6G Mobile Network Requirements and Technical Feasibility Study

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.

A Predictive 6G Network with Environment Sensing Enhancement:From Radio Wave Propagation Perspective

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.

Environment Information-Based Channel Prediction Method Assisted by Graph Neural Network

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.