Efficiency enhancement based on allocating bizarre peaks

TL;DR

This paper introduces a novel Peak to Average Power Ratio reduction technique for OFDM systems using wavelet transformation, adaptive detection, and peak replacement, outperforming previous neural network-based methods in PAPR reduction.

Contribution

The paper proposes a new averaging technique combining wavelet transformation, adaptive detection, and peak replacement, achieving up to 80% PAPR reduction over existing methods.

Findings

Achieves up to 80% PAPR reduction compared to previous work.

Reduces PAPR by an additional 15% over prior published methods.

Simulation results confirm effectiveness under various data types.

Abstract

A new work has been proposed in this paper in order to overcome one of the main drawbacks that found in the Orthogonal Frequency Division Multiplex (OFDM) systems, namely Peak to Average Power Ratio (PAPR). Furthermore, this work will be compared with a previously published work that uses the neural network (NN) as a solution to remedy this deficiency. The proposed work could be considered as a special averaging technique (SAT), which consists of wavelet transformation in its first stage, a globally statistical adaptive detecting algorithm as a second stage; and in the third stage it replaces the affected peaks by making use of moving average filter process. In the NN work, the learning process makes use of a previously published work that is based on three linear coding techniques. In order to check the proposed work validity, a MATLAB simulation has been run and has two main variables to compare with; namely BER and CCDF curves. This is true under the same bandwidth occupancy and channel characteristics. Two types of tested data have been used; randomly generated data and a practical data that have been extracted from a funded project entitled by ECEM. From the achieved simulation results, the work that is based on SAT shows promising results in reducing the PAPR effect reached up to 80% over the work in the literature and our previously published work. This means that this work gives an extra reduction up to 15% of our previously published work. However, this achievement will be under the cost of complexity. This penalty could be optimized by imposing the NN to the SAT work in order to enhance the wireless systems performance.