###### Abstract  
Non-orthogonal multiple access (NOMA) has emerged as a key enabler of massive connectivity in next-generation wireless networks. However, conventional NOMA studies predominantly focus on two-user scenarios, limiting their scalability in practical multi-user environments. A critical challenge in these systems is error propagation in successive interference cancellation (SIC), which is further exacerbated by hardware distortions (HWDs). Hybrid NOMA (HNOMA) mitigates SIC errors and reduces system complexity, yet cell-edge users (CEUs) continue to experience degraded sum spectral efficiency (SSE) and throughput. Cooperative NOMA (C-NOMA) enhances CEU performance through retransmissions but incurs higher energy consumption. To address these limitations, this study integrates intelligent omni-surfaces (IOSs) into a cooperative hybrid NOMA (C-HNOMA) framework to enhance retransmission efficiency and extend network coverage. The closed-form expressions for average outage probability and throughput are derived, and a power allocation (PA) optimization framework is proposed to maximize SSE, with validation through Monte Carlo simulations. The introduction of a novel strong-weak strong-weak (SW-SW) user pairing strategy capitalizes on channel diversity, achieving an SSE improvement of \(\sim\)0.48% to \(\sim\)3.81% over conventional pairing schemes. Moreover, the proposed system demonstrates significant performance gains as the number of IOS elements increases, even under imperfect SIC (iSIC) and HWD conditions. By optimizing PA values, SSE is further enhanced by at least 2.24%, even with an SIC error of 0.01 and an HWD level of 8%. These results underscore the potential of an IOS-assisted C-HNOMA system with SW-SW pairing as a viable solution for improving multi-user connectivity, SSE, and system robustness in future wireless communication networks. c 202  
to support a vast number of connected devices, ensuring massive connectivity in diverse environments. This leap will support unprecedented connectivity and data-hungry applications, including augmented reality, remote surgery, and industrial automation, fostering an era of seamless and ultra-reliable communication networks [1; 2]. The conventional orthogonal multiple access (OMA) techniques assign distinct resources to individual user equipment (UE), limiting the number of simultaneous UE. This limitation poses a significant challenge for accommodating a large number of UE, as the available resources are quickly depleted with increasing device counts. In contrast, the non-orthogonal multiple access (NOMA) method enables different UE typesto share the same resources through power domain and code domain techniques, thereby substantially enhancing the supported UE [3]. However, this advantage comes with the trade-off of heightened complexity in signal processing and potential successive interference cancellation (SIC) issues [4]. NOMA's complexity arises primarily from the need for SIC to differentiate between UE signals, which is computationally intensive and challenging to implement effectively. Additionally, in practical scenarios, the UE often faces hardware distortions (HWDs) that can further degrade performance [5]. In conventional NOMA, as the UE increases, the complexity arising from SIC also increases. Therefore, with conventional NOMA, the UE experiences large outages, less sum spectral efficiency (SSE), and may not meet area traffic capacity (bps/km\({}^{2}\)). In a practical scenario, the perfect SIC (pSIC) is not possible. Therefore, errors occur due to imperfect SIC (iSIC), and for large UE, this error is propagated and the complexity increases, resulting in performance degradation. These drawbacks can be mitigated by employing hybrid NOMA (HNOMA) systems, which combine the advantages of both OMA and NOMA. HNOMA schemes can leverage the benefits of resource allocation from OMA while still utilizing the enhanced connectivity and capability of NOMA, providing a more robust and efficient solution for massive connectivity in real-world environments [6].