US 11,601,830 B2 · Systems and methods for autonomous network management using deep reinforcement learning

Abstract

A system described herein may provide a technique for analyzing metrics, parameters, attributes, and/or other information associated with networks or other devices or systems associated with high-dimensional data in order to determine potential configuration changes that may be made to such networks or other devices or systems in order to optimize and/or otherwise enhance the operation of such networks or other devices or systems. Multiple autoencoders associated with multiple dimensions may be used to calculate reconstruction errors or other features of data (e.g., metrics, parameters, etc.) that may be used to define operating or performance states of the network. Operating or performance states of network components may be mapped to quantum state objects (“QSOs”) for analysis using artificial intelligence and/or machine learning techniques or other suitable techniques.

Claims20

Dependent claims

A device, comprising:

one or more processors configured to:
receive network parameters associated with a plurality of carriers associated with a wireless network;
encode the received network parameters using a plurality of different sets of encoding parameters to generate a plurality of sets of encoded network parameters, wherein each set of encoded network parameters corresponds to a particular set of encoding parameters of the plurality of sets of encoding parameters;
decode the plurality of sets of encoded network parameters to generate a plurality of sets of decoded network parameters;
calculate reconstruction errors based on differences between the received network parameters and the decoded network parameters;
and
determine one or more modifications to one or more network devices of the wireless network, the one or more network devices being associated with one or more of the plurality of carriers, based on the calculated reconstruction errors.

4 more elements

A non-transitory computer-readable medium, storing a plurality of processor-executable instructions to:

receive network parameters associated with a plurality of carriers associated with a wireless network;
encode the received network parameters using a plurality of different sets of encoding parameters to generate a plurality of sets of encoded network parameters, wherein each set of encoded network parameters corresponds to a particular set of encoding parameters of the plurality of sets of encoding parameters;
decode the plurality of sets of encoded network parameters to generate a plurality of sets of decoded network parameters;
calculate reconstruction errors based on differences between the received network parameters and the decoded network parameters;
and
determine one or more modifications to one or more network devices of the wireless network, the one or more network devices being associated with one or more of the plurality of carriers, based on the calculated reconstruction errors.

3 more elements

Dependencies are inferred from the claim text.

Systems and methods for autonomous network management using deep reinforcement learning

Life of this patent

Systems and methods for autonomous network management using deep reinforcement learning

Life of this patent

Abstract

Claims20