Machine learning (ml)-based dynamic demodulator parameter selection

An apparatus for wireless communication by a receiving device, comprising:

• means for predicting, with an artificial neural network, at each data block of a set of data blocks, a least complex set of demodulator parameters that will achieve a goal, based on features of a data block expected to be received;
• and
• means for dynamically selecting the least complex set of demodulator parameters, from a plurality of sets of demodulator parameters, based on the features of the data block expected to be received, the means for dynamically selecting preventing degradation of demodulation performance for each data block with the selected set of demodulator parameters for the data block, with respect to a more complex set of demodulator parameters.

A non-transitory computer-readable medium having program code recorded thereon, the program code executed by a processor of a receiving device and comprising:

• program code to predict, with an artificial neural network, at each data block of a set of data blocks, a least complex set of demodulator parameters that will achieve a goal, based on features of a data block expected to be received;
• and
• program code to dynamically select the least complex set of demodulator parameters, from a plurality of sets of demodulator parameters, based on the features of the data block expected to be received, the program code to dynamically select preventing degradation of demodulation performance for each data block with the selected set of demodulator parameters for the data block, with respect to a more complex set of demodulator parameters.