PAPR Reduction in OFDM Signals by Self-Adjustment Gain Method
| Autor: | Miin-Jong Hao, Wei-Wu Pi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
self-adjustment gain (SAG)
TK7800-8360 Computer Networks and Communications Orthogonal frequency-division multiplexing Computer science Clipping (signal processing) 02 engineering and technology PAPR Reduction (complexity) 0203 mechanical engineering Distortion 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering ACE OFDM Transmitter 020302 automobile design & engineering 020206 networking & telecommunications Transmission (telecommunications) Hardware and Architecture Control and Systems Engineering Signal Processing clipping Electronics Algorithm Quadrature amplitude modulation Phase-shift keying |
| Zdroj: | Electronics, Vol 10, Iss 1672, p 1672 (2021) Electronics Volume 10 Issue 14 |
| ISSN: | 2079-9292 |
| Popis: | OFDM in 5G wireless communication networks has the advantages of a high transmission volume and data rate. However, the problem of a high peak-to-average power ratio (PAPR) of OFDM signals may lead to serious performance degradation and distortion in the high-power amplifier at the transmitter. In this paper, with the clipping process, the self-adjustment gain (SAG) method is proposed, to tune up the positions of the clipped signals, for reducing the PAPR of OFDM signals without increasing the error probability. The distance between the estimated and clipped signal points in the signal space is measured. An updated process is developed to produce the new signal points based on the measured distance and the self-adjustment gain that is obtained from the clipping noise power and measurement power. The simulation results show that for QPSK/OFDM, SAG reduces up to 2 dB and 0.7 dB more PAPR than ACE with one and three iterations, respectively. For 16QAM/OFDM, SAG reduces up to 1.3 dB and 0.5 dB more PAPR than ACE with one and three iterations, respectively. SAG also outperforms the active constellation extension, with the projection onto convex sets (ACE-POCS) and gradient project (SGP) methods in first two iterations. Hence, the proposed method really reduces the PAPR value more effectively, within an acceptable error probability, and its computational complexity is also much lower in comparison with those methods based on the active constellation extension (ACE) with iterations. |
| Databáze: | OpenAIRE |
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