packets, which can be either TCP or UDP. This separation allows for better load distribution, bandwidth conservation, and balanced traffic across available paths. Real-world tests show that all multicast substreams can be smoothly transferred across the network evenly divided and routed through suitable paths whether symmetrical or asymmetrical scénario. Thus, the results of tests on the main performance and reliability parameters such as bandwidth utilization, cross traffic and delay, prove the algorithm's proactivity and responsiveness to network conditions and validate the effectiveness of our approach. In summary, using multicast in multipath TCP networks improves the streaming experience for end users. © 2025 John Wiley & Sons Ltd.

关键词： Transmission control protocol

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Demo: A Hybrid Semantic RAN Protocol Stack Design for 6G System and its Implementation

Demo: A Hybrid Semantic RAN Protocol Stack Design for 6G Sys...

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IEEE Conference on Wireless Communications and Networking

作者： Luhan Wang Haiwen Niu Zhaoming Lu Xiangming Wen Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Recently, Semantic Communication (SC) has been recognized as a crucial new paradigm in 6G, significantly improving information transmission efficiency. However, the diverse range of service types in 6G networks, such as high-data-volume services like AR/VR/MR and low-data-volume applications requiring high accuracy, such as industrial control and data collection, presents significant challenges to fully replacing the fundamental technologies with SC. Therefore, we design a Hybrid Semantic Communication Ratio Access Network (HSC-RAN) protocol stack demo for 6G systems to achieve compatibility and smooth transition between SC and non-SC. Specifically, we take the Physical Downlink Shared Channel (PDSCH) as an example, to efficiently integrate SC with Orthogonal Frequency Division Multiplexing (OFDM). Furthermore, we introduce a novel Downlink Control Information (DCI) format that jointly supports SC and non-SC, enabling real-time video transmission via SC and text transmission through non-SC. Experimental results demonstrate that our approach allows simultaneous transmission of semantic and non-semantic information while maintaining high-quality reconstruction at the receiver.

关键词： 6G mobile communication Wireless communication Protocols OFDM Receivers Streaming media Downlink Semantic communication Real-time systems Next generation networking

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Joint Optimization of VNF Reusing and Routing in Satellite networks

Joint Optimization of VNF Reusing and Routing in Satellite N...

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IEEE Conference on Wireless Communications and Networking

作者： Weixin Yan Zhilong Zhang Danpu Liu Tao Luo Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Low-earth orbit (LEO) satellite networks has attracted a lot of attention, since it is able to provide high-quality services worldwide. With the assistance of NFV technology, the flexibility and the quality of service (QoS) of LEO satellite networks can be further improved. However, most existing researches have not considered virtual network functions (VNFs) reusing during the process of service function chain (SFC) placement in NFV-enabled satellite networks. In this paper, we investigate the problem of SFC placement, and propose a joint VNF reusing and routing algorithm. By considering both of the initialization delay and sharing of VNFs, the service deployment delay can be effectively reduced, and the network profit can be largely improved. In our setting, simulation results show that our proposed algorithm outperforms baseline algorithms in profit, with an improvement of at least 23 %.

关键词： Satellites Service function chaining Simulation Low earth orbit satellites Quality of service Routing Orbits Delays Resource management Optimization

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Deep Learning-Based Mobile User Localization with Reconfigurable Intelligent Surface

Deep Learning-Based Mobile User Localization with Reconfigur...

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IEEE Conference on Wireless Communications and Networking

作者： Chenpan He Zhenghe Zhu Yawen Chen Wei Zheng Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Reconfigurable intelligent surface (RIS) is a promising technique for positioning systems. A large number of reference signals is needed for traditional base station (BS) with RIS localization system which occupy communication resources. To solve this problem, we directly use the DMRS (Demodulation Reference Signal), which does not consume additional communication resources to estimate the user equipment (UE) position. In this paper, we consider a RIS-assisted downlink localization system that takes account for the channel time variability caused by UE mobility. To estimate the UE position, DPSCN (Depthwise Separable Convolution) network consisting of three stages is adopted to capture the relationship between the received signal and the UE *** on the estimated position, the RIS phase is dynamically adjusted, to achieve accurate and continuous tracking of the UE. Extensive simulation results are also presented to demonstrate that the proposed DPSCN learning algorithm can estimate the UE position accurately and outperforms the traditional algorithms.

关键词： Location awareness Accuracy Convolution Simulation Heuristic algorithms Neural networks Reconfigurable intelligent surfaces Prediction algorithms Downlink Demodulation

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A Neural-Based OTFS Channel Estimator

A Neural-Based OTFS Channel Estimator

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IEEE Conference on Wireless Communications and Networking

作者： Zhenghe Zhu Yawen Chen Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

orthogonal time frequency space (OTFS) systems are considered as the reliable solution for addressing the challenges of high mobility in sixth generation (6G) scenarios. By modulating data across both the delay and Doppler dimensions, OTFS efficiently handles the double spread problems caused by high mobility. Accurate and efficient channel estimation is essential for ensuring reliable data reception in OTFS systems. In this paper, we propose a convolutional neural network (CNN) based channel estimator within the standard OTFS transceiver framework to estimate the transmitted channel from the received OTFS signal. Simulation results demonstrate that our neural channel estimator achieves superior channel reconstruction and outperforms existing neural-based methods. Moreover, we analyze its performance with higher-order modulation and show that it effectively supports such modulation under good channel conditions, improving spectral efficiency and throughput.

关键词： 6G mobile communication Time-frequency analysis Spectral efficiency Simulation Channel estimation Modulation Transceivers Reliability Convolutional neural networks Standards

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DRLRA: A Deep Reinforcement Learning architecture Based on Resource Allocation for Mining Key Players in networks

DRLRA: A Deep Reinforcement Learning Architecture Based on R...

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IEEE Conference on Wireless Communications and Networking

作者： Wang Kaili Wu Muqing Zhao Min Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Network key nodes are very important in a network, after identifying them, network optimization can be carried out for these nodes. For example, increasing bandwidth or resource allocation, enhancing fault tolerance, protecting and strengthening key nodes. Or destroy key nodes to quickly break down the network, just like spreading rumors and preventing diseases. Different network optimization measures may vary in different network scenarios, but the core is to enhance or break key nodes, thereby affecting the overall network performance and effectiveness. However, most current network key node identification is based on fixed mathematical models or formulas, limiting the possibility of deep information mining in the network. To address this issue, this paper proposes a deep learning network architecture based on resource allocation to identify key nodes in the network. This allows for more in-depth feature extraction of network semantics and structures while combining the resource allocation situation of each node. In the process of gradually disintegrating the network through deep reinforcement learning, finding the strategy with the fastest decrease in cumulative connectivity rate is considered optimal. Finally, this paper compares the performance of six classical algorithms and the optimal algorithm on six real networks. The effectiveness improvement is 8.11% without sacrificing performance, validating the effectiveness of the method.

关键词： Limiting Semantics Fault tolerant systems Network architecture Deep reinforcement learning Feature extraction Mathematical models Graph neural networks Resource management Optimization

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