System, method, and apparatus for providing optimized network resources
Systems, methods, and apparatuses for providing optimization of network resources. The system is operable to monitor the electromagnetic environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. The system extracts the environmental awareness of the electromagnetic environment by including customer goals. The system is operable to use the environmental awareness with the customer goals and/or user defined policies and rules to extract actionable information to help the customer optimize the network resources.
1 . A system for spectrum utilization management in a wireless network comprising:
a Multi-Access Edge Computing (MEC) layer in a network slice or subnetwork; and
a wireless network resource optimization application in the MEC layer;
wherein the MEC layer is in communication with at least one network;
wherein the wireless network resource optimization application is configured to create actionable data for optimizing network resources of the wireless network;
wherein the wireless network resource optimization application is configured to create a customer goals index vector of binary values based on at least one rule, at least one policy, and/or the actionable data;
wherein each binary value of the customer goals index vector represents whether or not a specific piece of measured electromagnetic environment data is relevant to satisfying customer goals;
a machine learning (ML) engine, wherein the ML engine identifies relevant information required by at least one customer application; and
wherein the network resources of the wireless network are optimized based on the actionable data, the relevant information required by the at least one customer application, and the customer goals index vector.
2 . The system of claim 1 , wherein the network resources are optimized based on reconfiguring the MEC layer.
3 . The system of claim 1 , wherein the network slice is occupied by at least two tenants.
4 . The system of claim 1 , wherein the MEC layer is configured to provide for optimization of physical layer resources for applications based on environmental conditions.
5 . The system of claim 1 , wherein the MEC layer supports cloud computing for the network slice.
6 . The system of claim 1 , wherein the network resources of the wireless network are further optimized based on quality of service (QOS) required by the at least one customer application.
7 . The system of claim 1 , further comprising a Network Slice Selection Function (NSSF) configured to select suitable network slice instances for at least one user equipment (UE) and/or applications.
8 . The system of claim 1 , wherein the at least one rule and/or the at least one policy is defined by at least one customer goal.
9 . The system of claim 1 , wherein the at least one network is a radio access network (RAN).
10 . A system for spectrum utilization management in a wireless network comprising:
a Multi-Access Edge Computing (MEC) layer in a network slice or subnetwork; and
a wireless network resource optimization application in the MEC layer;
wherein the MEC layer is in communication with at least one network;
wherein the wireless network resource optimization application is configured to create actionable data for optimizing network resources;
wherein the wireless network resource optimization application is configured to create a customer goals index vector of binary values based on the actionable data;
wherein each binary value of the customer goals index vector represents whether or not a specific piece of measured electromagnetic environment data is relevant to satisfying customer goals;
a machine learning (ML) engine, wherein the ML engine identifies relevant information required by at least one customer application; and
wherein the network resources of the wireless network are optimized based on the actionable data, the relevant information required by the at least one customer application from the ML engine, and the customer goals index vector.
11 . The system of claim 10 , wherein the MEC layer is configured to provide for optimization of physical layer resources for applications based on environmental conditions.
12 . The system of claim 10 , wherein the MEC layer supports cloud computing for the network slice.
13 . The system of claim 10 , wherein slicing architecture of the MEC layer is based on a plurality of factors including geographic scope, specificity of services, flexible architecture, and/or implementation of MEC applications as part of a slice access point.
14 . The system of claim 10 , wherein the at least one network is a radio access network (RAN).
15 . The system of claim 10 , wherein the system is configured to perform network slicing to create the network slice based on a network function virtualization (NFV) management and orchestration (MANO) architecture.
16 . The system of claim 10 , wherein the network resources of the wireless network are further optimized based on quality of service (QoS) required by the at least one customer application.
17 . A method for spectrum utilization management in a wireless network comprising:
a wireless network resource optimization application creating actionable data and creating a customer goals index vector of binary values based on the actionable data;
a machine learning (ML) engine identifying relevant information required by at least one customer application based on the customer goals index vector; and
the wireless network resource optimization application optimizing network resources of the wireless network based on the actionable data, the relevant information required by the at least one customer application from the ML engine, and the customer goals index vector;
wherein each binary value of the customer goals index vector represents whether or not a specific piece of measured electromagnetic environment data is relevant to satisfying customer goals;
wherein a Multi-Access Edge Computing (MEC) layer is in communication with at least one network or subnetwork; and
wherein the wireless network resource optimization application is in the MEC layer.
18 . The method of claim 17 , wherein the at least one network is a radio access network (RAN).
19 . The method of claim 17 , wherein the MEC layer is providing traffic steering to route network traffic to a network slice.
20 . The method of claim 19 , further comprising performing network slicing to create the network slice based on a network function virtualization (NFV) management and orchestration (MANO) architecture.