STAR-Reconfigurable Intelligent Surface-Assisted Mobile Edge Computing Network with NOMA and RF Energy Harvesting

Simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can make the wireless communication environment more intelligent in 6G networks. In this paper, we present the performance analysis for a STAR-RIS-aided mobile edge computing (MEC) network that applied non-orthogonal multiple access (NOMA) schemes with radio frequency energy harvesting (RF EH). Specifically, this system comprises an energy-constrained user, a STAR-RIS, and a pair of access points that equipped MEC servers. One of them, hybrid access point, can transfer the RF power to the user. To investigate the performance of this system, we derive the closed-form expression of successful computation probability by using statistical characteristics of signal-to-noise ratio (SNR). Furthermore, based on this expression, we investigate the behaviors of this considered system according to its key parameters, such as average transmit signal-to-noise ratio, power allocation ratio, and the number of elements of STAR-RIS. Finally, our analysis is verified by the Monte-Carlo simulation results.