Abstract: A first outcome variable to be predicted by a machine-learning model (MLM) is determined. The first outcome variable is associated with a product. Using product information that comprises values for each of a plurality of different input variables, a plurality of MLMs are trained to predict the first outcome variable, each MLM utilizing a different set of input variables of the plurality of different input variables. Using historical data that identifies historical values for the first outcome variable, each MLM is tested to determine an accuracy for each MLM. A first MLM is identified based on the testing.

Abstract: A digital phased array may include a plurality of antenna elements forming a main array of the digital phased array, a plurality of mixers configured to down-convert data received at respective ones of the antenna elements, a plurality of analog-to-digital converters configured to digitize data provided by corresponding ones of the mixers, and a digital signal processor. The digital signal processor may be configured to receive digitized data including at least one interference signal and at least one desired signal, determine an angle of arrival of the at least one interference signal, steer a beam of the main array toward the at least one interference signal based on the determined angle of arrival, and perform interference cancellation relative to the at least one interference signal.