Gain-adaptive STAR-RIS assisted vehicular NOMA with transmit antenna selection

This paper proposes a dynamic gain-adaptive scheme for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) based non-orthogonal multiple access (NOMA) networks (termed as ASRN) with a transmit antenna selection technique at the base station to serve two vehicular users. Unlike conventional fixed-ranking NOMA, ASRN dynamically allocates power and assigns successive interference cancellation order to users based on their channel conditions. Firstly, we derive closed-form expressions for the system outage probability, asymptotic outage behavior, and diversity order, revealing that the proposed scheme achieves a diversity gain scaling with the number of STAR-RIS elements. Then, a greedy algorithm is further proposed to jointly optimize antenna selection and NOMA users' pairing. Secondly, we introduce a key performance metric (termed as the ergodic sum rate (ESR)) and define the multiple averaging ergodic sum rate (MA-ESR) to evaluate the effectiveness of ASRN scheme in delay-tolerant networking approach, when compared to other schemes. Monte Carlo simulations demonstrate that ASRN significantly outperforms dynamic gain-adaptive STAR-RIS OMA conventional STAR-RIS NOMA/OMA, and decode-and-forward relaying schemes, offering superior outage performance, coding gains, ESR performance and MA-ESR performance, particularly in vehicular environments.