Currently,limited licensed frequency bands cannot meet the increasing demands for various wireless communication applications any more.It is necessary to extend wireless communication networks to unlicensed spectrum.I...Currently,limited licensed frequency bands cannot meet the increasing demands for various wireless communication applications any more.It is necessary to extend wireless communication networks to unlicensed spectrum.In this paper,we propose a new bargaining framework for unlicensed band access to achieve high spectrum efficiency,where one radio access technology(RAT)(such as macro cellular network)“competes”the unlicensed bands with multiple other RATs(such as small cellular networks or Wi-Fi)virtually.Considering that macro cell can share unlicensed frequencies with multiple small cells which are in the same coverage area for more freedom,we use bargaining game theory to fairly and effectively share the unlicensed spectrum between macro and multiple heterogeneous small cell networks,where bargaining loss and time dissipation loss for virtual“price”of unlicensed bands are mainly considered.In the oneto-many bargaining process,we also develop a multiple RAT alliance game strategy to reduce transmission loss in a joint manner.Simulation results show that the proposed unlicensed band sharing algorithm significantly improves the spectrum efficiency performance compared with the other practical schemes for heterogeneous networks.展开更多
To control the temporal profile of a relativistic electron beam to meet requirements of various advanced scientific applications like free-electronlaser and plasma wakefield acceleration,a widely-used technique is to ...To control the temporal profile of a relativistic electron beam to meet requirements of various advanced scientific applications like free-electronlaser and plasma wakefield acceleration,a widely-used technique is to manipulate the dispersion terms which turns out to be one-to-many problems.Due to their intrinsic one-to-many property,current popular stochastic optimization approaches on temporal shaping may face the problems of long computing time or sometimes suggesting only one solution.Here we propose a real-time solver for one-to-many problems of temporal shaping,with the aid of a semi-supervised machine learning method,the conditional generative adversarial network(CGAN).We demonstrate that the CGAN solver can learn the one-to-many dynamics and is able to accurately and quickly predict the required dispersion terms for different custom temporal profiles.This machine learning-based solver is expected to have the potential for wide applications to one-to-many problems in other scientific fields.展开更多
基金the National Natural Science Foundation of China under Grant 61871433,61828103in part by the Research Platform of South China Normal University and Foshan
文摘Currently,limited licensed frequency bands cannot meet the increasing demands for various wireless communication applications any more.It is necessary to extend wireless communication networks to unlicensed spectrum.In this paper,we propose a new bargaining framework for unlicensed band access to achieve high spectrum efficiency,where one radio access technology(RAT)(such as macro cellular network)“competes”the unlicensed bands with multiple other RATs(such as small cellular networks or Wi-Fi)virtually.Considering that macro cell can share unlicensed frequencies with multiple small cells which are in the same coverage area for more freedom,we use bargaining game theory to fairly and effectively share the unlicensed spectrum between macro and multiple heterogeneous small cell networks,where bargaining loss and time dissipation loss for virtual“price”of unlicensed bands are mainly considered.In the oneto-many bargaining process,we also develop a multiple RAT alliance game strategy to reduce transmission loss in a joint manner.Simulation results show that the proposed unlicensed band sharing algorithm significantly improves the spectrum efficiency performance compared with the other practical schemes for heterogeneous networks.
基金supported by National Natural Science Foundation of China(No.11922512)Youth Innovation Promotion Association of Chinese Academy of Sciences(No.Y201904)National Key R&D Program of China(No.2016YFA0401900).
文摘To control the temporal profile of a relativistic electron beam to meet requirements of various advanced scientific applications like free-electronlaser and plasma wakefield acceleration,a widely-used technique is to manipulate the dispersion terms which turns out to be one-to-many problems.Due to their intrinsic one-to-many property,current popular stochastic optimization approaches on temporal shaping may face the problems of long computing time or sometimes suggesting only one solution.Here we propose a real-time solver for one-to-many problems of temporal shaping,with the aid of a semi-supervised machine learning method,the conditional generative adversarial network(CGAN).We demonstrate that the CGAN solver can learn the one-to-many dynamics and is able to accurately and quickly predict the required dispersion terms for different custom temporal profiles.This machine learning-based solver is expected to have the potential for wide applications to one-to-many problems in other scientific fields.
