Abstract: A distributed unit (DU) in a virtual radio access network (vRAN) of a wireless 5G (fifth generation) network is implemented using software-defined networking (SDN) technology on a single computer server that is physically co-located with a radio unit in a distributed RAN (D-RAN) architecture. An update agent facilitates versioning support through interoperations with different DU versions, v1 and v2, to prevent them from concurrently executing real-time processing threads on the server's processor which would cause malfunctions. DU v2 is initialized using non-real-time threads. The update agent blocks real-time processing thread generation by DU v2 after initialization is complete. The update agent sets DU v1 processing threads to non-real-time and unblocks DU v2 to migrate data traffic between the versions. A layer 2 switch provides duplicated data packets to virtual network interface cards that are implemented for the respective DU versions and which share a common MAC (medium access control) address.

Abstract: Data traffic is communicated between a radio unit (RU) of a cellular network and a virtualized radio access network (vRAN) instance of a vRAN. In response to determining that the vRAN instance has failed to communicate a downlink fronthaul packet to the RU within a threshold timeout interval, a failure notification is sent to a PHY layer failure response function. The failure to communicate the downlink fronthaul packet to the RU within the threshold timeout interval is indicative of a failure of the vRAN instance.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for increasing resiliency in distributed units of a virtual radio access network (vRAN) of a telecommunications network (e.g., a 5G telecommunications network), particularly when performing a planned handover operation between the distributed units. The examples described herein specifically relate to implementing an inter-distributed unit handover between distributed units that are serviced by the same radio unit. In some examples, this handover is initiated by a middlebox entity that is positioned between the distributed units and a centralized unit. In some instances, features of the middlebox entity are implemented within the framework of a centralized unit. By allowing a quick handover as described herein, the distributed units can provide uninterrupted service to a UE while allowing the distributed units to perform various upgrades or modifications to the distributed unit without service interruptions.

Abstract: 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.

Abstract: 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.

Abstract: Methods and systems for dynamically re-routing layer traffic between different servers with little user-visible disruption and without modifications to the vRAN software stack are provided. This approach enables operators to initiate a PHY migration either on demand (e.g., during planned maintenances) or to set up automatic migration on unexpected events (e.g., server failures). It is recognized that PHY processing in cellular networks has no hard state that must be migrated. As a result, layer traffic such as the PHY-L2 traffic or L2-PHY traffic can be simply re-routed to a different server. This re-routing mechanism is realized by interposing one or more message controllers (e.g., middlebox) in a communication channel between the PHY and L2.

Abstract: The techniques disclosed herein manage computing environments associated with radio access networks using a natural language interface. This is achieved through utilizing natural language processing to analyze user generated inputs and generate robust large language model queries. In various examples, the queries can include radio access network documentation, diagnostic data, and past interactions to provide custom context to the large language model. Accordingly, the query can cause the large language model to generate an operation sequence comprising a plurality of commands to interface with a resource management tool and control computing resources and supporting components. In this way, the present techniques can alleviate the technical burden on end users and minimize the risk of errors.

Abstract: 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.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for increasing resiliency in distributed units of a virtual radio access network (vRAN) of a telecommunications network (e.g., a 5G telecommunications network). Systems described herein involve establishing, using a middlebox entity, stream control transmission protocol (SCTP) connections between each of multiple distributed units and a centralized unit. The middlebox entity may monitor failure conditions to detect a failure condition of a primary distributed unit and, based on the detected failure condition, quickly re-establish a connection between a backup distributed unit having the SCTP connection previously established with the centralized unit.

Abstract: Methods and apparatuses for improving the performance and energy efficiency of Radio Access Networks (RANs) are described. Various power control schemes may dynamically adjust RAN power consumption based on fluctuations in network traffic, throughput, latency, queue sizes, and/or packet error rates with the goal of increasing energy efficiency while maintaining quality of service metrics. The power control schemes may be implemented using a PRB controller for dynamically allocating physical resource blocks (PRBs) to user devices and a CPU controller for assigning CPU power profiles based on PRB allocations for the user devices. The PRB controller and CPU controller may periodically acquire real-time telemetry data and wireless network performance information and then adjust the number of PRBs for user devices and adjust the CPU power profiles for executing RAN functions based on the telemetry data and wireless network performance information.

Abstract: Methods and apparatuses for improving the performance and energy efficiency of Radio Access Networks (RANs) are described. Various power control schemes may dynamically adjust RAN power consumption based on fluctuations in network traffic, throughput, latency, queue sizes, and/or packet error rates with the goal of increasing energy efficiency while maintaining quality of service metrics. The power control schemes may be implemented using a PRB controller for dynamically allocating physical resource blocks (PRBs) to user devices and a CPU controller for assigning CPU power profiles based on PRB allocations for the user devices. The PRB controller and CPU controller may periodically acquire real-time telemetry data and wireless network performance information and then adjust the number of PRBs for user devices and adjust the CPU power profiles for executing RAN functions based on the telemetry data and wireless network performance information.

Abstract: A fifth generation (5G) mobile network radio access network (RAN) is virtualized for operations on edge computing platforms in a cloud-computing environment in which radio units (RUs) and radio frequency (RF) spectrum are shared among distributed units (DUs) to support use cases including: 1) live migration in which a DU is moved from one computing server to another without disruption to network traffic, and 2) RAN sharing in which two DUs share the same RU and spectrum.

Abstract: Described are examples for providing a distributed fault-tolerant state store for a virtualized base station. In an aspect, a first server at a datacenter may perform physical layer processing for at least one virtualized base station. While performing the physical layer processing, the first server may generate inter-slot physical layer state data during a first slot. The inter-slot physical layer state data is to be used in a subsequent slot. The first server may periodically transmit the inter-slot physical layer state data to one or more other servers of the plurality of servers within the datacenter. One of the other servers may take over the physical layer processing for the at least one virtualized base station based on the inter-slot physical layer state data, for example, in response to a fault at the first server or a migration of the at least one virtualized base station.

Abstract: Described are examples for deploying workloads in a cloud-computing environment. In an aspect, based on a desired number of workloads of a process to be executed in a cloud-computing environment and based on one or more failure probabilities, an actual number of workloads of the process to execute in the cloud-computing environment to provide a level of service can be determined and deployed. In another aspect, a standby workload can be executed as a second instance of the process without at least a portion of the separate configuration used by the multiple workloads, and based on detecting termination of one of multiple workloads, the standby workload can be configured to execute based on the separate configuration of the separate instance of the process corresponding to the one of the multiple workloads.

Abstract: Methods and systems for dynamically re-routing layer traffic between different servers with little user-visible disruption and without modifications to the vRAN software stack are provided. For instance, transformations on messages between the L2 and PHY, such as duplication and filtering, enable the system to maintain one or more low-overhead “hot, inactive” PHY clones. A hot, inactive PHY clone may be a duplicate of an operational PHY, where the PHY clone is primed to process a PHY workload of the operational PHY (e.g., “hot”) but is not currently responsible for processing the PHY workload (e.g., low-overhead, inactive). In this way, a PHY workload may be automatically and seamlessly migrated to the hot PHY clone in response to planned downtime (e.g., scheduled maintenance, software upgrades) or unexpected events (e.g., server failures) within the strict transmission time intervals (TTIs) required for processing the PHY workload.

Abstract: Data traffic is communicated between a radio unit (RU) of a cellular network and a virtualized radio access network (vRAN) instance of a vRAN. In response to determining that the vRAN instance has failed to communicate a downlink fronthaul packet to the RU within a threshold timeout interval, a failure notification is sent to a PHY layer failure response function. The failure to communicate the downlink fronthaul packet to the RU within the threshold timeout interval is indicative of a failure of the vRAN instance.

Abstract: 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.

Abstract: Methods and systems for dynamically re-routing layer traffic between different servers with little user-visible disruption and without modifications to the vRAN software stack are provided. This approach enables operators to initiate a PHY migration either on demand (e.g., during planned maintenances) or to set up automatic migration on unexpected events (e.g., server failures). It is recognized that PHY processing in cellular networks has no hard state that must be migrated. As a result, layer traffic such as the PHY-L2 traffic or L2-PHY traffic can be simply re-routed to a different server. This re-routing mechanism is realized by interposing one or more message controllers (e.g., middlebox) in a communication channel between the PHY and L2.

Abstract: 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.

Abstract: Described are examples for deploying workloads in a cloud-computing environment. In an aspect, based on a desired number of workloads of a process to be executed in a cloud-computing environment and based on one or more failure probabilities, an actual number of workloads of the process to execute in the cloud-computing environment to provide a level of service can be determined and deployed. In another aspect, a standby workload can be executed as a second instance of the process without at least a portion of the separate configuration used by the multiple workloads, and based on detecting termination of one of multiple workloads, the standby workload can be configured to execute based on the separate configuration of the separate instance of the process corresponding to the one of the multiple workloads.