Microsoft Technology Licensing, LLC

Yu Yan

Paramvir Bahl

Ganesh Ananthanarayanan

Landon Prentice Cox

Sanjeev Mehrotra

Manikanta Kotaru

Ryan Andrew Beckett

Anuj Kalia

Martin Ellis

Yuanchao Shu

Shadi Abdollahian Noghabi

Michael Harrison Rudow

Neil Agarwal

Aspects of the present disclosure relate to determining reference symbol transmission times. In some examples, a method for determining reference symbol transmission times for cellular communications includes receiving signal feedback based on a wireless communication channel between a wireless communication device and a base station, identifying a periodic exchange of reference symbols that are used to adjust beamforming between the wireless communication device and the base station, generating a vector based on the signal feedback, and providing the vector as an input to a trained machine learning model. A training of the trained machine learning model includes calculating a plurality of rewards for a respective plurality of transmission time delays. The plurality of rewards are each calculated based on a function of downlink throughput and uplink overhead. The function of downlink throughput and uplink overhead are based upon a priority level of the wireless communication device.

Determining reference signal transmission times

A method for utilizing computing resources in a vRAN is described. A predicted resource load is determined for data traffic processing of wireless communication channels served by the vRAN using a trained neural network model. The data traffic processing comprises at least one of PHY data processing or MAC processing for a 5G RAN. Computing resources are allocated for the data traffic processing based on the predicted resource load. Wireless parameter limits are determined for the wireless communication channels that constrain utilization of the allocated computing resources using the trained neural network model, including setting one or more of a maximum number of radio resource units per timeslot or a maximum MCS index for the wireless parameter limits. The data traffic processing is performed using the wireless parameter limits to reduce load spikes that cause a violation of real-time deadlines for the data traffic processing.

Wireless parameter limits for predicted vRAN resource loads

Example implementations include a method, apparatus, and computer-readable medium configured for generating a network configuration using a large language model (LLM). The apparatus receives, at an interface between a user and LLM, a natural language intent for a network configuration. The apparatus requests the large language model to update the network configuration to an updated network configuration that satisfies the natural language intent in a declarative network configuration language. The apparatus verifies whether the updated network configuration satisfies a configuration syntax of the declarative network configuration language to detect an error. The apparatus requests the large language model to update the updated network configuration to correct the error. The apparatus deploys the updated network configuration to a user network.

Artificial intelligence for intent-based networking

Systems and methods are provided for reducing stream data according to a data streaming protocol under a multi-access edge computing. In particular, an IoT device, such as a video image sensing device, may capture stream data and generate inference data by applying a machine-learning model trained to infer data based on the captured stream data. The inference data represents the captured stream data in a reduced data size based on performing data analytics on the captured data. The IoT device formats the inference data according to the data streaming protocol. In contrast to video data compression, the data streaming protocol includes instructions for transmitting the reduced volume of inference data through a data analytics pipeline.

Data streaming protocols in edge computing

A method for data stream prioritization by a session controller is described. Usage data associated with a video communication session is received for one or more client devices of the video communication session. The usage data is based on content within data streams of the video communication session. A first client device of the one or more client devices is identified as having a higher priority level during the video communication session based on the usage data. Instructions are sent to the first client device during the video communication session causing the first client device to improve a quality of a first data stream generated by the first client device for the video communication session.

Data stream prioritization for communication session

A method for communication session management by a session controller is described. Usage data associated with a video communication session is received for data stream handlers of a first network topology, which handle data streams of the video communication session at a first time. The first network topology includes a plurality of client devices and at least a first media server. A second network topology is determined based on the usage data to handle the data streams when a network parameter and/or an application parameter reaches a corresponding update threshold. Data stream handlers of the second network topology include at least a second media server. The data stream handlers of the second network topology are configured to handle the data streams at a second time, including instructing the first media server to offload at least some of the data streams to the second media server.

Media server management for communication session

Identifying, by a sender and for each frame i of a plurality of frames of a video stream, a partition of a set of video data symbols D[i] into a first set of video data symbols U[i] and a second set of video data symbols V[i]. Generating, by the sender and for each frame i, a set of one or more streaming forward error correction (FEC) code parity symbols P[i] based on the symbols: V[i&#x2212;&#x3c4;] through V[i&#x2212;1], U[i&#x2212;&#x3c4;], and the symbols D[i], wherein &#x3c4; is a function of a maximum tolerable latency of the video stream expressed as a whole number of frames. Encoding, by the sender and for each frame i, packets carrying the symbols D[i], and P[i]. Transmitting, by the sender, each frame i of encoded packets in frame order to one or more receivers.

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Bellevue, WA, United States of America

Yu Yan