US 12,308,922 B2
Reinforcement learning machine learning-assisted beam pair selection for handover in radio access networks
Sebastian Thalanany, Kildeer, IL (US); Narothum Saxena, Hoffman Estates, IL (US); and Michael S. Irizarry, Barrington Hills, IL (US)
Assigned to United States Cellular Corporation, Chicago, IL (US)
Filed by United States Cellular Corporation, Chicago, IL (US)
Filed on Dec. 30, 2022, as Appl. No. 18/091,557.
Prior Publication US 2024/0223256 A1, Jul. 4, 2024
Int. Cl. H04B 7/06 (2006.01); H04W 72/044 (2023.01); H04W 72/0457 (2023.01); H04W 72/50 (2023.01)
CPC H04B 7/0639 (2013.01) [H04W 72/0457 (2023.01); H04W 72/046 (2013.01); H04W 72/535 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A method carried out over a mobile wireless network for performing beam pair (BP) and end-to-end (E2E) network slice selection for supporting an invoked service on a mobile equipment (ME), the method comprising:
establishing an initial BP with a radio access network (RAN) node, using an available link policy, enabling communicating a request to the RAN node including an indication of a desired service level for a service invoked on the ME;
updating, in accordance with the indication of a desired service level, a link policy and an E2E network slice policy by performing a reinforcement learning, wherein the link policy is used to select a BP for the ME for a given ME mobility pattern, and wherein the E2E network slice policy is used to select an E2E network slice for the desired service level for the service invoked on the ME; and
selecting an E2E network slice including a target BP selected according to the link policy, to support the service invoked by the ME.