
STAR-RIS-Enhanced NOMA-Aided Overlay Multiuser Cognitive Satellite-Terrestrial Networks with Discrete Phase Shifts Design
Author(s) -
Vibhum Singh,
Sourabh Solanki,
Rakesh Palisetty,
Carlos Luis Marcos Rojas,
Juan A. Vasquez-Peralvo,
Juan Carlos Merlano-Duncan,
Symeon Chatzinotas,
Bjorn Ottersten
Publication year - 2025
Publication title -
ieee transactions on aerospace and electronic systems
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 1.137
H-Index - 144
eISSN - 1557-9603
pISSN - 0018-9251
DOI - 10.1109/taes.2025.3575055
Subject(s) - aerospace , robotics and control systems , signal processing and analysis , communication, networking and broadcast technologies
Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) enhance the flexibility and performance of RIS by extending traditional $180^{\circ}$ half-space coverage to a full $360^{\circ}$ . Motivated by this, we explore a STAR-RIS-assisted overlay cognitive satellite-terrestrial network (OCSTN) with multiple users. In this setup, a primary satellite source communicates with terrestrial primary receivers (PRs) using the non-orthogonal multiple access (NOMA) scheme, while a decode-and-forward-based secondary transmitter (ST) facilitates primary communications in exchange for spectrum access. A STAR-RIS further aids the ST by simultaneously transmitting and reflecting superposed signals to enhance both primary and secondary communications. The analysis incorporates practical considerations, including imperfect successive interference cancellation (ipSIC) in the NOMA and overlay system, as well as quantization errors introduced by the discrete phase shifts of STAR-RIS elements. For terrestrial Nakagami- m fading and satellite shadowed-Rician fading, we derive exact and asymptotic outage probability expressions and ergodic rate bounds for primary and secondary networks. The numerical and simulation results demonstrate that the STAR-RIS-assisted OCSTN consistently achieves superior performance compared to standalone OCSTN benchmarks across key performance metrics.
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