PAPR Reduction in NOMA by Using Hybrid Algorithms

Abstract

Non-orthogonal multiple access (NOMA) is gaining considerable attention due to its features, such as low out-of-band radiation, signal detection capability, high spectrum gain, fast data rate, and massive D2D connectivity. It may be considered for 5G networks. However, the high peak-to-average power ratio (PAPR) is viewed as a significant disadvantage of a NOMA waveform, and it weakens the quality of signals and the throughput of the scheme. In this article, we introduce a modified NOMA system by employing a block of wavelet transform, an alternative to FFT (Fast Fourier transform). The modified system combines the details of fractional frequency and time analysis of NOMA signals. In this correspondence, we utilize an advanced partial transmission scheme (PTS), and selective mapping (SLM), and present a genetic algorithm (GA) for SLM to investigate the peak power performance of a WT-based NOMA system. The performance of WT-SLM, WT-PTS, and WT-SLM-GA methods is compared with that of the traditional NOMA-based SLM and PTS methods. The simulation results demonstrate that the proposed system effectively reduces PAPR in comparison with the traditional schemes.