Peak Reduction and Clipping Mitigation by Compressive Sensing
Ebrahim B. Al-Safadi, Tareq Y. Al-Naffouri

TL;DR
This paper presents a novel compressive sensing-based system for reducing Peak-to-Average Power Ratio in OFDM signals, leveraging sparsity and optimized clipping signals to improve signal recovery and capacity.
Contribution
It introduces a new PAPR reduction method using compressive sensing with tailored clipping signals and advanced recovery techniques, enhancing performance over existing tone-reservation approaches.
Findings
PAPR reduced by approximately 4.5 dB in simulations.
Enhanced support recovery with weighted ℓ1 minimization.
Significant capacity increase compared to all-tone clipping systems.
Abstract
This work establishes the design, analysis, and fine-tuning of a Peak-to-Average-Power-Ratio (PAPR) reducing system, based on compressed sensing at the receiver of a peak-reducing sparse clipper applied to an OFDM signal at the transmitter. By exploiting the sparsity of the OFDM signal in the time domain relative to a pre-defined clipping threshold, the method depends on partially observing the frequency content of extremely simple sparse clippers to recover the locations, magnitudes, and phases of the clipped coefficients of the peak-reduced signal. We claim that in the absence of optimization algorithms at the transmitter that confine the frequency support of clippers to a predefined set of reserved-tones, no other tone-reservation method can reliably recover the original OFDM signal with such low complexity. Afterwards we focus on designing different clipping signals that can embed…
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