公开(公告)号：US20220386171A1

公开(公告)日：2022-12-01

申请号：US17334577

申请日：2021-05-28

Applicant: Microsoft Technology Licensing, LLC

Inventor： Manikanta KOTARU ,  Arjun Varman BALASINGAM ,  Paramvir BAHL

IPC: H04W28/06 ,  H04W28/08

Abstract: Aspects of the present disclosure relate to allocating RAN resources among RAN slices using a machine learning model. In examples, the machine learning model may determine an optimal RAN resource configuration based on compute power needs. As a result, RAN resource allocation generation and compute power requirements may improve, even in instances with changing or unknown network conditions. In examples, a prediction engine may receive communication parameters and/or requirements associated with service-level agreements (SLAs) for applications executing at least partially at a device in communication with the RAN. The RAN may generate one or more RAN resource configuration for implementation among RAN slices. Upon a change in network conditions or SLA requirements, an optimal RAN configuration may be determined in terms of required compute power.

公开(公告)号：US20230413076A1

公开(公告)日：2023-12-21

申请号：US17841445

申请日：2022-06-15

Applicant: Microsoft Technology Licensing, LLC

Inventor： Manikanta KOTARU ,  Paramvir BAHL ,  Arjun Varman BALASINGAM

IPC: H04W24/08 ,  H04L43/0888 ,  H04B17/336 ,  H04B17/391

CPC classification number: H04W24/08 ,  H04L43/0888 ,  H04B17/336 ,  H04B17/3913

Abstract: Described are examples for calculating and exposing network capacity and congestion to applications. A network entity such as a radio access network (RAN) intelligent controller (RIC) or virtual base station component receives measurements of a signal quality for a plurality of user devices connected to a RAN. The network entity estimates a deliverable throughput of a wireless link for a user device of the plurality of user devices based on at least the measurements. The network entity can consider other factors such as a number of competing users, queue sizes of the user device and of the competing users, or a scheduling policy. The network entity provides the deliverable throughput to an application server for an application of the user device communicating with the application server via the RAN. The application server can adapt a data rate for the application and the user device based on the deliverable throughput.

公开(公告)号：US20220377751A1

公开(公告)日：2022-11-24

申请号：US17326731

申请日：2021-05-21

Applicant: Microsoft Technology Licensing, LLC

Inventor： Manikanta KOTARU ,  Paramvir BAHL ,  Arjun Varman BALASINGAM

IPC: H04W72/12 ,  H04W72/04

Abstract: In a 5G network, a slice controller operating in a radio access network (RAN) is arranged to make predictions of channel state information (CSI) for user equipment (UE) on the network using a predictive propagation model. The slice controller uses the predicted CSI to schedule subcarriers and time slots associated with physical radio resources for data transmission on slices of the 5G network between a 5G radio unit (RU) and the UE to maximize network throughput on a slice for the radio spectrum that is utilized for a given time period. In view of the CSI predictions, the slice controller controls operations of the MAC (Medium Access Control) layer functions based on PHY (physical) layer radio resource subsets to schedule the subcarrier and time slots for data transmissions on a slice over the 5G air interface from RU to UE.

公开(公告)号：US20220377597A1

公开(公告)日：2022-11-24

申请号：US17325165

申请日：2021-05-19

Applicant: Microsoft Technology Licensing, LLC

Inventor： Manikanta KOTARU ,  Paramvir BAHL ,  Arjun Varman BALASINGAM

IPC: H04W28/02 ,  H04W72/04 ,  H04W48/18 ,  H04W24/02

Abstract: In a 5G network, a slice controller is arranged to dynamically configure a radio access network (RAN) by allocating physical radio resources into RAN slices by making predictions of channel state information (CSI) for user equipment (UE) executing applications that make connectivity requests for admission to particular identified slices. The slice controller grants or denies admission requests based on the predicted CSI to ensure that applicable service level agreement (SLA) guarantees are satisfied for traffic across all the RAN slices. Each time new admission requests are received from applications, the slice controller determines whether a suitable RAN configuration exists that will enable SLA guarantees for the slices to continue to be satisfied for the current traffic while also meeting the SLA guarantees applicable to the new admission request.

公开(公告)号：US20240422813A1

公开(公告)日：2024-12-19

申请号：US18335700

申请日：2023-06-15

Applicant: Microsoft Technology Licensing, LLC

Inventor： Manikanta KOTARU ,  Arjun Varman BALASINGAM ,  Paramvir BAHL

IPC: H04W72/541 ,  H04L27/26 ,  H04W24/02 ,  H04W72/0457 ,  H04W72/542

Abstract: The present optimization of guard bands repurposes some guard band spectrum for data transmission in a 5G network. This approach takes spectrum that is otherwise “wasted” for guard bands to enable overall spectrum utilization to be increased. To mitigate effects of inter-numerology interference (INI) with narrower guard band bandwidth, physical resource blocks (PRBs) for particular user equipment (UE) are allocated to BWPs that are modified with increased bandwidth that comes from narrowing the guard band bandwidth. These particular UEs have high signal strength, for example, as characterized by SINR (signal-to-interference plus noise ratio), relative to other UE. Allocating PRBs for high signal strength UE in BWPs near the edges of the narrower guard band increases the risk of INI, but the higher signal strength for these UE helps to lessen the INI impact and enable overall throughput for all users to be maximized in the network.

公开(公告)号：US20230388851A1

公开(公告)日：2023-11-30

申请号：US18202041

申请日：2023-05-25

Applicant: Microsoft Technology Licensing, LLC

Inventor： Manikanta KOTARU ,  Arjun Varman BALASINGAM ,  Paramvir BAHL

IPC: H04W28/06 ,  H04W28/08

CPC classification number: H04W28/06 ,  H04W28/0942

Abstract: Aspects of the present disclosure relate to allocating RAN resources among RAN slices using a machine learning model. In examples, the machine learning model may determine an optimal RAN resource configuration based on compute power needs. As a result, RAN resource allocation generation and compute power requirements may improve, even in instances with changing or unknown network conditions. In examples, a prediction engine may receive communication parameters and/or requirements associated with service-level agreements (SLAs) for applications executing at least partially at a device in communication with the RAN. The RAN may generate one or more RAN resource configuration for implementation among RAN slices. Upon a change in network conditions or SLA requirements, an optimal RAN configuration may be determined in terms of required compute power.

公开(公告)号：US20230090021A1

公开(公告)日：2023-03-23

申请号：US18071386

申请日：2022-11-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Manikanta KOTARU ,  Paramvir BAHL ,  Arjun Varman BALASINGAM

IPC: H04W72/12 ,  H04W72/04

Abstract: In a 5G network, a slice controller operating in a radio access network (RAN) is arranged to make predictions of channel state information (CSI) for user equipment (UE) on the network using a predictive propagation model. The slice controller uses the predicted CSI to schedule subcarriers and time slots associated with physical radio resources for data transmission on slices of the 5G network between a 5G radio unit (RU) and the UE to maximize network throughput on a slice for the radio spectrum that is utilized for a given time period. In view of the CSI predictions, the slice controller controls operations of the MAC (Medium Access Control) layer functions based on PHY (physical) layer radio resource subsets to schedule the subcarrier and time slots for data transmissions on a slice over the 5G air interface from RU to UE.