Adaptive-Mode PAPR Reduction Algorithm for Optical OFDM Systems Leveraging Lexicographical Permutations

Abstract

In direct current optical orthogonal frequency division multiplexing (DCO-OFDM) systems, the high peak-to-average power ratio (PAPR) has been a significant challenge. Recently, lexicographical symbol position permutation (LSPP) using random permutations has been introduced as an efficient solution to reduce high PAPR. In this paper, we aim to evaluate the effectiveness of LSPP by comparing both adjacent and interleaved lexicographical permutation sequences with random lexicographical permutation sequences. Our findings demonstrate that random permutation yields superior PAPR reduction performance results when compared to adjacent and interleaved permutation. However, in scenarios with a limited number of sub-blocks, the use of adjacent and interleaved permutation becomes more favorable, as they can eliminate the possibility of generating identical permutation sequences, a drawback of random permutation. Additionally, we propose a novel algorithm to determine the optimal number of candidate permutation sequences that can achieve acceptable PAPR reduction performance while adhering to computational complexity constraints defined by the system requirements.