Abstract: Based on a load prediction analysis performed by a machine learning enabled processor and a load balancer, the load of multiple distributed unit (DU) resources in a baseband unit (BBU) pool hub can be optimized. The BBU pool hub can comprise multiple DU functionality and redundant centralized unit (CU) functionality connected via a high-speed/low latency redundant switch to enable pooling of resources. DU resource pooling can facilitate efficiencies in the network by allocating resources based on active and/or inactive status, load, of radio units RUs as well as Radio Bearers activity.

Abstract: Based on a load prediction analysis performed by a machine learning enabled processor and a load balancer, the load of multiple distributed unit (DU) resources in a baseband unit (BBU) pool hub can be optimized. The BBU pool hub can comprise multiple DU functionality and redundant centralized unit (CU) functionality connected via a high-speed/low latency redundant switch to enable pooling of resources. DU resource pooling can facilitate efficiencies in the network by allocating resources based on active and/or inactive status, load, of radio units RUs as well Radio Bearers activity.

Abstract: Based on a load prediction analysis performed by a machine learning enabled processor and a load balancer, the load of multiple distributed unit (DU) resources in a baseband unit (BBU) pool hub can be optimized. The BBU pool hub can comprise multiple DU functionality and redundant centralized unit (CU) functionality connected via a high-speed/low latency redundant switch to enable pooling of resources. DU resource pooling can facilitate efficiencies in the network by allocating resources based on active and/or inactive status, load, of radio units RUs as well Radio Bearers activity.

Abstract: System(s) and method(s) are provided for managing network capacity in a wireless network that serves various traffic flows with disparate quality of service requirements. Management can be based on multi-stage scheduling in frequency-time domain. A first scheduling stage can generate an allocation of radio resources that minimizes inter-cell interference amongst a plurality of base stations. Based on the first-stage allocation of radio resources, a second scheduling stage can compute flow capacity for a set of radio resources specific to a base station, and acquire guaranteed-bit-rate (GBR) and non-guaranteed GBR traffic flows. GBR traffic flows can be matched to computed flow capacity to generate an allocation of frequency-time resources. In second scheduling stage, GBR traffic flows can be scheduled based at least in part on allocated resources and priority queuing.

Abstract: System(s) and method(s) are provided for managing network capacity in a wireless network that serves various traffic flows with disparate quality of service requirements. Management is based on multi-stage scheduling in frequency-time domain. A first scheduling stage generates an allocation of radio resources that minimizes inter-cell interference amongst a plurality of base stations. Based on the first-stage allocation of radio resources, a second scheduling stage computes flow capacity for a set of radio resources specific to a base station, and acquires guaranteed-bit-rate (GBR) and non-guaranteed GBR traffic flows. GBR traffic flows are matched to computed flow capacity to generate an allocation of frequency-time resources. In second scheduling stage, GBR traffic flows are scheduled based at least in part on allocated resources and priority queuing.

Abstract: System(s) and method(s) are provided for managing network capacity in a wireless network that serves various traffic flows with disparate quality of service requirements. Management can be based on multi-stage scheduling in frequency-time domain. A first scheduling stage can generate an allocation of radio resources that minimizes inter-cell interference amongst a plurality of base stations. Based on the first-stage allocation of radio resources, a second scheduling stage can compute flow capacity for a set of radio resources specific to a base station, and acquire guaranteed-bit-rate (GBR) and non-guaranteed GBR traffic flows. GBR traffic flows can be matched to computed flow capacity to generate an allocation of frequency-time resources. In second scheduling stage, GBR traffic flows can be scheduled based at least in part on allocated resources and priority queuing.

Abstract: Systems and methods are described that provide network traffic engineering that obviate network over-provisioning by providing QoS to each traffic class. Embodiments dimension switching router LTE schedulers to ensure that each traffic class receives an appropriate QoS in terms of delay, jitter, Packet Loss Ratio and throughput. In addition to guaranteeing QoS, embodiments optimize transport cost, optimize switch-router port deployment, and work on top of IETF standards, IEEE standards, and MEF standards.

Abstract: System(s) and method(s) are provided for managing network capacity in a wireless network that serves various traffic flows with disparate quality of service requirements. Management is based on multi-stage scheduling in frequency-time domain. A first scheduling stage generates an allocation of radio resources that minimizes inter-cell interference amongst a plurality of base stations. Based on the first-stage allocation of radio resources, a second scheduling stage computes flow capacity for a set of radio resources specific to a base station, and acquires guaranteed-bit-rate (GBR) and non-guaranteed GBR traffic flows. GBR traffic flows are matched to computed flow capacity to generate an allocation of frequency-time resources. In second scheduling stage, GBR traffic flows are scheduled based at least in part on allocated resources and priority queuing.

Abstract: Systems and methods are described that provide network traffic engineering that obviate network over-provisioning by providing QoS to each traffic class. Embodiments dimension switching router LTE schedulers to ensure that each traffic class receives an appropriate QoS in terms of delay, jitter, Packet Loss Ratio and throughput.

Abstract: Systems and methods are described that provide network traffic engineering that obviate network over-provisioning by providing QoS to each traffic class. Embodiments dimension switching router LTE schedulers to ensure that each traffic class receives an appropriate QoS in terms of delay, jitter, Packet Loss Ratio and throughput.

Abstract: Systems and methods are described that provide network traffic engineering that obviate network over-provisioning by providing QoS to each traffic class. Embodiments dimension switching router LTE schedulers to ensure that each traffic class receives an appropriate QoS in terms of delay, jitter, Packet Loss Ratio and throughput.