An Intelligent Detoxification Function of Liver Algorithm-Partial Transmit Sequence (IDFLA-PTS) for the Reduction of Peak to Average Power Ratio in Underwater Acoustic OFDM Communication

Abstract

Intelligent algorithms in artificial intelligence have brought several benefits to digital signal processing. The boom in machine learning and intelligent systems provides new perspectives and methods to solve many research problems in Underwater Acoustic (UWA) Orthogonal Frequency Divisional Multiplexing (OFDM) communication. Partial transmit sequence is a tremendous technique for the mitigation of high Peak-to-Average Power Ratio (PAPR) in OFDM communication systems, but finding the optimum phase factors has still a few problems. In this paper, a Partial Transmit Sequence (PTS) based on an Intelligent Detoxification Function of Liver Algorithm-Partial Transmit Sequence (IDFLA-PTS) is proposed for the mitigation of PAPR in the UWA OFDM communication systems. This algorithm reduces the PAPR and the complexity of the proposed UWA OFDM model. The IDFLA-PTS is also compared with the Genetic Algorithm-Partial Transmit Sequence (GA-PTS). Besides this, the Bit Error Rate (BER) performance of the IDFLA-PTS is shown when a High Power Amplifier (HPA) is used for the BELLHOP channel model. The experimental results of the proposed IDFLA-PTS method achieved nearly optimum results with fair complexity as compared to GA-PTS and boosted the BER performance.