Truncated Fourier Signal Decomposition for Efficient RF Amplification
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Solution Overview
Problem
Modern wireless communication systems face inefficiencies in power amplification due to high peak-to-average power ratio (PAPR) in signals, leading to compromised energy efficiency in RF transceivers.
Innovation Solution
Signals are decomposed into a sum of truncated Fourier series, using quantization and mapping to restrict amplitudes to a finite alphabet, and then represented as a sum of time-truncated periodic components, with spectral masks defined by Fourier coefficients and pulse shaping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If signals with high PAPR are transmitted to achieve high spectral efficiency, then communication performance is improved, but power amplification efficiency deteriorates
Solution Approach 1:
The signal is segmented into multiple periodic components through Fourier series decomposition. Each component can be processed independently, allowing the power amplifier to operate at higher efficiency by handling simpler periodic signals rather than the complex high-PAPR composite signal.
Solution Approach 2:
The signal parameters are transformed by representing the original signal as a sum of truncated Fourier series components. This parameter transformation changes the signal structure from high-PAPR to multiple lower-PAPR periodic components, improving power amplification efficiency while maintaining spectral efficiency.
2Device complexity
If quantization is applied to restrict amplitudes to finite alphabet, then device complexity is reduced, but signal representation precision deteriorates
Solution Approach 1:
The quantization process is applied segmentally to each Fourier coefficient rather than to the entire signal amplitude. This allows precise representation of each frequency component while keeping the overall system complexity manageable through structured processing of individual coefficients.
Solution Approach 2:
The Fourier series coefficients serve as intermediaries between the original continuous signal and the quantized discrete representation. By quantizing these intermediate coefficients rather than directly quantizing the signal amplitudes, the system achieves both complexity reduction and acceptable precision through the mathematical transformation.
Data Source
AI summary
Systems and methods of performing decomposition of baseband and bandpass signals into a sum of time truncated periodic components with spectral masks defined by the set of Fourier coefficients of the components and the pulse used for time truncation are provided. In one exemplary embodiment, a user equipment device includes a circuitry operable to output a set of truncated periodic components that collectively represents a sample of an input signal having information and a set of power amplifier circuitry electrically coupled to the circuitry and operable to amplify the set of truncated periodic components that collectively represents an amplified sample of the input signal.