Adaptive Clustering of Users in Power Domain NOMA

Area of Science:

• Wireless communication networks
• Signal processing
• Evolutionary algorithms

Background:

• Power domain non-orthogonal multiple access (PD-NOMA) offers potential spectral efficiency gains for future wireless networks.
• Effective PD-NOMA performance relies on dynamic user clustering and power allocation strategies.
• Existing solutions often neglect the temporal variations in user numbers and channel conditions.

Purpose of the Study:

• To propose a novel user clustering technique for PD-NOMA systems that accounts for dynamic network conditions.
• To enhance the adaptability and efficiency of NOMA systems in real-world scenarios.
• To evaluate the performance of the proposed clustering method against existing systems.

Main Methods:

• A modified DenStream evolutionary algorithm was developed for dynamic user clustering.
• The proposed clustering technique was evaluated with the improved fractional strategy power allocation (IFSPA).
• Performance was assessed in a challenging NOMA scenario with limited channel gain differences.

Main Results:

• The proposed clustering technique successfully adapts to system dynamics, clustering all users effectively.
• It promotes uniform transmission rates across different clusters.
• A performance gain of approximately 10% was observed compared to orthogonal multiple access (OMA).

Conclusions:

• The novel clustering approach enhances PD-NOMA system adaptability to dynamic environments.
• This method offers a significant improvement over OMA, particularly in challenging channel conditions.
• The evolutionary algorithm-based clustering is a promising technique for future wireless communication networks.