Transceiver with machine learning for generation of communication parameters and cognitive resource allocation

A method for operating a radio frequency (RF) network having a plurality of communication nodes, the method comprising:

• evaluating, via a machine learning model, a state of the RF network based on a spectrum environment and a communication objective, wherein the network transceiver is configured to communicate with at least one of the plurality of communication nodes in the RF network,
• generating a set of communication parameters based on the state of the RF network;
• causing the network transceiver to communicate with the at least one of the plurality of communication nodes using the generated set of communication parameters;
• and
• modifying the machine learning model based on a result of causing the network transceiver to communicate with the at least one of the plurality of communication nodes.

A program product for operating a radio frequency (RF) network having a plurality of communication nodes, the computer program product comprising a computer readable storage medium on which is stored program code for causing a computer system to perform actions including:

• evaluating, via a machine learning model, a state of the RF network based on a spectrum environment and a communication objective, wherein the network transceiver is configured to communicate with at least one of the plurality of communication nodes in the RF network,
• generating a set of communication parameters based on the state of the RF network;
• causing the network transceiver to communicate with the at least one of the plurality of communication nodes using the generated set of communication parameters;
• and
• modifying the machine learning model based on a result of causing the network transceiver to communicate with the at least one of the plurality of communication nodes.