TX IQ Mismatch Pre-Compensation via Direct Learning Adaptation
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Solution Overview
Problem
Existing direct conversion wireless transmitter designs face challenges in compensating for IQ mismatch, particularly in broadband signals and temperature variations, as existing methods assume frequency-independent mismatch or restrict signal bandwidth, failing to effectively adapt to frequency-dependent changes.
Innovation Solution
The implementation of direct learning adaptation for IQ mismatch pre-compensation in the transmitter, using a feedback receiver to adjust IQ pre-compensation filters, allowing for frequency-dependent compensation and tracking of mismatch variations, implemented as a combination of digital signal processing and hardware acceleration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If digital filtering (IQ mismatch compensation) is used to compensate for IQ mismatch, then out-of-band emissions are suppressed, but the method assumes frequency-independent mismatch which is not satisfied for broadband signals
Solution Approach 1:
The patent implements dynamic adaptation of IQ mismatch compensation filter coefficients using a feedback receiver and direct learning algorithm. The system continuously updates the compensation filters based on received feedback signals, enabling the system to adapt to frequency-dependent mismatch variations across broadband spectra rather than using static frequency-independent coefficients.
Solution Approach 2:
The patent introduces a feedback receiver (FBRX) that captures transmitted signals and provides feedback to the transmitter. This feedback loop enables the system to measure actual IQ mismatch characteristics and adjust compensation filters accordingly, resolving the limitation of assuming frequency-independent mismatch by providing real measurements of frequency-dependent mismatch across the operating bandwidth.
2Device complexity
If TX QMC filter coefficients are adapted assuming frequency-independent mismatch, then device complexity is reduced, but broadband signal handling capability deteriorates
Solution Approach 1:
The system transitions from static filter coefficients to dynamic adaptation using a feedback-based direct learning algorithm. The filter coefficients are continuously updated based on feedback measurements, enabling the system to handle broadband signals with frequency-dependent mismatch while maintaining manageable complexity through efficient adaptation algorithms.
Solution Approach 2:
The patent changes the parameters of the compensation filters dynamically by updating filter coefficients based on feedback measurements. This allows the system to adapt to frequency-dependent mismatch characteristics across broadband spectra, transforming the system from a fixed-parameter design to a variable-parameter design that can optimize performance for different frequency components.
3Measurement precision
If a feedback receiver is used to capture feedback data for direct learning adaptation, then IQ mismatch compensation accuracy is improved, but device complexity increases
Solution Approach 1:
The feedback receiver serves multiple functions: it captures transmitted signals for IQ mismatch measurement, provides feedback for filter adaptation, and enables direct learning algorithms. By making the feedback receiver multi-functional, the patent improves measurement accuracy without proportionally increasing system complexity, as the same hardware infrastructure supports multiple operational requirements.
Solution Approach 2:
The system uses its own transmitted signal as the feedback source for IQ mismatch measurement and adaptation. The feedback receiver captures the transmitted signal directly, and the direct learning algorithm uses this self-generated feedback to adapt the compensation filters. This self-service approach eliminates the need for external calibration equipment, improving measurement accuracy while keeping the system self-contained.
Data Source
AI summary
A direct conversion wireless transmitter includes IQ mismatch pre-compensation using direct learning adaptation to adjust IQ pre-compensation filtering. Widely-linear IQ_mismatch pre-compensation filtering compensates for IQ mismatch in the TX analog chain, filtering of input data x(n) to provide pre-compensated data y(n) with a compensation image designed to interfere destructively with the IQ_mismatch image. A feedback receiver FBRX captures feedback data z(n) used for direct learning adaptation. DL adaptation adjusts IQ_mismatch filters, modeled as an x(n)_direct and complex conjugate x(n)_image transfer functions w1 and w2, including generating an adaptation error signal based on a difference between TX/FBRX-path delayed versions of x(n) and z(n), and can include estimation and compensation for TX/FBRX phase errors. DL adaptation adjusts the IQ pre-comp filters w1/w2 to minimize the adaptation error signal. Similar modeling can be used for IQ mismatch. The IQ_mismatch pre-compensator can be implemented as a combination of digital signal processing and hardware acceleration.