Several recent algorithms are being explored for PAPR reduction in NOMA technology and 5G:

Hybrid Algorithms:

• PTS-SLM: This combines the advantages of Partial Transmit Sequence (PTS) and Selective Mapping (SLM) algorithms. PTS divides the signal into sub-blocks and optimizes phase sequences for each sub-block, while SLM selects the best phase sequences from a pre-defined set. By combining them, PTS-SLM achieves significant PAPR reduction while maintaining low complexity.
• SLM-CT: This combines SLM with Circular Transformation (CT). CT transforms the complex-valued OFDM symbols to polar coordinates, allowing for phase adjustments without affecting the magnitude. This combination offers excellent PAPR reduction and is particularly suitable for high-order constellations.
• TR-FISTA: Tone Reservation (TR) reserves specific subcarriers for high-power users, reducing PAPR. Fast Iterative Shrinkage Threshold Algorithm (FISTA) further optimizes the power allocation for improved PAPR performance.
• QRM-MLD: This algorithm uses Quadratic Residue Mapping (QRM) combined with Maximum Likelihood Detection (MLD) to achieve efficient PAPR reduction with low complexity. It is particularly suitable for high-order modulation schemes.

Other promising algorithms:

• Companding: This technique applies a non-linear function to the signal to compress the peak values, reducing PAPR. It works well for low-order modulation schemes but can introduce signal distortion.
• Adaptive clipping: This dynamically adjusts the clipping threshold based on the signal characteristics, achieving a balance between PAPR reduction and signal distortion.
• Machine Learning-based approaches: Deep learning algorithms are being explored to learn complex relationships between signal features and PAPR. These algorithms can achieve state-of-the-art performance but often require high computational resources and extensive training data.

It's important to consider the specific requirements of the application when choosing an algorithm. Factors such as PAPR reduction performance, complexity, spectral efficiency, and hardware constraints should be carefully evaluated.

Here are some additional resources you might find helpful:

• An Efficient Hybrid PAPR Reduction for 5G NOMA-FBMC Waveforms: https://books.aijr.org/index.php/press/catalog/book/114/chapter/1073
• Low-complexity selective mapping technique for PAPR reduction in downlink power domain OFDM-NOMA: https://ieeexplore.ieee.org/document/8398247
• Sustainability | Free Full-Text | Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm: https://www.mdpi.com/2071-1050/14/15/9631/html
• (PDF) PAPR reduction in NOMA by using hybrid algorithms: https://link.springer.com/article/10.1007/s11277-023-10683-y

thank you ver much