Microsoft Technology Licensing, LLC

Other

Arjun Varman Balasingam

Paramvir Bahl

Manikanta Kotaru

James M Janky

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.

Calculating and exposing network capacity and congestion to applications

Aspects include a machine learning based resource block scheduler configured to meet service level requirements of applications. Aspects include receiving a plurality of scheduling requests each associated with a respective application of a plurality of applications on a plurality of wireless devices, identifying a plurality of current channel state information each associated with one of the plurality of wireless devices, and identifying a plurality of different types of service level requirements each associated with one of the plurality of applications. Further, the aspects include determining, by a machine learning based scheduler based on each of the plurality of current channel state information, a sequence of resource assignments expected to meet the plurality of different types of service level requirements, the sequence of resource assignments including a plurality of grants of a scheduled assignment of a resource, and transmitting respective grants for the respective applications to the wireless devices.

Learned scheduler for flexible radio resource allocation to applications

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.

Dynamic 5G network slicing to maximize spectrum utilization

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.

5G admission by verifying slice SLA guarantees

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.

Compute-aware resource configurations for a radio access network

Improving wind-based piezoelectric power conversion is provided. For example, a piezoelectric element affixed to a vibratory member is provided. A rigid mounting system coupled with a rotatable base is provided for said vibratory member on one end of the vibratory member. A solar generator is coupled with the rigid mounting system and at least one obstacle is provided located on the flexing side of the vibratory member. The obstacle induces a vortex in the wind passing the obstacle and arriving at the vibratory member, which enhances wind-induced displacement in the vibratory member.

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San Jose, CA, United States of America

Arjun Varman Balasingam