Convolutional Modulation With Zero-Coefficient Insertion for PAPR Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current wireless communication technologies face challenges in reducing Peak Average Power Ratio (PAPR) in high-frequency scenarios, particularly in 5G and future generations, which affects signal-to-noise ratio and power consumption, especially in massive Machine Type Communication scenarios where low PAPR is necessary for extended battery life and efficient power amplification.
Innovation Solution
The technique involves generating an output sequence through convolutional modulation by inserting zero coefficients between coefficients of an input sequence, using non-zero coefficients with values between 0 to π/2, and applying multi-path delay operations to reduce PAPR, while maintaining low complexity and improving signal-to-noise ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional modulation schemes are used in high-frequency wireless communication, then spectral efficiency is maintained, but Peak Average Power Ratio (PAPR) increases leading to reduced power efficiency and increased device power consumption
Solution Approach 1:
The patent changes the parameters of the modulation scheme by using a specific convolutional kernel with coefficients constrained to values between 0 and π/2, and by inserting N zero coefficients between input coefficients. These parameter changes transform the modulation process to inherently produce lower PAPR output sequences without requiring complex post-processing
Solution Approach 2:
The patent segments the input sequence by inserting N zero coefficients between each coefficient of the input sequence. This segmentation creates an intermediate sequence with spaced-out non-zero elements, which when convolved with the specific kernel, produces output with reduced peak power values while maintaining the essential information structure
2Duration of action of moving object
If PAPR reduction techniques are applied to improve power efficiency, then device battery life is extended, but processing complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining the convolutional kernel coefficients and pre-determining the zero-coefficient insertion pattern before actual modulation. This preliminary structuring ensures that the modulation process itself naturally produces low PAPR output without requiring complex real-time processing or iterative optimization during transmission
Solution Approach 2:
By changing the modulation parameters to use a specific kernel with coefficients in the range [0, π/2] and using a fixed zero-insertion pattern, the system achieves low PAPR through the modulation structure itself rather than through complex post-processing, thus extending battery life without significantly increasing processing complexity
3Reliability
If traditional modulation schemes are used, then implementation simplicity is maintained, but signal-to-noise ratio deteriorates due to high PAPR
Solution Approach 1:
The patent changes the modulation parameters by using a convolutional kernel with coefficients constrained to values between 0 and π/2, and by inserting N zero coefficients between input coefficients. These parameter changes fundamentally alter the output sequence characteristics to achieve lower PAPR, which directly improves signal-to-noise ratio in power-limited high-frequency scenarios
Solution Approach 2:
The patent creates a transformed copy of the input sequence through convolution with the specific kernel and zero-insertion, producing an output sequence that preserves the essential information while having fundamentally different power distribution characteristics with reduced peaks and improved signal-to-noise ratio
Data Source
AI summary
Methods, apparatus, and systems for reducing Peak Average Power Ratio (PAPR) in signal transmissions are described. In one example aspect, a wireless communication method includes determining, for an input sequence, an output sequence. The output sequence corresponds to an output of a convolutional modulation between a set of coefficients and an intermediate sequence. The intermediate sequence is generated by inserting N zero coefficients between coefficients of the input sequence. The number of non-zero coefficients in the set of coefficients is based on N, N being a positive integer. Values of the non-zero coefficients correspond to values between 0 to π/2 to reduce a peak to average power ratio of the output sequence. The method also includes generating a waveform using the output sequence.