Abstract: 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.

Abstract: 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.

Abstract: Aspects of the present disclosure relate to allocating RAN resources among RAN slices according to reinforcement learning techniques. For example, a network slice controller (NSC) may generate a RAN resource allocation and associated expected slice characteristics may be determined for each slice based on the RAN resource allocation. Resources of the RAN may be allocated accordingly, such that resulting actual slice characteristics may be observed and compared to the expected slice characteristics. A reward may be generated for the resource allocation, for example based on a difference between the expected and observed slice characteristics. RAN resource allocation and slice characteristic forecasting may be adapted according to such rewards. As a result, RAN resource allocation generation may improve, even in instances with changing or unknown network conditions.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.