Crest Factor Reduction Using Polynomial Peak Detection
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Solution Overview
Problem
Existing crest factor reduction (CFR) methods, particularly in wireless communications, face challenges with inaccurate peak detection due to low oversampling rates, leading to missed or created peaks, which increases latency and reduces efficiency in base station operations.
Innovation Solution
The system employs a peak detector that performs a polynomial fit to accurately locate and measure peak values, using window CFR to prevent peak misses and new peak creation, and spectrally-shaped cancellation pulses to reduce peak magnitudes while preserving signal phase, combining with PCCFR for efficient peak cancellation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If oversampling rate is decreased to reduce processing complexity, then device complexity is reduced, but measurement precision of peak magnitude deteriorates
Solution Approach 1:
A polynomial fitting function is introduced as an intermediary between the sampled signal points and the true peak value. The function fits polynomial curves to the sampled points around the peak region and extrapolates to determine the precise peak magnitude and location, eliminating the need for high oversampling while maintaining measurement accuracy.
Solution Approach 2:
The approach changes the parameter of sampling rate by using polynomial fitting to compensate for low oversampling. Instead of increasing the sampling rate, the system uses mathematical transformation (polynomial fitting) to recover accurate peak information from fewer samples, effectively decoupling measurement precision from oversampling rate.
2Reliability
If cancellation pulse method is used to reduce peak magnitudes, then peak magnitude is reduced, but reliability of peak detection deteriorates due to missed or created peaks
Solution Approach 1:
The system performs preliminary polynomial fitting to accurately identify peak locations and magnitudes before applying cancellation pulses. This preliminary characterization of peaks using polynomial models ensures that cancellation operations are precisely targeted and do not create artifacts or miss peaks, improving the reliability of peak detection throughout the CFR process.
3Reliability
If multiple processing stages are used to address undesirable peaks, then reliability of peak detection is improved, but loss of time increases due to increased latency
Solution Approach 1:
The polynomial fitting approach extracts accurate peak information directly from the signal model without requiring multiple iterative processing stages. By taking out the essential peak characteristics through mathematical fitting rather than repeated processing, the system achieves high reliability with minimal latency.
Data Source
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Figure 2-1
AI summary
A system (200) for crest factor reduction (CFR) includes a peak detector (205) configured to receive an input signal (xk); a running maximum filter (210) configured to generate a scaling factor based on a window gain (Gk) and a filter length, wherein the window gain (Gk) is based on the input signal (Xk) and a threshold value (T); a window CFR gain filter (215) configured to generate a gain correction (Fk) based on the scaling factor and the filter length; a delay (225) configured to delay the input signal (Xk) to generate a delayed input signal; a multiplier (230) configured to multiply the gain correction (Fk) by the delayed input signal to obtain a peak correction value; and an adder (235) configured to determine an output signal (yk) based on the peak correction value and the delayed input signal.