Randomization Approaches for Reducing PAPR with Partial Transmit Sequences and Semidefinite Relaxation

TL;DR

This paper introduces a novel randomization method using Gaussian vectors derived from a relaxed problem to select suitable vectors for PAPR reduction, improving over traditional selection methods in partial transmit sequence techniques.

Contribution

The proposed approach eliminates the need for extensive candidate selection by generating random vectors from a Gaussian distribution, leading to better PAPR reduction.

Findings

Lower peak-to-average power ratio achieved

Method simplifies vector selection process

Outperforms conventional methods in simulations

Abstract

To reduce peak-to-average power ratio, we propose a method to choose a suitable vector for a partial transmit sequence technique. With a conventional method for this technique, we have to choose a suitable vector from a large amount of candidates. By contrast, our method does not include such a selecting procedure, and consists of generating random vectors from the Gaussian distribution whose covariance matrix is a solution of a relaxed problem. The suitable vector is chosen from the random vectors. This yields lower peak-to-average power ratio, compared to a conventional method for the fixed number of random vectors.