Crest Factor Reduction via Peak Clustering and Parallel Cancellation
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Solution Overview
Problem
Current crest factor reduction (CFR) algorithms for wireless data transmissions are complex, require significant processing power, and increase error vector magnitude (EVM), leading to inefficiencies in power amplifier operation due to peak regrowth and out-of-band emissions.
Innovation Solution
The proposed CFR technique involves oversampling the input signal, detecting peak samples, clustering them, and applying truncated upsampled cancellation pulses in parallel to reduce peak amplitudes, followed by iterative gain computations to eliminate remaining peaks, thereby reducing complexity and processing power while maintaining signal quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Strength
If conventional CFR algorithms are used to reduce PAPR, then peak amplitudes are reduced, but processing complexity and power consumption increase significantly
Solution Approach 1:
The patent segments the peak cancellation process into distinct phases: detection of peak samples, computation of scaling factors for identified peaks, and selective application of cancellation pulses. This segmentation allows the system to focus processing resources only on peak samples rather than processing the entire signal, thereby reducing overall computational complexity while maintaining effective peak amplitude reduction
Solution Approach 2:
The patent applies local quality by computing scaling factors and applying cancellation operations selectively only at peak sample locations rather than uniformly across the entire signal. This localized approach concentrates processing effort where it is most needed (at peaks) while minimizing unnecessary computations in non-peak regions, thus reducing processing complexity without compromising peak reduction effectiveness
2Strength
If conventional CFR algorithms are used to reduce PAPR, then peak amplitudes are reduced, but processing power requirements increase
Solution Approach 1:
The patent divides the signal processing into segments where only peak samples are subjected to intensive processing. By identifying peak samples and limiting scaling factor computations and cancellation pulse applications to these specific segments, the system dramatically reduces the total processing power required compared to conventional algorithms that process the entire signal uniformly
Solution Approach 2:
The patent applies partial action by performing complete peak cancellation processing only on identified peak samples rather than on the entire signal. This selective partial processing approach reduces processing power consumption while still achieving the necessary peak amplitude reduction for PAPR control
3Strength
If conventional CFR algorithms are used to reduce PAPR, then peak amplitudes are reduced, but error vector magnitude increases
Solution Approach 1:
The patent applies local quality by restricting signal modification operations to only peak sample locations. By computing scaling factors and applying cancellation pulses locally at peak positions rather than applying uniform processing across the entire signal, the system minimizes distortion to non-peak portions of the signal, thereby maintaining lower EVM and better signal quality while still achieving effective peak reduction
4Strength
If conventional CFR algorithms are used to reduce PAPR, then peak amplitudes are reduced, but power amplifier efficiency decreases due to peak regrowth
Solution Approach 1:
The patent applies preliminary action by detecting peak samples and computing appropriate scaling factors before the actual cancellation pulse application. This preliminary identification and preparation of cancellation parameters ensures that when pulses are applied, they effectively suppress peaks without causing regrowth, thereby optimizing power amplifier efficiency by preventing the need for re-amplification of regrown peaks
Data Source
AI summary
Techniques are disclosed for the use of Crest Factor Reduction (CFR) algorithm that performs oversampling of an input signal and a cancellation pulse, and detects a set of peak samples in the upsampled input signal that exceed a predetermined threshold value. The peak samples are clustered such that a subset of the oversampled signal peaks are used to compute gain factors for the generation of a scaled truncated upsampled cancellation pulse. Several scaled truncated upsampled cancellation pulses are applied in parallel to perform peak cancellation of the highest peak in each cluster as part of an initial peak cancellation process. Any remaining peaks are canceled by iterative gain factors computation process. A final cancellation pulse is then generated by multiplying a cancellation pulse by the computed gain factors.