Advances in Noma for 6g: Key Technologies, Future Research Avenues, and Security Issues

Non-orthogonal multiple access (NOMA) is a crucial enabler for fifth-generation (5G) and sixth-generation (6G) wireless networks, addressing diverse communication needs such as enhanced network throughput, ultra-reliable low-latency communication (URLLC), robust connectivity, and consistent quality of service (QoS). NOMA provides advantages over conventional multiple access (MA) systems, including support for densely deployed networks, heightened fairness among wireless users, efficient bandwidth utilization, and enhanced device connectivity. Its efficacy depends on appropriate resource allocation strategies, including user pairing and power distribution. This paper examines the function of NOMA in 5G and 6G networks, including MA mechanisms, variations of NOMA, integrated sensing and communication (ISAC), and NOMA inside integrated satellite-terrestrial networks (ISTN). It classifies resource allocation tactics and emphasizes solutions that use deep learning (DL) and machine learning (ML) for user segmentation, enhancing spectral efficiency (SE) and energy efficiency (EE). Furthermore, it delineates prospective research avenues to enhance the integration and efficacy of NOMA in forthcoming network generations.