In offshore maritime communication sys-tems,base stations(BSs)are employed along the coastline to provide high-speed data service for ves-sels in coastal sea areas.To ensure the line-of-sight propagation of BS-vessel ...In offshore maritime communication sys-tems,base stations(BSs)are employed along the coastline to provide high-speed data service for ves-sels in coastal sea areas.To ensure the line-of-sight propagation of BS-vessel links,high transceiver an-tenna height is required,which limits the number of geographically available sites for BS deployment,and imposes a high cost for realizing effective wide-area coverage.In this paper,the joint user association and power allocation(JUAPA)problem is investigated to enhance the coverage of offshore maritime systems.By exploiting the characteristics of network topology as well as vessels’motion in offshore communica-tions,a multi-period JUAPA problem is formulated to maximize the number of ships that can be simultane-ously served by the network.This JUAPA problem is intrinsically non-convex and subject to mixed-integer constraints,which is difficult to solve either analyt-ically or numerically.Hence,we propose an iterative augmentation based framework to efficiently select the active vessels,where the JUAPA scheme is iteratively optimized by the network for increasing the number of the selected vessels.More specifically,in each itera-tion,the user association variables and power alloca-tion variables are determined by solving two separate subproblems,so that the JUAPA strategy can be up-dated in a low-complexity manner.The performance of the proposed JUAPA method is evaluated by exten-sive simulation,and numerical results indicate that it can effectively increase the number of vessels served by the network,and thus enhances the coverage of off-shore systems.展开更多
In recent years,deep learning-based semantic communications have shown great potential to enhance the performance of communication systems.This has led to the belief that semantic communications represent a breakthrou...In recent years,deep learning-based semantic communications have shown great potential to enhance the performance of communication systems.This has led to the belief that semantic communications represent a breakthrough beyond the Shannon paradigm and will play an essential role in future communications.To narrow the gap between current research and future vision,after an overview of semantic communications,this article presents and discusses ten fundamental and critical challenges in today’s semantic communication field.These challenges are divided into theory foundation,system design,and practical implementation.Challenges related to the theory foundation including semantic capacity,entropy,and rate-distortion are discussed first.Then,the system design challenges encompassing architecture,knowledge base,joint semantic-channel coding,tailored transmission scheme,and impairment are posed.The last two challenges associated with the practical implementation lie in cross-layer optimization for networks and standardization.For each challenge,efforts to date and thoughtful insights are provided.展开更多
针对现有图像视频恢复增强方法存在的问题,本文提出一种基于语义特征提取的神经网络模型图像及视频质量增强算法.首先提出一种基于语义特征的图像恢复增强框架,然后建立退化模型和重建模型的联合优化.在公开数据集上对所提模型进行验证...针对现有图像视频恢复增强方法存在的问题,本文提出一种基于语义特征提取的神经网络模型图像及视频质量增强算法.首先提出一种基于语义特征的图像恢复增强框架,然后建立退化模型和重建模型的联合优化.在公开数据集上对所提模型进行验证,并与现有算法进行对比,结果表明:所提方法相比新型超分辨率算法PULSE(Photo Upsampling via Latent Space Exploration,潜空间搜索照片升采样)能够实现RankIQA(Rank Image Quality Assessment,图像质量评价排名)得分50%的提升,并且和原始高清图像、视频质量得分接近;在用户评价方面,有81%的重建结果被认为优于对比算法,表明所提算法具有更高的重构图像和视频质量.展开更多
The integration of satellite communication network and cellular network has a great potential to enable ubiquitous connectivity in future communication networks.Among numerous related application scenarios,the direct ...The integration of satellite communication network and cellular network has a great potential to enable ubiquitous connectivity in future communication networks.Among numerous related application scenarios,the direct connection of mobile phone to satellite has attracted increasing attention.However,the spectrum scarcity in the sub-6 GHz band and low spectrum utilization prevents its popularity.To address these problem,in this paper,we propose a dynamic spectrum sharing method for satellite network and cellular network based on beam-hopping.Specifically,we first develop a centralized dynamic spectrum sharing architecture based on beam-hopping,and propose a delay pre-compensation scheme for beam hopping pattern.Then,an optimization problem is formulated to maximize the overall capacity of the integrated network,with considering the service requirements,the fairness between beam positions and mixed co-channel interference,etc.To solve this problem,a polling-based dynamic resource allocation algorithm is proposed.Simulation results confirm that the proposed algorithm can effectively reduce the serious cochannel interference between different beams or different systems,and improve the spectrum utilization rate as well as system capacity.展开更多
基金supported by the National Key Research and Development Program of China under Grant 2018YFA0701601by the Program of Jiangsu Province under Grant NTACT-2024-Z-001.
文摘In offshore maritime communication sys-tems,base stations(BSs)are employed along the coastline to provide high-speed data service for ves-sels in coastal sea areas.To ensure the line-of-sight propagation of BS-vessel links,high transceiver an-tenna height is required,which limits the number of geographically available sites for BS deployment,and imposes a high cost for realizing effective wide-area coverage.In this paper,the joint user association and power allocation(JUAPA)problem is investigated to enhance the coverage of offshore maritime systems.By exploiting the characteristics of network topology as well as vessels’motion in offshore communica-tions,a multi-period JUAPA problem is formulated to maximize the number of ships that can be simultane-ously served by the network.This JUAPA problem is intrinsically non-convex and subject to mixed-integer constraints,which is difficult to solve either analyt-ically or numerically.Hence,we propose an iterative augmentation based framework to efficiently select the active vessels,where the JUAPA scheme is iteratively optimized by the network for increasing the number of the selected vessels.More specifically,in each itera-tion,the user association variables and power alloca-tion variables are determined by solving two separate subproblems,so that the JUAPA strategy can be up-dated in a low-complexity manner.The performance of the proposed JUAPA method is evaluated by exten-sive simulation,and numerical results indicate that it can effectively increase the number of vessels served by the network,and thus enhances the coverage of off-shore systems.
基金supported in part by the National Key Research and Development Program of China under Grant 2021YFA1000500(4)in part by the Natural Science Foundation of China(NSFC)under Grant 62293484,Grant U22B2001,Grant 62425110,Grant 62227801,Grant 62442106.
文摘In recent years,deep learning-based semantic communications have shown great potential to enhance the performance of communication systems.This has led to the belief that semantic communications represent a breakthrough beyond the Shannon paradigm and will play an essential role in future communications.To narrow the gap between current research and future vision,after an overview of semantic communications,this article presents and discusses ten fundamental and critical challenges in today’s semantic communication field.These challenges are divided into theory foundation,system design,and practical implementation.Challenges related to the theory foundation including semantic capacity,entropy,and rate-distortion are discussed first.Then,the system design challenges encompassing architecture,knowledge base,joint semantic-channel coding,tailored transmission scheme,and impairment are posed.The last two challenges associated with the practical implementation lie in cross-layer optimization for networks and standardization.For each challenge,efforts to date and thoughtful insights are provided.
文摘针对现有图像视频恢复增强方法存在的问题,本文提出一种基于语义特征提取的神经网络模型图像及视频质量增强算法.首先提出一种基于语义特征的图像恢复增强框架,然后建立退化模型和重建模型的联合优化.在公开数据集上对所提模型进行验证,并与现有算法进行对比,结果表明:所提方法相比新型超分辨率算法PULSE(Photo Upsampling via Latent Space Exploration,潜空间搜索照片升采样)能够实现RankIQA(Rank Image Quality Assessment,图像质量评价排名)得分50%的提升,并且和原始高清图像、视频质量得分接近;在用户评价方面,有81%的重建结果被认为优于对比算法,表明所提算法具有更高的重构图像和视频质量.
基金supported in part by the National Key Research and Development Program of China under Grant 2018YFA0701601in part by the National Natural Science Foundation of China under Grant 61922049 and Grant 61941104in part by the Tsinghua University-China Mobile Communications Group Company Ltd.,Joint Institute.
文摘The integration of satellite communication network and cellular network has a great potential to enable ubiquitous connectivity in future communication networks.Among numerous related application scenarios,the direct connection of mobile phone to satellite has attracted increasing attention.However,the spectrum scarcity in the sub-6 GHz band and low spectrum utilization prevents its popularity.To address these problem,in this paper,we propose a dynamic spectrum sharing method for satellite network and cellular network based on beam-hopping.Specifically,we first develop a centralized dynamic spectrum sharing architecture based on beam-hopping,and propose a delay pre-compensation scheme for beam hopping pattern.Then,an optimization problem is formulated to maximize the overall capacity of the integrated network,with considering the service requirements,the fairness between beam positions and mixed co-channel interference,etc.To solve this problem,a polling-based dynamic resource allocation algorithm is proposed.Simulation results confirm that the proposed algorithm can effectively reduce the serious cochannel interference between different beams or different systems,and improve the spectrum utilization rate as well as system capacity.
