PAPR Limiter Using Slope-Adaptive Signal Compression
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Solution Overview
Problem
Current methods for reducing Peak to Average Power Ratio (PAPR) in OFDM and SC-FDMA signals in transmitter devices face challenges in balancing PAPR reduction with spectral emissions and Error Vector Magnitude (EVM) compliance, leading to high design complexity, size, and power consumption, while meeting stringent spectral mask and EVM requirements.
Innovation Solution
A PAPR limiter unit within the transmitter device employs a sample-by-sample non-linear compression method, selecting from a set of pre-defined compression functions based on the first derivative of the signal magnitude and slope thresholds to dynamically adjust compression, reducing PAPR with minimal impact on spectral emissions and EVM.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If signal compression is applied to reduce PAPR, then the maximum power requirements are reduced, but spectral emissions and EVM deteriorate
Solution Approach 1:
The patent applies dynamic compression where the compression function is adjusted based on the instantaneous signal characteristics. The system dynamically selects from multiple pre-defined compression functions or adapts compression parameters in real-time according to the signal's amplitude distribution and statistical properties, allowing optimal balance between PAPR reduction and spectral emissions control for different signal conditions
Solution Approach 2:
The patent changes compression parameters such as compression threshold, compression factor, and function selection based on signal statistics. By monitoring signal characteristics and adjusting compression parameters dynamically, the system achieves adaptive PAPR reduction that maintains spectral mask compliance while reducing peak power requirements
2Power
If signal compression is applied to reduce PAPR, then the maximum power requirements are reduced, but Error Vector Magnitude deteriorates
Solution Approach 1:
The system dynamically adapts compression strength and function selection based on signal conditions to minimize EVM degradation. By adjusting compression parameters in real-time according to signal statistics and characteristics, the system maintains better signal fidelity while still achieving PAPR reduction, preventing excessive EVM deterioration that would occur with static compression
Solution Approach 2:
The patent applies compression selectively rather than uniformly to all signal peaks. By using partial compression on specific peaks based on their statistical significance and exceeding threshold, the system avoids over-compression that would unnecessarily degrade EVM, applying only the necessary compression to achieve target PAPR reduction
3Device complexity
If PAPR reduction is implemented, then complexity and size of transmitter are reduced, but compliance with spectral mask and EVM standards becomes difficult to maintain
Solution Approach 1:
The patent employs pre-defined compression functions and pre-calculated compression parameters that are prepared in advance based on statistical analysis of signal characteristics. This preliminary preparation allows the actual compression process to be simple and fast, maintaining low complexity while ensuring compliance through pre-optimized functions that were designed to meet spectral mask and EVM requirements
Solution Approach 2:
The system uses the inherent statistical properties of the signal itself to guide the compression process. By monitoring signal statistics and using self-adaptive compression based on observed signal characteristics, the system automatically maintains compliance without requiring complex external control mechanisms, keeping device complexity low while ensuring reliability
Data Source
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AI summary
The invention discloses a method for reduction of signal peak to average power of an input signal, which is inputted to a transmitter device. The object of the invention to design a low complexity, low effort method to reduce PAPR of signals to be transmitted, will be solved by a method comprising the following steps: detecting a moment in time by a selection algorithm when an input signal magnitude exceeds a defined magnitude threshold mth a first time, calculating a first order derivative over time of the input signal as a decision metric, comparing the metric with a slope threshold sth, selecting a signal compression function depending on whether the metric exceeds or falls below the slope threshold, performing a signal compression by a signal processing block on a sample by sample basis, wherein the selected signal compression function is applied to each of subsequent input samples to generate corresponding output samples until the input signal magnitude falls below the magnitude threshold mth.