Block-Based Crest Factor Reduction for Peak Regrowth Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing Crest Factor Reduction techniques process signals sample-by-sample, leading to inefficiencies due to overhead processing, which impairs the efficiency of power amplifiers in wireless communication systems.
Innovation Solution
Implementing block-based Crest Factor Reduction methods that process data in blocks rather than samples, using a vector engine and employing pre-cursor and post-cursor blocks to maintain continuity and avoid edge effects, with iterative processing to address peak regrowth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sample-by-sample Crest Factor Reduction processing is used, then peak amplitude reduction is achieved, but processing efficiency deteriorates due to overhead per sample
Solution Approach 1:
The patent divides the continuous signal stream into discrete blocks of samples. Each block is processed independently as a unit, allowing overhead operations (peak detection, pulse generation, cancellation) to be performed once per block rather than once per sample. This segmentation maintains accurate peak cancellation within each block while dramatically reducing the frequency of overhead execution, thereby resolving the contradiction between processing accuracy and efficiency.
2Productivity
If block-based processing is implemented, then processing efficiency improves by amortizing overhead, but edge effects and discontinuities are introduced at block boundaries
Solution Approach 1:
The patent performs preliminary peak detection and pulse cancellation operations on blocks of data before the blocks are transmitted or further processed. By detecting peaks and applying cancellation pulses within each block beforehand, the system prepares the signal to minimize regrowth effects. This preliminary action within blocks, combined with proper block sizing and overlapping, maintains signal continuity while achieving the efficiency benefits of block-based processing.
3Measurement precision
If iterative Crest Factor Reduction is applied, then peak regrowth is addressed and PAR reduction is improved, but processing complexity increases
Solution Approach 1:
The patent implements iterative Crest Factor Reduction where the output of one iteration becomes the input for the next. Each iteration detects remaining peaks that may have regrown after previous cancellation operations and applies additional cancellation pulses. This feedback loop continues for a specified number of iterations or until convergence, effectively addressing peak regrowth and improving PAR reduction effectiveness while managing complexity through controlled iteration counts.
Data Source
AI summary
Block-based crest factor reduction (CFR) techniques are provided. An exemplary block-based crest factor reduction method comprises obtaining a block of data samples comprised of a plurality of samples; applying the block of data to a crest factor reduction block; and providing a processed block of data from the crest factor reduction block. The block-based crest factor reduction method can optionally be iteratively performed a plurality of times for the block of data. The block of data samples can comprise an expanded block having at least one cursor block. For example, at least two pre-cursor blocks and one post-cursor block can be employed. The peaks can be cancelled, for example, only in the block of data samples and in a first of the pre-cursor blocks.


