Microsoft Technology Licensing, LLC

Xenofon Foukas

Bozidar Radunovic

Sanjeev Mehrotra

Yongguang Zhang

Paramvir Bahl

Landon Prentice Cox

Daehyeok Kim

Anuj Kalia

Jincao Zhu

Ganesh Ananthanarayanan

Manikanta Kotaru

Arun Bhamidimarri

Zhehui Zhang

Atul Bansal

The systems and methods relate to virtual radio access networks (vRANs). The systems and methods may offload a signal processing task of a physical layer from a vRAN server located at the far edge of a network nearby a base station to a remote location further away from the base station. The remote location may include higher level edge deployments of servers or a cloud deployment of servers. The system and methods may scale the vRAN server capacity by offloading the signal processing task to the remote location without compromising quality of service requirements or latency requirements of the user equipment or the applications.

Virtualized radio access network (vRAN) decoding as a service

During a first transmission time interval (TTI) of a vRAN, data traffic between a radio unit (RU) of a cellular network and a first vRAN instance of the vRAN is monitored. The first vRAN instance executes on a first server of the vRAN and the first vRAN instance is configured to perform PHY layer processing and L2 processing of the data traffic. Based on the data traffic between the RU of the cellular network and the first vRAN instance during the first TTI, a workload at the first vRAN instance during a second TTI is estimated.

Load estimation and balancing in virtualized radio access networks

Described are examples for providing radio access network (RAN) analytics for a virtualized base station. An analytics engine includes a memory storing one or more parameters or instructions for operating the virtualized RAN and at least one processor coupled to the memory. The analytics engine is configured to perform multiple protocol layers of RAN processing for at least one cell at the virtualized base station. The analytics engine is configured to determine a time series of real-time metrics at two or more layers of the multiple protocol layers for the at least one cell or a user equipment (UE) connected to the at least one cell. The analytics engine is configured to correlate a time series for each of the two or more layers to detect a network condition. The analytics engine is configured to modify a configuration of the at least one cell based on the detected network condition.

Real-time radio access network analytics

A real-time radio intelligent controller (RIC) executes in parallel with one or more virtual radio access network functions to provide real-time analytics and control of the virtual radio access network functions. At least a first processor core is configured to execute a radio network virtual function. The radio network virtual function is configured with a codelet to output selected operational data to a first stream associated with a first stream ID and receive control information from a control stream associated with a second stream ID. At least a second processor core is configured to execute the real-time RIC isolated from the at least the first processor core. The real-time RIC includes one or more dynamically loaded programs configured to: access the first stream; perform processing on the operational data; and write commands for the radio network virtual function to the control stream.

Real-time radio intelligent controller

Described are examples for monitoring performance metrics of one or more workloads in a cloud-computing environment and reallocating compute resources based on the monitoring. Reallocating compute resources can include migrating workloads among nodes or other resources in the cloud-computing environment, reallocating hardware accelerator resources, adjusting transmit power for virtual radio access network (vRAN) workloads, and/or the like.

Techniques for adaptively allocating resources in a cloud-computing environment

The present disclosure relates to systems and methods for sharing compute resources. The systems and methods may include identifying a plurality of workloads to complete by a deadline. The systems and methods may include generating a performance prediction for each workload of the plurality of workloads. The systems and methods may use the performance prediction to calculate a number of compute resources required for the plurality of workloads to complete by the deadline. The systems and methods may schedule the plurality of workloads across the number of compute resources.

Sharing of compute resources between the virtualized radio access network (vRAN) and other workloads

Techniques are disclosed for dynamically adjusting associations between radio units (RUs) and a virtualized radio access network (vRAN) by a virtual translational layer running in a controller of the vRAN. Each of the RUs have a number of antennas and are configured to service a cell of a cellular communications network. Based on performance metrics, the virtual translational layer maps the RUs to a virtualized cell of the cellular communications network, the virtualized cell including a mapped selection of the RUs.

Virtualized cells with multiple antennas in a virtual Radio Access Network

To meet the stringent 5G radio access network (RAN) service requirements, layers one and two need to be processed in essentially real time. Thus, prompt anomaly detection is important to prevent negative impacts on customer experience, which is critical for mobile networks to meet the stringent service requirements. However, monitoring networks for anomalies is difficult due at least to (1) the resource constrained edge deployments in which the vRAN resides, (2) the variety of anomaly types and fault locations making anomalies difficult to detect, and (3) the low frequency of anomalies leading to unbalanced data sets for training, among others. The present application addresses these issues by decoupling anomaly detection at the infrastructure layer (servers, NICs, switches, etc.) from anomaly detection at the VNF layer (L1, high-DU, CU). This enables different techniques for identifying anomalies and for reducing the monitoring overhead that is tailored to each layer.

Anomaly detection for virtualized rans

Described are examples for providing fine-grained real-time pre-emption of codelets based on a runtime threshold. A controller inserts checkpoints into extended Berkeley packet filter (eBPF) bytecode of a third-party codelet prior to verification of the third-party codelet. A device executes the codelet at a hook point of an application. The inserted checkpoints determine a runtime of the codelet. The device terminates the codelet in response to the runtime exceeding a threshold. The application can be a virtualized radio access network (vRAN) network function and the codelet can control the vRAN function or export network metrics. The application may be executed in a container management system that modifies a container for the application to mount code including a function associated with the hook point of the application to the container; detect an annotation for the container that identifies the codelet; and symbolically links the codelet to the hook point.

Fine-grained real-time pre-emption of codelets based on runtime threshold

Described are examples for receiving, from one or more second virtual radio access network (vRAN) workloads operating one or more second cells, an indication of a measurement of at least a first signal transmitted by a first vRAN workload operating a first cell, computing, based on measurements of at least the first signal as received from the one or more second vRAN workloads, a boundary of the first cell, and adjusting, based on the boundary of the first cell, a transmit parameter of the first vRAN workload for transmitting signals in the first cell.

Techniques for adaptively determining cell boundary in wireless communications

Described are examples for using codelets executing within applications to use machine-learning (ML) models to infer a result based on application data. The codelets may be dynamically loaded into the applications during execution. A controller verifies, based on extended Berkeley packet filter (eBPF) bytecode of the codelet, that the codelet satisfies safety requirements for execution within the application. A computing device executing the application loads the verified codelet into a library of the application. The application executes the verified codelet to apply application data to the machine-learning model to infer a result. The ML model may be implemented by the eBPF code of the codelet or the codelet may include a call to a machine-learning model of a type supported by a controller of the application and a map for a serial representation of the machine-learning model. The computing device may reconstruct the ML model based on the serial representation.

Inference with inline real-time ML models in applications

A method for adjusting discontinuous reception (DRX) behavior of a user equipment (UE) to conserve energy use includes exposing a DRX application programming interface (API) that enables DRX parameters to be changed and defining a conflict resolution policy that controls when requests to change the DRX parameters should be granted. The method also includes receiving, via the DRX API, a request from an application to change a DRX parameter for the UE. The UE is in wireless communication with a base station, and the application is configured to send data to the UE via a mobile network that comprises the base station. The method also includes determining, based at least in part on the conflict resolution policy, that the request should be granted and sending a command to the base station that causes the base station to communicate a new value of the DRX parameter to the UE.

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Cambridge, United Kingdom

Xenofon Foukas