A low complex PTS-SLM-Companding technique for PAPR reduction in 5G NOMA waveform

Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technology to enhance spectrum efficiency and accommodate the ever-increasing demand for higher data rates and improved connectivity. However, one of the critical challenges associated with NOMA is the high Peak to Average Power Ratio (PAPR) of transmitted signals, which can lead to signal distortion and reduced power efficiency of the framework. This paper presents a novel and efficient approach for PAPR reduction in 5G NOMA waveforms, utilizing a low-complexity Partial transmission Selection based Selective Mapping (PTS-SLM-Companding) technique. The proposed PTS-SLM technique leverages the advantages of both precoding and transform domain techniques to efficiently reduce PAPR. In the precoding stage, the transmitted signals are adaptively precoded to manipulate their amplitudes, followed by a selection process that identifies the optimal precoding scheme for each symbol. Subsequently, the selected precoding schemes are combined with a transform domain technique, namely selective mapping (SLM), which further reduces PAPR by generating alternative signal sequences with lower peak amplitudes. Simulation results demonstrate that the proposed low-complexity PTS-SLM technique effectively reduces PAPR in 5G NOMA waveforms, surpassing the performance of existing PAPR reduction methods while maintaining low computational overhead. The trade-off between PAPR reduction and computational complexity is systematically evaluated, revealing that the proposed technique achieves a favorable balance for practical implementation. It should be noted that the hybrid techniques increase gain and power efficiency to 4.34 dB and 34%, respectively, while lowering the PAPR to 3.1 dB. Further, it is noted that the bit error rate (BER) of the framework is retained while optimizing the PAPR and power spectral density performance.