Implementation of Principal Component Analysis Technique in Peak to Average Power Ratio

Abstract

Wireless communication has become one of the most rapid technology evolvements in the world. Orthogonal Frequency Division Multiplexing (OFDM) has become the most efficient multi carrier modulation technique for achieving high data rate transmission in wireless communication. OFDM offers advantages such as immunity to selective fading, spectrum efficiency, and various more. Regardless of these beneficial advantages, the performance of OFDM is limited as Peak Power to Average Power Ratio (PAPR) is significantly high. This would reduce the performance of Power Amplifier (PA). Principle Component Analysis (PCA) technique is proposed to implement in the high PAPR as it identifies the directions of most variation in the data set and it reduces the data down into its basic component, dispossess any unnecessary parts. A novel framework using Principal Component Analysis in PAPR is studied. A simple framework using Covariance Matrix in Principal Component Analysis is developed in the PAPR. An evaluation of the overall performances is made using the proposed Principal Component Analysis scheme in PAPR in OFDM system. The project starts with initializing the parameter used in the OFDM system. The input data is generated randomly. Next, serial to parallel the subcarriers ready for the processing of IFFT. Then, the IFFT takes place. After that, PCA technique save the largest eigenvector and eigenvalue. Lastly, the optimal transmitted signal with the lowest PAPR is chosen. It has been observed from the obtained results that PCA technique increased the value of PAPR even when it suppressed the amplitude of the signal. When implementing PCA technique with clipping, PAPR is reduced significantly. It can be concluded that PCA technique alone do not reduced PAPR, but when combining with other methods, notable PAPR reduction can be achieved.