A comprehensive survey on reconfigurable intelligent surfaces (RIS) and STAR-RIS for next-generation wireless networks

Reconfigurable Intelligent Surfaces (RIS) have rapidly emerged as a pivotal innovation for future wireless communication systems, offering a low-power, cost-efficient means of enhancing spectral efficiency, signal coverage, and network adaptability. Composed of metasurfaces with controllable elements, RIS can manipulate electromagnetic wave behavior in real time, distinguishing them from traditional relaying methods and massive multiple-input multiple-output (MIMO) architectures. This article provides a comprehensive overview of RIS fundamentals, including recent advances in beam training, channel estimation, and their applications in terahertz (THz) and millimeter-wave (mmWave) transmission, security frameworks, and electromagnetic information theory (EIT). It also evaluates how artificial intelligence (AI) enhances RIS configuration, particularly under near-field conditions and in complex propagation environments. In addition, the survey explores Simultaneously Transmitting and Reflecting RIS (STAR-RIS), which extends conventional RIS capabilities by enabling full 360-degree coverage. STAR-RIS operates in three distinct modes, Mode Switching (MS), Energy Splitting (ES), and Time Switching (TS), making it well-suited for dense user deployments and high spectral reuse scenarios. The paper further examines the integration of STAR-RIS with non-orthogonal multiple access (NOMA), focusing on system design, beamforming strategies, and performance metrics such as outage probability, ergodic capacity, and symbol error rate. Results show that STAR-RIS-assisted NOMA systems offer significant advantages over conventional orthogonal multiple access (OMA) schemes in terms of efficiency and reliability. Moreover, this review addresses emerging applications of STAR-RIS in unmanned aerial vehicle (UAV) communications, vehicular (V2X) systems, mobile edge computing (MEC), simultaneous wireless information and power transfer (SWIPT), over-the-air computation (AirComp), and cooperative rate splitting. It highlights cutting-edge optimization techniques, experimental testbeds, and architectural innovations that support practical deployment. Despite their considerable potential, RIS and STAR-RIS face key challenges, including complex channel state information (CSI) acquisition, hardware scalability, adaptation to dynamic environments, and security risks. The paper concludes by identifying future research directions, such as hybrid passive-active RIS architectures, AI-based optimization frameworks, and unified standards for integration into sixth-generation (6G) wireless infrastructure. This survey aims to serve as a foundational reference for researchers and practitioners striving to deploy intelligent, high-performance wireless networks.