A Low-Complexity Compressive Sensing Algorithm for PAPR Reduction

One of the major challenges in orthogonal frequency division multiplexing system is its high peak-to-average-power ratio (PAPR). Among the existing PAPR reduction technologies, clipping is the most often used one due to its simplicity of implementation. But it induces signal distortion. In this paper, we propose a new PAPR reduction method which introduces compressive sensing theory to help the clipping and signal recovery processes. Our method has superior symbol-error-rate (SER) performance compared with traditional clipping, and at the same time has better PAPR reduction performance compared with traditional tone reservation based algorithms. What is more, different from the existing high-complexity compressive sensing based scheme which tries to solve an optimization problem, the proposed algorithm uses orthogonal matching pursuit scheme to recover the distorted signals, thereby it significantly reduces the computational complexity with the same PAPR reduction efficiency. Simulation results show that our proposed scheme can achieve dramatic PAPR reduction with only about \(10\,\%\) of the existing method, while still keeps good SER performance.