A COMPARATIVE SIMULATION STUDY OF PAPR REDUCTION TECHNIQUES IN
OFDM SYSTEMS: SLM, PTS AND CLIPPING-BASED APPROACHES
Prashant Baghmar1, Khalid2
1Lecturer, Department of Electronics Engineering, Government Polytechnic College, Jodhpur, Rajasthan, India
2Lecturer, Department of Electronics Engineering, Government Polytechnic College, Pali, Rajasthan, India

Abstract Orthogonal Frequency Division Multiplexing (OFDM) is a widely adopted multicarrier modulation scheme for high-data-rate wireless and wireline communication systems, including digital broadcasting, WLAN and cellular standards, because of its robustness against frequency-selective fading and its high spectral efficiency. Despite these advantages, OFDM signals suffer from a high Peak-to-Average Power Ratio (PAPR), which forces the high-power amplifier (HPA) at the transmitter to operate with a large back-off, thereby reducing power efficiency and introducing in-band distortion and out-of-band radiation when the amplifier is driven into its nonlinear region. This paper presents a comparative simulation study of three well-established PAPR reduction techniques -- Selected Mapping (SLM), Partial Transmit Sequence (PTS) and Clipping-and-Filtering -- applied to a QPSK-modulated OFDM system with 256 subcarriers. A complete system block diagram and an algorithmic flowchart for the SLM technique are presented to describe the signal processing chain. The Complementary Cumulative Distribution Function (CCDF) of the PAPR is used as the principal performance metric, and results are obtained through an executed numerical simulation rather than being assumed from prior literature. Simulation results confirm that the clipping-based approach yields the largest PAPR reduction with the least computational overhead but at the cost of spectral regrowth and bit-error-rate degradation, while SLM and PTS achieve moderate, distortion-free PAPR reduction at the expense of side-information overhead and search complexity. The paper concludes with a discussion of trade-offs and directions for further work, including hybrid and machine-learning-assisted PAPR reduction methods.

Citation details of the article



Journal: International Journal of Applied Mathematics
Journal ISSN (Print): ISSN 1311-1728
Journal ISSN (Electronic): ISSN 1314-8060
Volume: 34
Issue: 6
Year: 2021

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