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arXiv · 1805.04666

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

Hirofumi Tsuda·Ken Umeno
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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.

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Hirofumi Tsuda, Ken Umeno. 2018-05-12. Randomization Approaches for Reducing PAPR with Partial Transmit Sequences and Semidefinite Relaxation. https://arxiv.org/abs/1805.04666

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