NORMALIZATION OF HILBERT MATRIX FOR REDUCTION OF PEAK-TO-AVERAGE POWER RATIO IN OFDM COMMUNICATION SYSTEM PAPER

Abstract

An important drawback of Orthogonal Frequency Division Multiplexing is high Peak-to-
Average Power Ratio (PAPR) of the transmitted signal. This high PAPR affects efficiency and
accuracy of the system. It creates distortions in the amplifier because of saturation, or the
unnecessary use of large range amplifier which increases noise. Selective Mapping (SLM) is
a signal processing technique normally used for PAPR reduction for Orthogonal Frequency
Division Multiplexing (OFDM). Out of multiple alternatives, it chooses the mapping code that
gives the best PAPR. The system performance is largely affected by the nature and design of
the Phase Rotation Vectors (PRVs) used in the mapping technique. Some of the techniques
also has to send large information over the communication channel for the receiver to know
the actual PRV used in the mapping. In this paper, we investigate the use of different
normalization schemes on a Hilbert matrix as the PRVs for the SLM technique to find the
technique with the best PAPR values. The results showed that normalisation of Hilbert matrix
using Eigen Vector and Eigen Value technique gives the highest PAPR gain of 1.8dB over non
normalised matrix. Matrix determinant normalisation and Euclidean normalisation technique
provided 0.7dB gains.