检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A Low-Latency Routing Scheme with Traffic Balancing for LEO Satellite networks 15

A Low-Latency Routing Scheme with Traffic Balancing for LEO ...

引用

15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

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

ISBN: (纸本)9798350324662

Low Earth Orbit (LEO) satellites play an important role in satellite networks owing to low-cost, low round-trip delay and high data rate. However, the rapid change of constellation topology makes routing an intractable problem. In a scenario where LEO satellite network serves communication between ground stations (GSs), we adopt virtual topology to shield the dynamics and develop an approach of unipath routing with aim to minimize the total latency of all packets. Inspired by queuing theory, we propose a new routing metric that jointly considers propagation, transmission and queuing latency, which is helpful for traffic distribution awareness and balancing. In addition, Routing Algorithm with Queuing Metric (RAQM) is proposed to calculate the route. Moreover, we have contributed to clarifying the routing latency of packets in detail and quantifying the ability of the constellation to support the traffic load. The simulation results show that the proposed scheme has advantages especially under the condition of heavy traffic load as it can effectively reduce queuing time and end-to-end latency. Furthermore, the proposed scheme can support up to 115% more traffic and avoid network congestion effectively when compared to the selected benchmarks. © 2023 IEEE.

关键词： latency Low Earth Orbit (LEO) satellites queuing theory routing satellite communications traffic

来源：评论

学校读者我要写书评

暂无评论

Deadline-Aware Hybrid HARQ Scheme for Multi-devices Deterministic Transmission over 5G-TSN systems 15

Deadline-Aware Hybrid HARQ Scheme for Multi-devices Determin...

引用

15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Zhao, Jian Wang, Tao Tong, Haonan He, Xinxin Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence China

ISBN: (纸本)9798350324662

This paper studies a deadline-aware hybrid retransmission scheme for an Industrial Internet of Things (IIoT) system over the 5th generation and Time-Sensitive networking (5G-TSN) system. In the considered model, multiple devices must transmit time-sensitive data flows to an edge server for real-time computing and industrial control. These scenarios require the 5G-TSN technique to realize deterministic transmission, which must complete the data transmission before the specified deadline while maintaining ultra-high reliability. Since the uncertainty of the wireless channel in 5G-TSN affects the transmission reliability, hybrid automatic repeat request (HARQ) technology is introduced to improve the transmission success probability. However, two existing mainstream HARQ negative acknowledgment-based (NACK-based) and blind schemes lead to a huge delay penalty and drain spectrum resources, respectively. These drawbacks make it challenging to meet multiple devices' diverse quality of service (QoS) requirements. To address this issue, a deadline-aware hybrid HARQ scheme that combines NACK and blind retransmission schemes is proposed to maximize the number of successfully transmitted deterministic data flows over the 5G-TSN. Specifically, we first propose the delay and reliability models for our considered 5G-TSN IIoT system. Subsequently, our proposed scheme is introduced in detail. The simulation results demonstrate that the proposed scheme can increase the number of deterministic transmission flows by 170.52% and 27.98% with 100 devices compared to 1) single NACK-based and 2) traditional hybrid HARQ schemes. © 2023 IEEE.

关键词： deterministic transmission hybrid automatic repeat request Industrial Internet of Things Time-Sensitive networking

来源：评论

学校读者我要写书评

暂无评论

Attention Aided Channel Prediction Scheme For Satellite-Terrestrial networks 35

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

Dynamic Beam Hopping and Resource Management Optimization Ba...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Swarm Intelligence Joint Optimization Algorithm for Wireless Sensor networks 9

Swarm Intelligence Joint Optimization Algorithm for Wireless...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

Green Task Offloading with Integration of Communication an...

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：