LMS Adaptive Filtering for RF Self-Interference Cancellation
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Solution Overview
Problem
Existing self-interference cancellation methods in wireless full duplex communication systems suffer from slow convergence speed and inaccurate estimation of amplitude and phase due to reliance on RSSI detection, which limits their effectiveness in dynamic environments.
Innovation Solution
An adaptive radio-frequency interference cancelling device and method utilizing least mean squares (LMS) adaptive filtering algorithm to adjust the amplitude and phase of a radio-frequency reference signal, enabling faster convergence and more accurate estimation by extracting baseband signals and applying LMS processing to generate control signals for the amplitude phase adjusting module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RSSI detection method is used for self-interference cancellation, then the system can achieve basic interference cancellation functionality, but the convergence speed is slow and estimation accuracy is poor
Solution Approach 1:
The patent replaces the traditional RSSI detection method (analog signal processing) with an LMS adaptive filtering algorithm (digital signal processing). The LMS algorithm processes digital baseband signals to generate amplitude and phase control signals, providing faster convergence and more accurate estimation compared to the analog RSSI-based approach. This substitution of processing domain and methodology directly resolves the contradiction between convergence speed and estimation accuracy.
2Adaptability or versatility
If amplitude and phase search algorithm is used to adjust reference signal, then the system can cancel self-interference, but the algorithm converges slowly and cannot follow parameter variation in time
Solution Approach 1:
The patent implements a dynamic adaptive filtering system where the LMS algorithm continuously adjusts the amplitude and phase of the reference signal in real-time based on incoming baseband signals. The algorithm adapts to parameter variations dynamically, making the system responsive to changing channel conditions and interference characteristics, thereby resolving the contradiction between adaptability and convergence speed.
Solution Approach 2:
The LMS adaptive filtering algorithm employs a feedback mechanism where the error signal (difference between actual and desired output) is used to continuously update the filter coefficients. This feedback loop enables the system to automatically track and adapt to parameter variations in real-time, achieving both fast convergence and high adaptability to changing conditions.
3Object-affected harmful factors
If guard band or guard interval is used to isolate transmission and reception, then self-interference is prevented, but frequency spectrum efficiency is reduced
Solution Approach 1:
The patent converts the harmful self-interference signal into a useful reference signal for adaptive cancellation. By capturing the transmitted signal through the same antenna and processing it through LMS adaptive filtering, the system transforms the interference problem into an opportunity for precise interference characterization and cancellation, enabling full-duplex operation without requiring guard bands or guard intervals.
Solution Approach 2:
The patent changes the approach from frequency or time separation to amplitude and phase adjustment of the reference signal. By dynamically adjusting the amplitude and phase parameters of the self-interference cancellation signal through LMS filtering, the system achieves complete isolation of self-interference without sacrificing frequency spectrum efficiency, enabling simultaneous transmission and reception on the same frequency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The LMS adaptive filtering algorithm achieves faster convergence speed and more accurate estimation of self-interference cancellation, significantly improving the efficiency of wireless full duplex communication systems by effectively isolating self-interference from useful signals.
Implementation Method 1
one is mixed with the radio-frequency residual signal by the first multiplier and then passes through the first low-pass filter to obtain a first baseband product signal, and the other split radio-frequency reference signal is phase shifted by 90 degrees via the phase shifter, mixed with the radio-frequency residual signal by the second multiplier and filtered by the second low-pass filter
Implementation Method 2
passes through the first low-pass filter to obtain a first baseband product signal... filtered by the second low-pass filter to obtain a second baseband product signal
Implementation Method 3
perform least mean squares adaptive filtering processing on the baseband signal to obtain an amplitude phase control
Data Source
AI summary
The present invention provides an adaptive radio-interference cancelling device and method, a receiver, and a wireless full duplex communication system. The device includes an amplitude phase adjusting module, configured to adjust an amplitude and a phase of a radio-frequency reference signal and output a radio-frequency adjustment signal to enable the radio-frequency adjustment signal to converge to a self-interference signal in a radio-frequency received signal; a subtractor, configured to output a radio-frequency residual signal, where the radio-frequency residual signal is a difference signal between the radio-frequency received signal and the radio-frequency adjustment signal; and a baseband extracting and filtering module, configured to receive the radio-frequency reference signal and the radio-frequency residual signal output by the subtractor, extract baseband signals and perform least mean squares adaptive filtering processing on the baseband signals to obtain an amplitude phase control signal and output to the amplitude phase adjusting module. The present invention adopts an LMS adaptive filtering algorithm, has a faster convergence speed and provides a more accurate estimation result.