OFDM PAPR Reduction via Sparse Partial-Update Transforms
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently reducing the Peak to Average Power Ratio (PAPR) of OFDM signals, which affects transmission efficiency and hardware requirements.
Innovation Solution
The use of partial updates and selective mapping techniques to optimize PAPR reduction in OFDM signals, including MIMO-OFDM, spread-OFDM, and SC-FDMA, by employing stochastic partial update algorithms and sparse invertible transforms to minimize computational complexity and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional PAPR reduction techniques are applied to OFDM signals, then PAPR is reduced, but computational complexity increases
Solution Approach 1:
The patent segments the OFDM signal into multiple candidate sequences by applying different unitary transforms (such as DFT matrices) to the original signal. Instead of processing the entire signal uniformly, it divides the signal space into multiple transformed versions and selects the one with lowest PAPR, thereby reducing the need for complex exhaustive search while achieving PAPR reduction
Solution Approach 2:
The patent employs partial update algorithms where only a subset of signal parameters or candidates are updated or evaluated in each iteration rather than complete reprocessing. This partial action approach reduces computational burden by focusing only on the necessary transformations needed to achieve acceptable PAPR levels
2Object-affected harmful factors
If exhaustive search methods are used to find optimal PAPR reduction, then PAPR reduction performance is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary transformations by generating multiple candidate sequences through unitary transforms before actual transmission. By pre-computing these transformed candidates and selecting the best one based on PAPR criteria, the system avoids time-consuming exhaustive search during real-time operation, thus reducing processing time while maintaining performance
Solution Approach 2:
The patent changes the parameter space by applying different unitary transform matrices (such as DFT with different dimensions or types) to generate candidate sequences. This parameter transformation approach allows the system to explore the signal space efficiently and find low-PAPR solutions without exhaustive search, balancing performance and processing time
3Productivity
If advanced PAPR reduction algorithms are implemented, then transmission efficiency is improved, but power consumption increases
Solution Approach 1:
The patent extracts and applies unitary transforms (such as DFT operations) to generate candidate sequences for PAPR reduction. By selectively applying these transforms only to the necessary signal components and candidates, rather than processing the entire signal with full complexity algorithms, the system achieves transmission efficiency improvement while minimizing the additional power consumption that would result from exhaustive processing
Data Source
AI summary
Certain aspects of the present disclosure generally relate to wireless communications. In some aspects, a wireless device reduces a peak-to-average power ratio (PAPR) of a discrete-time orthogonal frequency division multiplexing (OFDM) transmission by selecting a signal with low PAPR from a set of candidate discrete-time OFDM signals. The wireless device may generate a partial-update discrete-time OFDM signal by performing a sparse transform operation on a base data symbol sequence, and then linearly combine the partial-update discrete-time OFDM signal with a base discrete-time OFDM signal to produce an updated discrete-time OFDM signal, which is added to the set of candidate discrete-time OFDM signals. Numerous other aspects are provided.