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Intelligent UAV Based Energy Supply for 6G Wireless Powered IoT networks

China Communications 2024年第9期21卷 321-337页

作者： Miao Jiansong Chen Haoqiang Wang Pengjie Li Hairui Zhao Yan Mu Junsheng Yan Shi School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and TelecommunicationsBeijing 100876China

In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same *** our dual-UAV scheme,UAV-E,with a constant power supply,transmits energy to charge the IoT devices on the ground,whereas UAV-B serves the IoT devices by data collection as a base *** this framework,the system's energy efficiency is maximized,which we define as a ratio of the sum rate of IoT devices to the energy consumption of two UAVs during a fixed working *** the constraints of duration,transmit power,energy,and mobility,a difficult non-convex issue is presented by optimizing the trajectory,time duration allocation,and uplink transmit power of *** tackle the non-convex fractional optimization issue,we deconstruct it into three subproblems and we solve each of them iteratively using the descent method in conjunction with sequential convex approximation(SCA)approaches and the Dinkelbach *** simulation findings indicate that the suggested cooperative design has the potential to greatly increase the energy efficiency of the 6G intelligent UAV-assisted wireless powered IoT system when compared to previous benchmark systems.

关键词： 6G wireless powered network energy efficiency IoT intelligent network UAV communication

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Joint User Association,Resource Allocation and Trajectory Design for Multi-UAV-Aided NOMA Wireless Communication systems

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China Communications 2025年第3期22卷 217-233页

作者： Yin Sixing Qu Zhaowei Yu Peng Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and TelecommunicationsBeijing 100876China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

In this paper,we investigate a multi-UAV aided NOMA communication system,where multiple UAV-mounted aerial base stations are employed to serve ground users in the downlink NOMA communication,and each UAV serves its associated users on its own *** aim at maximizing the overall common throughput in a finite time *** a problem is a typical mixed integer nonlinear problem,which involves both continuous-variable and combinatorial *** efficiently solve this problem,we propose a two-layer algorithm,which separately tackles continuous-variable and combinatorial ***,in the inner layer given one user association scheme,subproblems of bandwidth allocation,power allocation and trajectory design are solved based on alternating *** the outer layer,a small number of candidate user association schemes are generated from an initial scheme and the best solution can be determined by comparing all the candidate *** particular,a clustering algorithm based on K-means is applied to produce all candidate user association schemes,the successive convex optimization technique is adopted in the power allocation subproblem and a logistic function approximation approach is employed in the trajectory design *** results show that the proposed NOMA scheme outperforms three baseline schemes in downlink common throughput,including one solution proposed in an existing literature.

关键词： NOMA resource allocation trajectory design UAV communications user association

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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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Reinforcement-Learning-Based Trajectory Design and Phase-Shift Control in UAV-Mounted-RIS Communications

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and networking 2025年 3卷 163-175页

作者： Sun, Tianjiao Yin, Sixing Deng, Li Richard Yu, F. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China Shenzhen University Shenzhen Key Laboratory of Digital and Intelligent Technologies and Systems Shenzhen518060 China

Taking advantages of both unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs), UAV-mounted-RIS systems are expected to enhance transmission performance in complicated wireless environments. In this paper, we focus on system design for a UAV-mounted-RIS system and investigate joint optimization for the RIS's phase shift and the UAV's trajectory. To cope with the practical issue of inaccessible information on the user terminals' (UTs) location and channel state, a reinforcement learning (RL)-based solution is proposed to find the optimal policy with finite steps of "trial-and-error". As the action space is continuous, the deep deterministic policy gradient (DDPG) algorithm is applied to train the RL model. However, the online interaction between the agent and environment may lead to instability during the training and the assumption of (first-order) Markovian state transition could be impractical in real-world problems. Therefore, the decision transformer (DT) algorithm is employed as an alternative for RL model training to adapt to more general situations of state transition. Experimental results demonstrate that the proposed RL solutions are highly efficient in model training along with acceptable performance close to the benchmark, which relies on conventional optimization algorithms with the UT's locations and channel parameters explicitly known beforehand. © 2023 CCBY.

关键词： Reinforcement learning

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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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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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Scalable and Efficient URLLC Resource Allocation Based on Offline Graph Reinforcement Learning 6

Scalable and Efficient URLLC Resource Allocation Based on Of...

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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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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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Green Task Offloading with Integration of Communication and Computation for LEO Satellite Computing networks 18th

Green Task Offloading with Integration of Communication an...

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18th EAI International Conference on Communications and networking in China, ChinaCom 2023

作者： Gu, Jinlu Liu, Danpu Zhang, Zhilong Beijing Laboratory of Advanced Information Network Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9783031671616

Low Earth Orbit (LEO) satellite computing networks have opened up new possibilities for handling onboard tasks in space, while facing the challenges such as latency, energy consumption, and satellite battery lifespan degradation. In the existing studies, onboard computations are often fully offloaded to ground stations, resulting in large delays and energy inefficiencies. Moreover, the impact of satellite battery discharge depth on network lifespan has been neglected. To address these issues, this paper proposes a novel approach to offload tasks among satellites, which enables in-space processing and collaborative computation. To effectively extend the overall lifespan of the satellite network, we formulate an optimization problem which aims to maximize the weighted sum utility of energy efficiency and satellite battery lifespan degradation. Furthermore, a green task offloading strategy based on genetic algorithm is proposed to solve the problem. Simulation results demonstrate its benefits in reducing both satellites’energy consumption and discharge depth. Compared to the baseline, the proposed strategy prolongs the lifespan of the satellite network by 42.2%. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Satellite communication systems

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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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