Adaptive Digital Cancellation Using Probe Waveforms for STAR Systems
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Solution Overview
Problem
Conventional adaptive digital filtering techniques are ineffective in removing self-interference and attenuating external signals of interest in simultaneous transmit and receive (STAR) systems, especially in systems with channel noise and nonlinear distortion, and are computationally expensive for multichannel systems, leading to slow convergence and poor performance.
Innovation Solution
The method involves emitting a transmit signal with a source signal and a probe signal, estimating the observed probe signal, generating a cancel signal, and combining it with the receive signal to isolate the signal of interest, using observation channels and adaptive cancellers to remove self-interference while introducing probe signals to each transmit channel to improve convergence and reduce computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional adaptive digital filtering techniques are used for self-interference reduction, then the filter structure can match the physical response of the system, but channel noise and nonlinear distortion cannot be corrected and external signals of interest are attenuated or removed
Solution Approach 1:
The transmit signal is segmented into a source signal portion and a probe signal portion. The probe signal portion is specifically designed to be uncorrelated with the source signal, allowing the adaptive filter to learn the self-interference channel without being confounded by correlated external signals. This segmentation enables the filter to distinguish between self-interference and external signals of interest.
Solution Approach 2:
A probe signal acts as an intermediary element that mediates between the transmit and receive channels. This probe signal is injected into the transmit channel and used to train the adaptive filter, serving as a reference that helps the filter learn the self-interference path without requiring direct access to the actual external signals of interest.
2Device complexity
If computationally efficient filter algorithms (e.g., least mean square) are used, then computational complexity is reduced, but convergence speed becomes extremely slow for practical waveforms like narrowband communication and chirps
Solution Approach 1:
The system changes the parameter of the transmitted waveform by adding a probe signal portion that is uncorrelated with the source signal. This parameter change in the waveform structure improves the conditioning of the correlation matrix, enabling faster convergence of computationally efficient algorithms like LMS while maintaining their low computational complexity.
3Measurement precision
If multichannel cancellation is implemented for STAR phased arrays, then self-interference cancellation is achieved across multiple channels, but convergence is extremely poor due to high correlation between transmit channels
Solution Approach 1:
Each transmit channel in the multichannel system is segmented to include its own probe signal portion. This per-channel segmentation creates uncorrelated reference signals for each channel, breaking the high correlation between transmit channels and enabling effective multichannel adaptive filtering with fast convergence.
Solution Approach 2:
The probe signal adds a new dimension to the signal space that is orthogonal to the source signal. This dimensional expansion provides the adaptive filter with additional independent information about the self-interference channel, improving convergence in multichannel systems where traditional approaches fail due to channel correlation.
4Measurement precision
If existing multichannel solutions are used, then self-interference cancellation is achieved, but computational requirements scale poorly as the number of channels increases
Solution Approach 1:
The segmentation of each channel's transmit signal into source and probe portions enables independent processing of each channel. This segmentation allows the system to use computationally efficient algorithms that process each channel separately or with minimal cross-channel computation, preventing computational complexity from scaling poorly with the number of channels.
Data Source
AI summary
A method and apparatus for achieving simultaneous transmit and receive operation with digital cancelling based upon probe waveforms is described. Digital cancelling based upon probe waveforms enables adjacent transmitting and receiving channels to transmit and receive correlated signals.