Abstract
The future sixth generation (6G) is going to face the significant challenges of massive connections and green communication. Recently, reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) have been proposed as two key technologies to solve the above problems. Motivated by this fact, we consider a downlink RIS-aided NOMA system, where the source aims to communicate with the two NOMA users via RIS. Considering future network supporting real-time service, we investigate the system performance with the view of effective capacity (EC), which is an important evaluation metric of delay sensitive systems. Specifically, we derive the analytical expressions of the EC of the near and far users. To obtain more useful insights, we deduce the analytical approximation expressions of the EC in the low signal-to-noise-ratio approximation by utilizing Taylor expansion. Moreover, we provide the results of orthogonal multiple access (OMA) for the purpose of comparison. It is found that (1) The number of RIS components and the transmission power of the source have important effects on the performance of the considered system; (2) Compared with OMA, NOMA system has higher EC due to the short transmission time.
Highlights
For the past few years, the sixth generation (6G) mobile communication technology has attracted a lot of attentions because of its high transmission rate, high reliability and high capacity
Compared with the above works, the main contribution of this paper is to study the effect of delay on the reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) system performance and accommodate delay quality of service (QoS) constraints so that NOMA can be employed for delay-sensitive transmissions
4 Results and discussion numerical analysis is provided to verify the accuracy of the analysis results for the considered system
Summary
For the past few years, the sixth generation (6G) mobile communication technology has attracted a lot of attentions because of its high transmission rate, high reliability and high capacity. It has great practical and economic values for the industry and Internet of Things (IoT) [1,2,3,4]. It is imperative to find solutions with low power consumption and high economic benefits for future wireless networks to meet users’ high requirements for quality of service (QoS) and data rate [5, 6]. Instead of using active transmitters, they utilize the surrounding
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