Next-Generation Interference Management: A Narrative Review of power Allocation and SIC Enhancement in 5G/6G NOMA

Non-Orthogonal Multiple Access (NOMA) is an emerging technology to support 5G and beyond wireless networks, and it has the potential to increase spectral efficiency, connectivity, and system throughput by significant margins. At the heart of the NOMA operation is Successive Interference Cancellation (SIC), which allows user signals that have been superimposed to be separated in the power domain. Nevertheless, SIC performance is highly susceptible to interference, channel estimation errors, and inefficient power allocation, especially in heterogeneous and dense networks. This review summarizes power allocation techniques and SIC improvement techniques and follows their development to optimization-based, heuristic, hybrid, and artificial intelligence-based approaches. A discussion regarding trade-offs between sum rate, fairness, and computational complexity has been noted, and it has shown that it is important to incorporate some form of adaptive, fairness-aware, and robust power allocation to establish reliable SIC. The new trends, including the Artificial Intelligence (AI), and Machine learning (ML) integration, energy-efficient designs, massive connectivity support, cross-layer optimization, cooperative NOMA, and robust SIC to ultra-reliable low-latency communications (URLLC) in 6G networks, have been critically examined. This review specifically contributes to the field by systematically analyzing the literature to identify the major design principles, lessons learnt and future directions of intelligent, scalable and robust interference management in next-generation NOMA systems. The presented insights can be used as a guide by the researchers and practitioners to improve the performance of SIC and power allocation in dynamic and high-density wireless networks.