METHODS AND SYSTEMS FOR MULTI-MODEL RADIAL BASIS FUNCTION NEURAL NETWORK BASED NON-LINEAR INTERFERENCE MANAGEMENT IN MULTI-TECHNOLOGY COMMUNICATION DEVICES

摘要

The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a multi-model radial basis function neural network with Hammerstein structure by executing a radial basis function on aggressor signals at a hidden layer of the radial basis function neural network with Hammerstein structure to obtain hidden layer outputs, augmenting aggressor signal(s) by weight factors, infusing the hidden layer outputs by infusion factors, and, executing a linear combination of the augmented output, at an intermediate layer to produce a combined hidden layer outputs. At an output layer, a linear filter function may be executed on the hidden layer outputs to produce an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal.