公开(公告)号：US12088386B1

公开(公告)日：2024-09-10

申请号：US18128089

申请日：2023-03-29

Applicant: T-MOBILE INNOVATIONS LLC

Inventor： Timur Kochiev ,  Mochamad Mirza ,  Relin Thomas

IPC: H04B7/06 ,  H04B7/0452 ,  H04W4/02 ,  H04W28/02

CPC classification number: H04B7/0686 ,  H04B7/0452 ,  H04W4/027 ,  H04W28/0278

Abstract: Aspects herein provide systems, methods, and media for dynamically switching between multiple input multiple output algorithms to improve spectral efficiency and capacity. In aspects, based on data encoding traffic, user device mobility, and coverage, a base station automatically and intelligently selects and implements a particular downlink operating schema. Using various periodicity, the base station dynamically switches between various downlink operating schemas to reflect changing conditions in the date that encodes traffic, user device mobility, and coverage.

公开(公告)号：US12034221B2

公开(公告)日：2024-07-09

申请号：US17989402

申请日：2022-11-17

Applicant: T-Mobile Innovations LLC

Inventor： Timur Kochiev

IPC: H01Q3/08 ,  H01Q1/22 ,  H01Q3/02 ,  H04B7/0404 ,  H04B7/10 ,  H04B17/309

CPC classification number: H01Q3/08 ,  H01Q1/2291 ,  H04B7/0404 ,  H04B7/10 ,  H04B17/309

Abstract: System and method for determining the best downlink and uplink speeds, and optimal latency for a customer premise equipment (CPE) device. A series of three-dimensional positions are measured with the downlink speed, uplink speed, and latency taken at each position. The optimal downlink and uplink speeds and latency are determined. A mmWave antenna is moved to the position that coincides with the optimal downlink and uplink speeds, and optical latency.

公开(公告)号：US20240073107A1

公开(公告)日：2024-02-29

申请号：US17893482

申请日：2022-08-23

Applicant: T-Mobile Innovations LLC

Inventor： Timur Kochiev ,  Ariz Jacinto ,  Xiao Zhang ,  Mana Mehraein ,  Andreas Thomas Hindman

IPC: H04L41/5009 ,  H04L41/0631 ,  H04L41/16

CPC classification number: H04L41/5009 ,  H04L41/0631 ,  H04L41/16

Abstract: Computerized systems and methods are provided that utilize a machine learning model to predict anomalies for a radio access network site device and taking corrective action. After a radio access network site device has received a software update, key performance indicators are extracted from the data from the radio access network site device. The key performance indicators are utilized with an anomaly risk model to determine if one or more of the key performance indicators exceeds a baseline threshold for the one or more radio access network devices. Responsive to the one or more key performance indicators exceeding a baseline threshold, a corrective action is initiated modifying the one or more radio access network devices.