Enhancing spectral efficiency in non-line-of-sight (NLoS) environments is essential as 5G networks evolve, surpassing 4G systems with high information rates and minimal interference. Instead of relying on traditional Orthogonal Multiple Access (OMA) systems to tackle issues caused by NLoS, advanced wireless networks adopt innovative models like Non-Orthogonal Multiple Access (NOMA), cooperative relaying, Multiple Input Multiple Output (MIMO), and intelligent reflective surfaces (IRSs). Therefore, this study comprehensively analyzes these techniques for their potential to improve communication reliability and spectral efficiency in NLoS scenarios. Specifically, it encompasses an analysis of cooperative relaying strategies for their potential to improve reliability and spectral efficiency in NLoS environments through user cooperation. It also examines various MIMO configurations to address NLoS challenges via spatial diversity. Additionally, it investigates IRS settings, which can alter signal paths to enhance coverage and reduce interference and analyze the role of Unmanned Aerial Vehicles (UAVs) in establishing flexible communication infrastructure in difficult environments. This paper also surveys effective energy harvesting (EH) strategies that can be integrated with NOMA for efficient and reliable energy-communication networks. Our findings show that incorporating these technologies with NOMA not only enhances connectivity and spectral efficiency but also promotes a stable and environmentally sustainable data communication system.

Enhancing spectral efficiency, especially in Non-line-of-Sight environments, is emerging as a pivotal necessity when dealing with the evolving landscape of 5G networks and beyond, sur-passing the capabilities of previous 4G systems to achieve a high information rate combined with minimal interference. Contrary to depending on the traditional Orthogonal Multiple Access (OMA) systems to tackle the issues caused by Non-line-of-Sight (NLoS) environments, advanced wireless networks are focusing on adopting innovative models such as Non-Orthogonal Multiple Access (NOMA), MIMO, IRS technology, etc. In this paper, we present technologies that can be integrated with NOMA to enhance communication reliability in environments that suffer from issues such as limited coverage and lower spectral efficiency mainly caused by huge obstacles. Our exploration includes the fundamental concepts of Cooperative NOMA (C-NOMA), Multiple Input Multiple Output (MIMO), Intelligent Reflective Surfaces (IRS), Unmanned Aerial Vehicles (UAVs), and Energy Harvesting (HV), which adds a layer of sophistication, promising not only improved connectivity but also contributing to a more reliable and environmentally friendly data trans-mission landscape.

In this paper, a wireless network architecture is considered that combines double intelligent reflecting surfaces (IRSs), energy harvesting (EH), and non-orthogonal multiple access (NOMA) with cooperative relaying (C-NOMA) to leverage the performance of non-line-of-sight (NLoS) communication mainly and incorporate energy efficiency in next-generation networks. To optimize the phase shifts of both IRSs, we employ a machine learning model that offers a low-complexity alternative to traditional optimization methods. This lightweight learning-based approach is introduced to predict effective IRS phase shift configurations without relying on solver-generated labels or repeated iterations. The model learns from channel behavior and system observations, which allows it to react rapidly under dynamic channel conditions. Numerical analysis demonstrates the validity of the proposed architecture in providing considerable improvements in spectral efficiency and service reliability through the integration of energy harvesting and relay-based communication compared with conventional systems, thereby facilitating green communication systems.

In this paper, a wireless network architecture is considered that combines double Intelligent Reflecting Surfaces (IRSs), Energy Harvesting (EH), and Non-Orthogonal Multiple Access (NOMA) with cooperative relaying (C-NOMA), to leverage the performance of Non-Line-of-Sight (NLoS) communication and incorporate energy efficiency in next-generation networks. To optimize the phase shifts of both IRSs, we employ a machine learning model that offers a low-complexity alternative to traditional optimization methods. This lightweight learning-based approach is introduced to predict effective IRS phase configurations without relying on solver-generated labels or repeated iterations. The model learns from channel behaviour and system observations, which allows it to react rapidly under dynamic channel conditions. Numerical analysis demonstrates the validity of the proposed architecture in providing considerable improvements in terms of spectral efficiency and service reliability through the integration of energy harvesting and relay-based communication, compared to conventional systems, thereby facilitating green communication systems.