Hardware allocation in RFIC based on machine learning

A User Equipment, comprising:

• a processing circuit;
• and
• memory connected to the processing circuit,
• the memory storing instructions that, when executed by the processing circuit, cause the User Equipment to perform a method, the method comprising:
• receiving, by a first neural network, a first state and a first state transition, the first state comprising:
• one or more identifiers for available active ports, and
• a set of available connections between two or more circuit elements, each of the circuit elements being one of:
• (1) a first circuit type, (2) a second circuit type that operatively connects a circuit element of the first circuit type to one of the available active ports, and (3) the available active ports;
• and
• generating, by the first neural network, a first estimated quality value, for the first state transition, the first estimated quality value corresponding to a likelihood of the first state transition being one of a sequence of transitions terminating in a terminal state in which a connection is made to each of the available active ports,
• wherein:
• the first state transition is a transition from the first state to a second state, and
• the second state includes a connection, not present in the first state, between two of the circuit elements.

A User Equipment, comprising:

• means for processing;
• and
• memory connected to the means for processing,
• the memory storing instructions that, when executed by the means for processing, cause the User Equipment to perform a method, the method comprising:
• receiving, by a first neural network, a first state and a first state transition, the first state comprising:
• one or more identifiers for available active ports, and
• a set of available connections between two or more circuit elements, each of the circuit elements being one of:
• (1) a first circuit type, (2) a second circuit type that operatively connects a circuit element of the first circuit type to one of the available active ports, and (3) the available active ports;
• and
• generating, by the first neural network, a first estimated quality value, for the first state transition, the first estimated quality value corresponding to a likelihood of the first state transition being one of a sequence of transitions terminating in a terminal state in which a connection is made to each of the available active ports,
• wherein:
• the first state transition is a transition from the first state to a second state, and
• the second state includes a connection, not present in the first state, between two of the circuit elements.