Machine Learning for Analog Canceler Tuning

Signal interference is a common issue in network devices, especially when signals are transmitted and received within the same frequency band. This problem interrupts communication flow and results in loss of data, making it an important challenge to overcome in the field of communications technology. Traditional approaches to mitigating signal interference can be reactive and often inefficient. Typically, they only offer broad mitigating strategies without clearly addressing the nuances of each unique interfering signal. Moreover, these strategies do not use learning algorithms that can enhance their effectiveness over time. This shortsightedness in current solutions creates ample space for improved methods.

Technology Description

This network device includes a transceiver able to send and receive signals concurrently within a single frequency band. However, this usage may result in radio-frequency signal interference, a problem this device aims to mitigate through its unique components, an analog canceler and a neural network. The neural network takes in data detailing the characteristics of the signal interference and produces coefficients for the analog canceler as outputs. These outputs are utilized directly by the analog canceler. What differentiates this technology is its approach to solve the issue of signal interference in a single frequency band. The cooperative functionality of the analog canceler and neural network results in a dual-layered solution that first identifies the unique patterns of interference before addressing it with learned mathematical principles. The practical implementation of these coefficients by the analog canceler offers a novel and effective method to tackle resonant signal interference.