Machine-learning framework for spectrum allocation

A method for predicting spectrum allocation in a wireless access network, the method comprising:

• receiving data associated with an access point (AP), wherein the data comprises a percentage of user equipments (UEs) connected to the AP with timeouts and social media inputs, and wherein the social media inputs include information reported by a user in connection with one or more social media accessed by the user through the AP;
• creating, based on the received data and a machine learning algorithm, a decision model for predicting a future spectrum requirement of the AP;
• generating a prediction, using the decision model, of the future spectrum requirement of the AP;
• and
• sending the prediction to a spectrum access system (SAS) in the wireless access network, wherein the SAS is configured to allocate spectrum to the AP based on the prediction.

A system for predicting spectrum allocation in a wireless access network, the system comprising:

• a memory storing instructions;
• and
• a processor configured to execute the instructions to:
• receive data associated with an access point (AP), wherein the data comprises a percentage of user equipments (UEs) connected to the AP with timeouts and social media inputs, and wherein the social media inputs include information reported by a user in connection with one or more social media accessed by the user through the AP;
• create, based on the received data and a machine learning algorithm, a decision model for predicting a future spectrum requirement of the AP;
• generate a prediction, using the decision model, of the future spectrum requirement of the AP;
• and
• send the prediction to a spectrum access system (SAS) in the wireless access network, wherein the SAS is configured to allocate spectrum to the AP based on the prediction.