Methods and Systems for Multi-layer Perceptron 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 multilayer perceptron neural network with Hammerstein structure by dividing an aggressor signal into real and imaginary components, augmenting the components by weight factors, executing a linear combination of the augmented components, and executing a nonlinear sigmoid function for the combined components at a hidden layer of multilayer perceptron neural network to produce a hidden layer output signal. At an output layer, hidden layer output signals may be augmented by weight factors, and the augmented hidden layer output signals may be linearly combined to produce real and imaginary components of an estimated jammer signal. A linear filter function may be executed for the components of the jammer signal, and to produce a nonlinear interference estimate used to cancel the nonlinear interference of a victim signal.

主权项

1. A method for managing interference in a multi-technology communication device, comprising:
receiving an aggressor signal at the multi-technology communication device; dividing the aggressor signal into a real aggressor signal component and an imaginary aggressor signal component; augmenting the real aggressor signal component and the imaginary aggressor signal components with weight factors at a hidden layer of a multilayer perceptron neural network; executing a first linear combination of the real aggressor signal component and the imaginary aggressor signal component at the hidden layer to produce a hidden layer intermediate signal; executing a nonlinear sigmoid function for the hidden layer intermediate signal at the hidden layer to produce a hidden layer output signal; augmenting, a plurality of the hidden layer output signals each with the weight factors at an output layer of the multilayer perceptron neural network; executing a second linear combination of the augmented plurality of hidden layer output signals at the output layer to produce a real output layer output signal and an imaginary output layer output signal; and executing a linear filter function on the real output layer output signal and the imaginary output layer output signal to produce an estimated real nonlinear interference and an estimated imaginary nonlinear interference.