Title:
ACCESS NETWORK NODE, ACCESS NETWORK NODE METHOD, AND NETWORK SYSTEM
Document Type and Number:
WIPO Patent Application WO/2023/152861
Kind Code:
A1
Abstract:
An access network node (12) is connected to a first core network (15) of a first public land mobile network (PLMN) (10), and is connected to a second core network (25) of a second PLMN (20). In response to an increase in load of the first core network or to application of a disaster condition to an area to which a cell of the access network node (12) belongs, the access network node (12) limits accesses to the cell from a plurality of pieces of user equipment (UEs) (32) using the second core network (25) and prioritizes accesses to the cell from UEs (31) using the first core network (15). This allows users of one PLMN to be given higher priority, than users of other PLMNs, for using a shared wireless access network at the time of, for example, a disaster occurrence or the like, as needed.
Inventors:
TAMURA TOSHIYUKI (JP)
FUTAKI HISASHI (JP)
FUTAKI HISASHI (JP)
Application Number:
PCT/JP2022/005303
Publication Date:
August 17, 2023
Filing Date:
February 10, 2022
Export Citation:
Assignee:
NEC CORP (JP)
International Classes:
H04W48/06; H04W48/02; H04W48/16; H04W84/06; H04W88/10
Domestic Patent References:
| WO2009125799A1 | 2009-10-15 |
Foreign References:
| US20140051437A1 | 2014-02-20 | |||
| US20130225123A1 | 2013-08-29 |
Other References:
HUAWEI, HISILICON: "Evaluation & conclusion for KI#6", 3GPP DRAFT; C1-211009, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. CT WG1, no. Electronic meeting; 20210225 - 20210305, 18 February 2021 (2021-02-18), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France , XP051976619
Attorney, Agent or Firm:
IEIRI Takeshi (JP)
Download PDF:
Previous Patent: POWER CONVERSION DEVICE
Next Patent: WIND CONDITION LEARNING DEVICE, WIND CONDITION PREDICTING DEVICE, AND DRONE SYSTEM
Next Patent: WIND CONDITION LEARNING DEVICE, WIND CONDITION PREDICTING DEVICE, AND DRONE SYSTEM