Unitary Transform PAPR Reduction in MCM Systems
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Solution Overview
Problem
Multicarrier modulation systems face high peak-to-average power ratio (PAPR) issues, leading to complex power amplifier design challenges and non-linear distortion due to limited linear regions, with existing methods like selective mapping (SLM) requiring extensive computations and complex implementations.
Innovation Solution
A method that supplements baseband signal blocks with zeros, performs oversampled IFFT, applies circular shifts, and uses unitary transforms to reduce PAPR, selecting the best candidate for transmission based on lowest peak value or clipping noise power, thereby reducing computational complexity and non-linear distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional SLM method is used to reduce PAPR, then PAPR reduction effectiveness is improved, but computational complexity increases significantly due to requiring Q inverse fast Fourier transforms with highly complicated computation
Solution Approach 1:
The patent changes the fundamental parameter of the transform operation from conventional IFFT to unitary transform. This parameter change maintains the PAPR reduction effectiveness while significantly reducing computational complexity, as unitary transforms require much fewer operations compared to multiple IFFT computations required by conventional SLM
Solution Approach 2:
The patent uses unitary transform matrices that can be pre-computed and stored as lookup tables. During signal processing, these pre-computed unitary transforms are applied through simple matrix multiplication rather than complex iterative computations, effectively copying the transform results in advance to reduce real-time computational burden
2Reliability
If high Q value is used in SLM to effectively reduce PAPR, then PAPR reduction is improved, but implementation complexity increases due to requiring high Q value and numerous IFFTs
Solution Approach 1:
The patent changes the approach from using high Q values with multiple IFFTs to using a fixed unitary transform matrix. This parameter change maintains effective PAPR reduction while simplifying implementation, as the unitary transform can be applied with a single matrix operation regardless of the number of signal blocks
Solution Approach 2:
The unitary transform matrix serves multiple functions simultaneously: it performs the PAPR reduction, handles different numbers of signal blocks, and provides the same computational complexity regardless of input size. This universal approach eliminates the need for adjusting Q values and performing multiple IFFTs for different scenarios
Data Source
AI summary
The method contains the following steps. First, in a MCM system with N sub-carriers, the baseband signal blocks Xj, j=1, 2, . . . ,B are supplemented with zeros and processed with LN-point IFFT, respectively, to obtain L-time oversampled time-domain signal blocks xj, j=1,2, . . . ,B. Then, xj undergoes Q Time Domain Circular Shifts or Frequency Domain Circular Shifts to obtain Q signal blocks {tilde over (x)}j(i<sub2>j</sub2>), ij=1, Λ, Q. Subsequently, a B×B unitary transform is performed against ( x1, {tilde over (x)}2(i<sub2>2</sub2>), . . . , {tilde over (x)}B(i<sub2>B</sub2>)). After the unitary transform, for each (i2, . . . , iB) a combination having B time-domain signal blocks is obtained as follows: ({tilde over (y)}1(i<sub2>2</sub2>, . . . , i<sub2>B</sub2>), {tilde over (y)}2(i<sub2>2</sub2>, . . . , i<sub2>B</sub2>), . . . , {tilde over (y)}B(i<sub2>2</sub2>, . . . ,i<sub2>B</sub2>))=( x1, {tilde over (x)}2(i<sub2>2</sub2>), . . . , {tilde over (x)}B(i<sub2>B</sub2>)) cU where U is the B×B unitary matrix, and c is an arbitrary constant (c≠0). Finally, the total QB−1 combinations are compared against each other to select a best candidate for transmission that could produce the lowest peak value, or the smallest PAPR, or the lowest clipping noise power.