Adaptive Receiver Linearization for Frequency-Dependent Distortion
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Solution Overview
Problem
Modern digital receivers face challenges in linearizing analog front-end components without the need for a test signal, as nonlinearities are frequency-dependent and vary with external conditions, making it difficult to achieve wide-band linearization with existing solutions that are often resource-intensive.
Innovation Solution
A digital receiver linearizer system that uses a digital down converter to move the frequency band of interest to zero center frequency, applies digital filters, and generates complex amplitude correction coefficients to modify and subtract nonlinear distortion from the input signal, allowing for continuous correction and adaptive linearization without a test signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wide-band linearization is achieved using traditional methods (e.g., Volterra series), then linearity is improved, but device complexity and resource consumption increase significantly
Solution Approach 1:
The patent segments the frequency band into multiple sub-bands and processes each sub-band separately with dedicated correction coefficients. This divides the complex wide-band linearization problem into simpler narrow-band problems, reducing overall device complexity while maintaining linearity across the full bandwidth.
Solution Approach 2:
The patent applies local quality by using frequency-dependent correction coefficients that are optimized for specific frequency regions. Each sub-band receives tailored correction parameters rather than a uniform approach, improving linearity locally while reducing the complexity of a monolithic solution.
2Reliability
If analog front-end linearity is improved to reduce distortion, then signal-to-noise ratio is improved, but power consumption and cost increase
Solution Approach 1:
The patent replaces mechanical/analog linearization methods with digital signal processing. Instead of using complex analog circuits to achieve linearity, the system uses digital correction algorithms that process the signal after ADC conversion, significantly reducing power consumption while improving signal-to-noise ratio.
Solution Approach 2:
The linearizer uses the distorted signal itself to generate correction coefficients through feedback mechanisms. The system extracts distortion components from the received signal and uses them to create corrective terms, eliminating the need for external test signals or complex training sequences, thereby reducing overall system resource requirements.
3Device complexity
If static inverse function is used for linearization, then device complexity is reduced, but adaptability to frequency-dependent nonlinearities is lost
Solution Approach 1:
The patent transitions from static to dynamic linearization by implementing frequency-dependent correction coefficients that can be updated adaptively. The system continuously monitors distortion characteristics and adjusts correction parameters accordingly, enabling adaptation to changing nonlinearities while maintaining manageable device complexity through efficient algorithms.
Solution Approach 2:
The patent changes the parameters of the linearization function to be frequency-dependent rather than fixed. By making the correction coefficients variable across frequency and adaptable over time, the system achieves high adaptability to different distortion conditions while controlling complexity through parameterized models rather than complex circuitry.
4Measurement precision
If training with test signal is used to linearize the receiver, then measurement precision is improved, but ease of operation deteriorates due to impracticality
Solution Approach 1:
The linearizer performs self-testing and self-correction by using the actual received signal to estimate and correct distortion. The system extracts distortion components directly from the signal of interest without requiring external test signals, making the system easy to operate in real-world conditions while maintaining high measurement precision through adaptive algorithms.
Solution Approach 2:
The patent implements feedback mechanisms where the output of the linearizer is monitored and used to adjust correction coefficients. This closed-loop approach allows the system to continuously improve measurement precision by learning from actual operating conditions, while eliminating the need for separate training phases with test signals, thereby improving ease of operation.
Data Source
AI summary
A system and method for digital receiver linearization is provided. An input digital signal is accepted with a plurality of spectral components. The input digital signal may be either a radio frequency (RF) digital signal or a baseband digital signal. Nonlinear distortion is created in response to the input digital signal. As the result of a corrected input digital signal, a primary baseband signal is created with real (I) and imaginary quadrature (Q) components. In response to the nonlinear distortion, auxiliary baseband signals are created with real (IAUX) and imaginary quadrature (QAUX) components. The primary baseband signal is compared to the auxiliary baseband signals to supply complex amplitude correction coefficients. The complex amplitude correction coefficients are used to modify the nonlinear distortion, and the modified nonlinear distortion is subtracted from the input digital signal to supply the corrected input digital signal.