The various embodiments include methods and apparatuses for cancelling nonlinear interference during concurrent communication of dual-technology wireless communication devices. Nonlinear interference may be estimated using a multilayer perceptron neural network by augmenting aggressor signal(s) by weight factors, executing a linear combination of the augmented aggressor signals, and executing a nonlinear sigmoid function for the combined aggressor signals at a hidden layer of multilayer perceptron neural network to produce a hidden layer output signal. Multiple hidden layers may repeat the process for the hidden layer output signals. 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 an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal. The weight factors may be trained based on a determination of an error of the estimated nonlinear interference.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for managing interference in a multi-technology communication device, comprising: receiving aggressor signals at an input layer of a multilayer perceptron neural network; augmenting the aggressor signals with a weight factor at a first hidden layer of the multilayer perceptron neural network; executing a linear combination of the augmented aggressor signals at the first hidden layer; executing a nonlinear sigmoid function for the combined aggressor signals at the first hidden layer to produce a hidden layer output signal; augmenting the hidden layer output signal with a weight factor at an output layer of the multilayer perceptron neural network; and executing a linear combination of augmented hidden layer output signals at the output layer to produce an estimated nonlinear interference. 2. The method of claim 1, further comprising: training weight factors to reduce an error of the estimated nonlinear interference. 3. The method of claim 2, further comprising: determining an error of the estimated nonlinear interference; determining whether the error of the estimated nonlinear interference exceeds an efficiency threshold; and cancelling the estimated nonlinear interference from a victim signal. 4. The method of claim 3, wherein: training weight factors to reduce an error of the estimated nonlinear interference comprises training weight factors in response to determining that the error of the estimated nonlinear interference exceeds the efficiency threshold, and cancelling the estimated nonlinear interference from a victim signal comprises cancelling the estimated nonlinear interference from the victim signal in response to determining that the error of the estimated nonlinear interference does not exceed the efficiency threshold. 5. The method of claim 3, further comprising selecting the weight factors to reduce an error of the estimated nonlinear interference. 6. The method of claim 5, wherein selecting the weight factors to reduce an error of the estimated nonlinear interference comprises selecting the weight factors used for a previous determination of a previous estimated nonlinear interference for a previous victim signal within a predetermined time period. 7. The method of claim 2, wherein training weight factors to reduce an error of the estimated nonlinear interference comprises using a Gauss-Newton algorithm to train the weight factors. 8. The method of claim 2, wherein training weight factors to reduce an error of the estimated nonlinear interference comprises using a Levenberg-Marquardt algorithm to train the weight factors. 9. The method of claim 1, wherein executing a linear combination of the augmented aggressor signals at the first hidden layer comprises executing a linear combination of the combined aggressor signals and a bias factor at the first hidden layer. 10. The method of claim 1, wherein executing a linear combination of augmented hidden layer output signals at the output layer to produce an estimated nonlinear interference comprises executing a linear combination of the combined hidden layer output signals and a bias factor at the output layer. 11. The method of claim 1, further comprising cancelling the estimated nonlinear interference from a victim signal. 12. The method of claim 11, further comprising decoding the victim signal after cancelling the estimated nonlinear interference from the victim signal. 13. The method of claim 1, further comprising: augmenting hidden layer output signals with a weight factor at a second hidden layer of the multilayer perceptron neural network; executing a linear combination of the augmented hidden layer output signals at the second hidden layer; and executing a nonlinear sigmoid function for the combined hidden layer output signals at the second hidden layer to produce a hidden layer output signal. 14. A multi-technology communication device, comprising: a processor configured with processor-executable instructions to: receive aggressor signals at an input layer of a multilayer perceptron neural network; augment the aggressor signals with a weight factor at a first hidden layer of the multilayer perceptron neural network; execute a linear combination of the augmented aggressor signals at the first hidden layer; execute a nonlinear sigmoid function for the combined aggressor signals at the first hidden layer to produce a hidden layer output signal; augment the hidden layer output signal with a weight factor at an output layer of the multilayer perceptron neural network; and execute a linear combination of augmented hidden layer output signals at the output layer to produce an estimated nonlinear interference. 15. The multi-technology communication device of claim 14, wherein the processor is further configured with processor-executable instructions to: train weight factors to reduce an error of the estimated nonlinear interference. 16. The multi-technology communication device of claim 15, wherein the processor is further configured with processor-executable instructions to: determine an error of the estimated nonlinear interference; determine whether the error of the estimated nonlinear interference exceeds an efficiency threshold; and cancel the estimated nonlinear interference from a victim signal. 17. The multi-technology communication device of claim 16, wherein the processor is further configured with processor-executable instructions to: train weight factors to reduce an error of the estimated nonlinear interference comprises training weight factors in response to determining that the error of the estimated nonlinear interference exceeds the efficiency threshold, and cancel the estimated nonlinear interference from a victim signal comprises cancelling the estimated nonlinear interference from the victim signal in response to determining that the error of the estimated nonlinear interference does not exceed the efficiency threshold. 18. The multi-technology communication device of claim 16, wherein the processor is further configured with processor-executable instructions to: select the weight factors to reduce an error of the estimated nonlinear interference. 19. The multi-technology communication device of claim 18, wherein the processor is further configured with processor-executable instructions to select the weight factors to reduce an error of the estimated nonlinear interference by selecting the weight factors used for a previous determination of a previous estimated nonlinear interference for a previous victim signal within a predetermined time period. 20. The multi-technology communication device of claim 15, wherein the processor is further configured with processor-executable instructions to train weight factors to reduce an error of the estimated nonlinear interference using a Gauss-Newton algorithm to train the weight factors. 21. The multi-technology communication device of claim 15, wherein the processor is further configured with processor-executable instructions to train weight factors to reduce an error of the estimated nonlinear interference using a Levenberg-Marquardt algorithm to train the weight factors. 22. The multi-technology communication device of claim 14, wherein the processor is further configured with processor-executable instructions to execute a linear combination of the augmented aggressor signals at the first hidden layer by executing a linear combination of the combined aggressor signals and a bias factor at the first hidden layer. 23. The multi-technology communication device of claim 14, wherein the processor is further configured with processor-executable instructions to execute a linear combination of augmented hidden layer output signals at the output layer to produce an estimated nonlinear interference by executing a linear combination of the combined hidden layer output signals and a bias factor at the output layer. 24. The multi-technology communication device of claim 14, wherein the processor is further configured with processor-executable instructions to cancel the estimated nonlinear interference from a victim signal. 25. The multi-technology communication device of claim 24, wherein the processor is further configured with processor-executable instructions to decode the victim signal after cancelling the estimated nonlinear interference from the victim signal. 26. The multi-technology communication device of claim 14, wherein the processor is further configured with processor-executable instructions to: augment hidden layer output signals with a weight factor at a second hidden layer of the multilayer perceptron neural network; execute a linear combination of the augmented hidden layer output signals at the second hidden layer; and execute a nonlinear sigmoid function for the combined hidden layer output signals at the second hidden layer to produce a hidden layer output signal. 27. A multi-technology communication device, comprising: means for receiving aggressor signals at an input layer of a multilayer perceptron neural network; means for augmenting the aggressor signals with a weight factor at a first hidden layer of the multilayer perceptron neural network; means for executing a linear combination of the augmented aggressor signals at the first hidden layer; means for executing a nonlinear sigmoid function for the combined aggressor signals at the first hidden layer to produce a hidden layer output signal; means for augmenting the hidden layer output signal with a weight factor at an output layer of the multilayer perceptron neural network; and means for executing a linear combination of augmented hidden layer output signals at the output layer to produce an estimated nonlinear interference. 28. A non-transitory processor-readable medium having stored thereon processor-executable software instructions to cause a processor to perform operations comprising: receiving aggressor signals at an input layer of a multilayer perceptron neural network; augmenting the aggressor signals with a weight factor at a first hidden layer of the multilayer perceptron neural network; executing a linear combination of the augmented aggressor signals at the first hidden layer; executing a nonlinear sigmoid function for the combined aggressor signals at the first hidden layer to produce a hidden layer output signal; augmenting the hidden layer output signal with a weight factor at an output layer of the multilayer perceptron neural network; and executing a linear combination of augmented hidden layer output signals at the output layer to produce an estimated nonlinear interference.