Randomization Approaches for Reducing PAPR with Partial Transmit Sequences and Semidefinite Relaxation
Information Theory
2018-06-06 v2 math.IT
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.
Keywords
Cite
@article{arxiv.1805.04666,
title = {Randomization Approaches for Reducing PAPR with Partial Transmit Sequences and Semidefinite Relaxation},
author = {Hirofumi Tsuda and Ken Umeno},
journal= {arXiv preprint arXiv:1805.04666},
year = {2018}
}