The sixth generation(6G)mobile network is envisaged to be commercially deployed around 2030,which will profoundly change people's lifestyles and accelerate the digitalization of society.To ensure that the requirem...The sixth generation(6G)mobile network is envisaged to be commercially deployed around 2030,which will profoundly change people's lifestyles and accelerate the digitalization of society.To ensure that the requirements of 6G can be achieved,it is essential to establish a set of key performance indicators(KPIs).This paper comprehensively assesses the KPIs not only from the service requirements but also from the technical feasibility points of view.Specifically,theoretical derivations of KPIs have been clarified,and numerical evaluations have been conducted with reasonable technical assumptions.Evaluation results show that some KPIs defined from the service requirements can be improved through advanced technologies while some are still challenging for practical implementations,such as Tbps-level peak data rate and 0.1 ms user plane latency.In addition,it is also necessary to comply with multiple KPIs for some cases.Furthermore,based on the technical analysis,the potential enabling technologies are outlined and foreseeable implementation challenges as well as possible solutions are presented,which promotes a more reasonable design for 6G mobile network.展开更多
In order to support the future digital society,sixth generation(6G)network faces the challenge to work efficiently and flexibly in a wider range of scenarios.The traditional way of system design is to sequentially get...In order to support the future digital society,sixth generation(6G)network faces the challenge to work efficiently and flexibly in a wider range of scenarios.The traditional way of system design is to sequentially get the electromagnetic wave propagation model of typical scenarios firstly and then do the network design by simulation offline,which obviously leads to a 6G network lacking of adaptation to dynamic environments.Recently,with the aid of sensing enhancement,more environment information can be obtained.Based on this,from radio wave propagation perspective,we propose a predictive 6G network with environment sensing enhancement,the electromagnetic wave propagation characteristics prediction enabled network(EWave Net),to further release the potential of 6G.To this end,a prediction plane is created to sense,predict and utilize the physical environment information in EWave Net to realize the electromagnetic wave propagation characteristics prediction timely.A two-level closed feedback workflow is also designed to enhance the sensing and prediction ability for EWave Net.Several promising application cases of EWave Net are analyzed and the open issues to achieve this goal are addressed finally.展开更多
Recently,whether the channel prediction can be achieved in diverse communication scenarios by directly utilizing the environment information gained lots of attention due to the environment impacting the propagation ch...Recently,whether the channel prediction can be achieved in diverse communication scenarios by directly utilizing the environment information gained lots of attention due to the environment impacting the propagation characteristics of the wireless channel.This paper presents an environment information-based channel prediction(EICP)method for connecting the environment with the channel assisted by the graph neural networks(GNN).Firstly,the effective scatterers(ESs)producing paths and the primary scatterers(PSs)generating single propagation paths are detected by building the scatterercentered communication environment graphs(SCCEGs),which can simultaneously preserve the structure information and highlight the pending scatterer.The GNN-based classification model is implemented to distinguish ESs and PSs from other scatterers.Secondly,large-scale parameters(LSP)and small-scale parameters(SSP)are predicted by employing the GNNs with multi-target architecture and the graphs of detected ESs and PSs.Simulation results show that the average normalized mean squared error(NMSE)of LSP and SSP predictions are 0.12 and 0.008,which outperforms the methods of linear data learning.展开更多
Channels are one of the five critical components of a communication system,and their ergodic capacity is based on all realizations of a statistical channel model.This statistical paradigm has successfully guided the d...Channels are one of the five critical components of a communication system,and their ergodic capacity is based on all realizations of a statistical channel model.This statistical paradigm has successfully guided the design of mobile communication systems from first generation(1G)to fifth generation(5G).However,this approach relies on offline channel measurements in specific environments,and thus,the system passively adapts to new environments,resulting in deviation from the optimal performance.As sixth generation(6G)expands into ubiquitous environments and pursues higher capacity,numerous sensing and artificial intelligence(AI)-based methods have emerged to combat random channel fading.However,there remains an urgent need for a proactive and online system design paradigm.From a system perspective,we propose an environment intelligence communication(EIC)based on wireless environmental information theory(WEIT)for 6G.The proposed EIC architecture operates in three steps.First,wireless environmental information(WEI)is acquired using sensing techniques.Then,leveraging WEI and channel data,AI techniques are employed to predict channel fading,thereby mitigating channel uncertainty.Finally,the communication system autonomously determines the optimal air-interface transmission strategy based on real-time channel predictions,enabling intelligent interaction with the physical environment.To make this attractive paradigm shift from theory to practice,we establish WEIT for the first time by answering three key problems:How should WEI be defined?Can it be quantified?Does it hold the same properties as statistical communication information?Subsequently,EIC aided by WEI(EIC-WEI)is validated across multiple air-interface tasks,including channel state information prediction,beam prediction,and radio resource management.Simulation results demonstrate that the proposed EIC-WEI significantly outperforms the statistical paradigm in decreasing overhead and performance optimization.Finally,several open problems and challenges,including regarding its accuracy,complexity,and generalization,are discussed.This work explores a novel and promising way for integrating communication,sensing,and AI capability in 6G.展开更多
文摘The sixth generation(6G)mobile network is envisaged to be commercially deployed around 2030,which will profoundly change people's lifestyles and accelerate the digitalization of society.To ensure that the requirements of 6G can be achieved,it is essential to establish a set of key performance indicators(KPIs).This paper comprehensively assesses the KPIs not only from the service requirements but also from the technical feasibility points of view.Specifically,theoretical derivations of KPIs have been clarified,and numerical evaluations have been conducted with reasonable technical assumptions.Evaluation results show that some KPIs defined from the service requirements can be improved through advanced technologies while some are still challenging for practical implementations,such as Tbps-level peak data rate and 0.1 ms user plane latency.In addition,it is also necessary to comply with multiple KPIs for some cases.Furthermore,based on the technical analysis,the potential enabling technologies are outlined and foreseeable implementation challenges as well as possible solutions are presented,which promotes a more reasonable design for 6G mobile network.
基金supported by the National Natural Science Foundation of China(No.92167202,61925102,U21B2014,62101069)the National Key R&D Program of China(No.2020YFB1805002)。
文摘In order to support the future digital society,sixth generation(6G)network faces the challenge to work efficiently and flexibly in a wider range of scenarios.The traditional way of system design is to sequentially get the electromagnetic wave propagation model of typical scenarios firstly and then do the network design by simulation offline,which obviously leads to a 6G network lacking of adaptation to dynamic environments.Recently,with the aid of sensing enhancement,more environment information can be obtained.Based on this,from radio wave propagation perspective,we propose a predictive 6G network with environment sensing enhancement,the electromagnetic wave propagation characteristics prediction enabled network(EWave Net),to further release the potential of 6G.To this end,a prediction plane is created to sense,predict and utilize the physical environment information in EWave Net to realize the electromagnetic wave propagation characteristics prediction timely.A two-level closed feedback workflow is also designed to enhance the sensing and prediction ability for EWave Net.Several promising application cases of EWave Net are analyzed and the open issues to achieve this goal are addressed finally.
基金supported by the National Science Fund for Distinguished Young Scholars(No.61925102)National Natural Science Foundation of China(No.62101069)+2 种基金National Natural Science Foundation of China(No.62031019)National Natural Science Foundation of China(No.92167202)BUPT-CMCC Joint Innovation Center.
文摘Recently,whether the channel prediction can be achieved in diverse communication scenarios by directly utilizing the environment information gained lots of attention due to the environment impacting the propagation characteristics of the wireless channel.This paper presents an environment information-based channel prediction(EICP)method for connecting the environment with the channel assisted by the graph neural networks(GNN).Firstly,the effective scatterers(ESs)producing paths and the primary scatterers(PSs)generating single propagation paths are detected by building the scatterercentered communication environment graphs(SCCEGs),which can simultaneously preserve the structure information and highlight the pending scatterer.The GNN-based classification model is implemented to distinguish ESs and PSs from other scatterers.Secondly,large-scale parameters(LSP)and small-scale parameters(SSP)are predicted by employing the GNNs with multi-target architecture and the graphs of detected ESs and PSs.Simulation results show that the average normalized mean squared error(NMSE)of LSP and SSP predictions are 0.12 and 0.008,which outperforms the methods of linear data learning.
基金supported by the National Natural Science Foundation of China(62525101 and 62401084)the National Key Research and Development Program of China(2023YFB2904805)the Beijing University of Posts and Telecommunications-China Mobile Communications Group Joint Innovation Center。
文摘Channels are one of the five critical components of a communication system,and their ergodic capacity is based on all realizations of a statistical channel model.This statistical paradigm has successfully guided the design of mobile communication systems from first generation(1G)to fifth generation(5G).However,this approach relies on offline channel measurements in specific environments,and thus,the system passively adapts to new environments,resulting in deviation from the optimal performance.As sixth generation(6G)expands into ubiquitous environments and pursues higher capacity,numerous sensing and artificial intelligence(AI)-based methods have emerged to combat random channel fading.However,there remains an urgent need for a proactive and online system design paradigm.From a system perspective,we propose an environment intelligence communication(EIC)based on wireless environmental information theory(WEIT)for 6G.The proposed EIC architecture operates in three steps.First,wireless environmental information(WEI)is acquired using sensing techniques.Then,leveraging WEI and channel data,AI techniques are employed to predict channel fading,thereby mitigating channel uncertainty.Finally,the communication system autonomously determines the optimal air-interface transmission strategy based on real-time channel predictions,enabling intelligent interaction with the physical environment.To make this attractive paradigm shift from theory to practice,we establish WEIT for the first time by answering three key problems:How should WEI be defined?Can it be quantified?Does it hold the same properties as statistical communication information?Subsequently,EIC aided by WEI(EIC-WEI)is validated across multiple air-interface tasks,including channel state information prediction,beam prediction,and radio resource management.Simulation results demonstrate that the proposed EIC-WEI significantly outperforms the statistical paradigm in decreasing overhead and performance optimization.Finally,several open problems and challenges,including regarding its accuracy,complexity,and generalization,are discussed.This work explores a novel and promising way for integrating communication,sensing,and AI capability in 6G.
