Reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) has the ability to overcome the challenges of the wireless environment like random fluctuations, shadowing, and mobility in an energy efficient way when compared to multiple input-multiple output (MIMO)-NOMA systems. The NOMA system can deliver controlled channel gains, improved coverage, increased energy efficiency, and enhanced fairness in resource allocation with the help of RIS. RIS-assisted NOMA will be one of the primary potential components of sixth-generation (6G) networks, due to its appealing advantages. The analytical outage probability expressions for smart RIS-assisted fixed NOMA (FNOMA) are derived in this paper, taking into account the instances of RIS as a smart reflector (SR) and an access point (AP). The analytical and simulation findings are found to be extremely comparable. In order to effectively maximize the sum capacity, the formulas for optimal powers to be assigned for a two-user case are also established. According to simulations, RIS-assisted FNOMA surpasses FNOMA in terms of outage and sum capacity. With the aid of RIS and the optimal power assignment, RIS-AP-FNOMA offers ≈62% improvement in sum capacity over the FNOMA system for a signal-to-noise ratio (SNR) of 10 dB and 32 elements in RIS. A significant improvement is also brought about by the increase in reflective elements.

Fifth-generation (5G) advancements improve transmitter and receiver functionalities, but the propagation environment remains uncontrolled. By changing the phase of impinging waves, reconfigurable intelligent surfaces (RIS) have the potential to regulate radio propagation environments. RIS-assisted non-orthogonal multiple access (NOMA) improves spectrum efficiency while enabling massive connectivity. The uplink outage probability expressions for blind-RIS-NOMA are derived in this work using RIS as a smart reflector (SR) and RIS as an access point (AP). Extensive Monte-Carlo simulations are performed to validate the derived closed-form expressions. The optimal powers to be allocated to the users are also derived in order to maximize the uplink sum capacity. In comparison to the sub-optimal power allocation, the optimal power allocation enhances the sum capacity. In terms of sum capacity for 20 dB signal-to-noise ratio (SNR) and 32 reflecting elements, it is demonstrated that the blind-RIS-NOMA surpasses the conventional NOMA by $\approx$38%. The sum capacity and outage performances are enhanced by the addition of RIS elements.

In intelligent vehicular networks, vehicles should be able to communicate with their surroundings while traveling. This results in more efficient, safer, and comfortable driving experiences, as well as new commercial prospects in a variety of industries. Connected vehicles and autonomous vehicles expect 100% reliable connectivity without any compromise in quality. However, due to challenges such as difficult channel terrains in urban scenarios and dead zones, the reliability of current vehicle-to-infrastructure (V2I) and vehicle-to-vehicle communication systems cannot be guaranteed. The performance of vehicular networks can be considerably enhanced with reconfigurable intelligent surfaces (RIS). Non-orthogonal multiple access (NOMA) allows for massive connectivity with the surroundings. In vehicular networks, the RIS-assisted NOMA can ensure regulated channel gains, better coverage, throughput, and energy efficiency. In this work, a blind RIS-assisted fixed NOMA (FNOMA) system is proposed for a downlink V2I scenario. The closed-form analytical outage probability and throughput expressions are derived by considering RIS as an intelligent reflector and as a roadside unit. It is observed that the analytical and Monte Carlo simulation results are closely related. In simulations, it has been discovered that RIS-assisted FNOMA outperforms the traditional NOMA variants in terms of outage and throughput. Even without precise channel knowledge, blind RIS transmission outperforms traditional NOMA variants due to huge array gain. The increase in the number of reflective elements also results in a significant improvement in signal-to-noise ratio gains.