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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Jiaqi, Ren Jing, Wenpeng Wen, Xiangming Lu, Zhaoming Zhao, Shuyue Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350344349

Deep reinforcement learning (RL) has emerged as a transformative technology for addressing intricate radio resource allocation problems in both 5G and 6G networks. Nevertheless, during the training phases of RL-based resource allocation algorithms, online RL methods introduce potential risks as they necessitate continuous interaction with the environment. Motivated by the fact that the historical data of the resource allocation process in the base station (BS) reveals the operation rules of the mobile network dynamics, this paper proposes a novel data-driven framework for resource allocation optimization, termed Offline Graph Reinforcement Learning (OGRL). Different from existing intelligent resource allocation schemes, which acquire policies by interacting either with wireless network simulators or real-world networks, the proposed OGRL framework exclusively leverages historical data from BS to train a high-performance resource allocation policy. Furthermore, by harnessing the capabilities of the graph neural networks (GNNs) at processing intricate data structures, OGRL can adeptly manage dynamic network environments characterized by continuously changing active users. Extensive experimental results substantiate that, following training with historical data, the proposed OGRL attains Quality of Service (QoS) and packet loss rates comparable to those of the best online algorithms. Furthermore, OGRL exhibits excellent scalability and generalization capabilities. © 2024 IEEE.

关键词： Reinforcement learning

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Dynamic Beam Hopping and Resource Management Optimization Based on Deep Reinforcement Learning for Interference Avoidance 35

Dynamic Beam Hopping and Resource Management Optimization Ba...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Lv, Zeyuan Jing, Wenpeng Zheng, Ziyuan Lu, Zhaoming Liu, Changhao Wen, Xiangming Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing China

ISBN: (纸本)9798350362244

The rapid expansion of low-Earth orbit (LEO) constellations has greatly intensified the difficulty of mitigating interference among them. Beam hopping (BH) technology is widely considered as one of the key technologies to achieve on-demand services while avoiding interference among these constellation systems. However, the majority of existing BH schemes neglect to address the interference among separate constellations. This paper proposes a novel BH scheme to tackle interference challenges in multi-LEO constellations coexistence scenarios. In contrast to existing BH schemes that predominantly target intra-system interference mitigation, our proposed BH scheme has the capability to dynamically adjust the beam bandwidth to avoid both intra-system interference and inter-system interference. We formulate an optimization problem that maximizes throughput, guarantees delay fairness, minimizes transmission power for LEO constellations, and avoids inter-system interference. To tackle this formidable non-convex and non-linear problem, the optimization problem is interpreted as a sequential decision-making problem, modeled by a Markov Decision Process (MDP). By utilizing the Advantage Actor-Critic (A2C) algorithm, the BH pattern is dynamically designed to avoid interference between beams from multiple LEO constellations in a time-varying network environment. Simulation results demonstrate that the proposed scheme outperforms other schemes in terms of long-term throughput and delay fairness. © 2024 IEEE.

关键词： Resource allocation

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Attention Aided Channel Prediction Scheme For Satellite-Terrestrial networks 35

Attention Aided Channel Prediction Scheme For Satellite-Terr...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Cui, Chuankai Jing, Wenpeng Lu, Zhaoming Wen, Xiangming Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing China

ISBN: (纸本)9798350362244

Low Earth orbit (LEO) communication satellites are becoming an essential component of the integrated spaceterrestrial network for sixth-generation (6G) networks nowadays. However, due to the time-varying communication environment and long round-trip time (RTT), the downlink channel state information (CSI), which is always obtained by satellites from user equipments feedback, is severely outdated. Furthermore, as the satellite moves, changes in elevation angle result in received CSI with varying correlation characteristics and levels of outdatedness. Motivated by these issues, this paper proposes a deep learning (DL)-based prediction scheme named ATNet, which is designed to make channel prediction in frequency division duplexing (FDD) satellite Communication systems. Specifically, the predictor integrate convolutional neural network (CNN) and attention mechanism with the foundation of long short-term with memory (LSTM) network. This scheme considers the relationship between elevation angle and channel correlation, as well as the degree of CSI outdatedness. Simulation results demonstrate that our ATNet scheme has achieved a 7 %-17 % improvement in prediction accuracy compared to the baseline schemes. © 2024 IEEE.

关键词： Convolutional neural networks

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Swarm Intelligence Joint Optimization Algorithm for Wireless Sensor networks 9

Swarm Intelligence Joint Optimization Algorithm for Wireless...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Zhang, Zhiwei Wu, Muqing Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing China

ISBN: (纸本)9798350317251

Wireless sensor networks consist of a large number of sensor nodes with limited energy. Because wireless sensor networks are mainly deployed in complex environments, it is difficult to charge or replace nodes. Therefore, the main concern of wireless sensor networks is the effective use of node energy. In this study, we propose a joint optimization algorithm based on the characteristics of Grey Wolf optimization algorithm, butterfly algorithm and ant colony algorithm. The algorithm uses the distance to the base station and the remaining energy to select the appropriate cluster head and the best data forwarding path. The simulation results show that the algorithm is superior to LEACH algorithm, grey wolf optimization algorithm and particle swarm optimization algorithm in network lifetime and residual energy. © 2023 IEEE.

关键词： Swarm intelligence

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

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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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Joint Optimization of Age of Information and Energy Consumption in UAV Trajectory Planning 24

Joint Optimization of Age of Information and Energy Consumpt...

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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Jiao, Yang Wu, Muqing Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363760

With the advancement of technology, the application of unmanned aerial vehicles (UAVs) in emergency communications and disaster response have received widespread attention, especially in emergency situations such as natural disasters where traditional communication cannot transmit information in a timely and effective manner. This paper investigates the auxiliary communication of UAVs to ground equipment in disaster response scenarios, focusing particularly on the problem of optimizing UAV trajectories to minimize the Age of Information (AoI). We propose a multi-agent control trajectory and communication algorithm (MACTC) to improve learning efficiency and performance, addressing the dual challenges of information timeliness and energy consumption in drone networks. The study demonstrates that optimizing UAV service allocation and trajectory design can significantly reduce the AoI and save energy. Simulation results show that, compared to existing algorithms, MACTC offers significant advantages in reducing AoI and energy consumption, providing an effective solution for UAV network performance optimization. © 2024 IEEE.

关键词： Antennas

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WiFineTrack: Enabling Fine-Grained Position Tracking Using Commodity WiFi 35

WiFineTrack: Enabling Fine-Grained Position Tracking Using C...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Jiang, Gaolong Han, Zijun Lu, Zhaoming Wen, Xiangming Jiang, Jiawei Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing100876 China

ISBN: (纸本)9798350362244

With the development of integrated sensing, communication and computation (ISCC), wireless sensing technology is turning a WiFi device into a special sensor. Specifically, the current research focuses on extending the inherent communication attributes of ubiquitous WiFi signals to achieve indoor position tracking. However, previous works based on geometric positioning like triangulation are limited by the number of antennas and bandwidth, and can only achieve decimeter-level positioning accuracy. To this end, we present WiFineTrack, enabling fine-grained position tracking based on Channel State Information (CSI). Instead of representing the continuous absolute positions directly, WiFineTrack first characterizes the relative motion traces by mapping the target's movements in relation to its prior position. Then the model correlates the angle-based triangulation with the derived relative motion traces to reconstruct the absolute motion traces. Thus, the model shifts the tracking task from traditional absolute geometry to relative length variation. WiFineTrack designs an elaborate denoising framework for tracking refinement, including phase correcting based on the bidirectional transmission mechanism and accumulated error suppression based on the Extended Kalman Filter (EKF). We implement WiFineTrack using off-the-shelf WiFi devices, and empirical results show that WiFineTrack can achieve an accuracy of 10.45 cm under diverse environment conditions. © 2024 IEEE.

关键词： Triangulation

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Radio Simultaneous Localization and Mapping with Moving Object Tracking in Dynamic Environments 35

Radio Simultaneous Localization and Mapping with Moving Obje...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Lv, Xue Xiang, Wan Chen, Yawen Lu, Zhaoming Wen, Xiangming Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350362244

Generation (5G) mobile communication provides high-resolution measurements of delays and angles, which make radio simultaneous localization and mapping (SLAM) a possibility. Current radio SLAM systems focus on static environments. However, moving objects may degrade the performance of SLAM systems. In this paper, we propose an integrated radio SLAM and moving object tracking scheme for dynamic environments. Specifically, we first use a grid-based object detection method to detect and filter out measurements belonging to the multipath components (MPCs) of moving objects for accurate radio SLAM. After obtaining the precise vehicle position via SLAM, a curve fitting method is used to track and predict the trajectories of the moving objects, and the prediction results will help in the detection of the object at the next moment. Finally, simulation results demonstrate the performance of the proposed scheme. © 2024 IEEE.

关键词： 5G mobile communication systems

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Autonomous Driving via Knowledge-Enhanced Safe Reinforcement Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Wang, Chengyu Wang, Luhan Lu, Zhaoming Zhou, Shuang Wu, Celimuge Wen, Xiangming Shou, Guochu Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China Meta-Networking Research Center The University of Electro-Communications 1-5-1 Chofugaoka Chofu-shi Tokyo Japan

Recently, the autonomous driving technology is at a critical phase evolving from typical, closed scenarios to largescale, open driving scenarios, which is challenged by the diversity and complexity of the driving environment. Reinforcement learning has the potential to address this challenge, but its application has been hindered by safety issues. To address the safety concern, we propose a Lagrangian framework called Knowledge-Enhanced Safe Reinforcement Learning (KESRL), which integrates prior knowledge of model-based methods into the model-free safe RL framework. Firstly, a model-based constraint based on high-order control barrier function is derived to deduce the safety criteria for the control system. Secondly, we propose a model-free constraint which estimates the distribution of accumulated safety cost and restricts the worst-case safety cost value. Furthermore, the above constraints are unified into the proposed Lagrangian framework to equip the end-to-end autonomous driving system with safety constraints. Experimental results conducted in the CARLA simulator demonstrate the effectiveness of the proposed framework KESRL in decreasing the collision rate and enhancing the control safety compared with several state-of-the-art RL methods. IEEE

关键词： Autonomous vehicles

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FLIT: Multidimensional Data Fusion Localization system Based on Vision Transformer in Single Base Station Mode 35

FLIT: Multidimensional Data Fusion Localization System Based...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

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

ISBN: (纸本)9798350362244

With the rapid development of 5th Generation Mobile Communication Technology (5G), the increased bandwidth not only bolsters communication efficiency but also paves the way for high-precision location-based services within these industrial applications. Positioning by using a single base station (BS) possesses potential vital values in scenarios that multiple BSs are not applicable, such as unmanned aerial vehicle assisted emergency communication scenarios, dynamic and temporary industrial 5G scenarios, etc. However, due to a lack of observed measurements from different base stations, the high accurate and stable positioning by using a single BS faces challenges. To address this problem, this paper introduces a novel approach utilizing the open-source 5G platform OpenAirInterface (OAI) to design application programming interfaces (APIs) for accessing low layer data. By extracting data directly from actual protocol stacks, we have developed a multidimensional data fusion localization system based on Vision Transformer (ViT) in a single-BS mode named FLIT. Conducted within an office setting, our experiments spanned a 100MHz bandwidth, culminating in a system capable of achieving an average positioning error of just 6.9cm, with 90% of measurements within a 9cm error margin and 80% within 5.3cm, thus attaining centimeter-level accuracy in positioning. © 2024 IEEE.

关键词： Bandwidth

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