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Recently, reconfigurable intelligent surfaces (RIS) aided design has been introduced for next-generation wireless networks to enhance system performance. Further, Terahertz (THz) communication has emerged as a promising technology to provide ultra-wide bandwidth satisfying varieties of emerging applications in 6G. The article considers the integration of RIS with THz communication to improve the performance of the 6G network. RIS-aided THz system is utilized both active and passive beamforming at the transmitter (Tx) and RIS, respectively. We survey and highlight how RIS may impact the design of THz systems with the possible challenges and potential solutions. The phase optimization and channel estimation are discussed for the RIS-aided THz system. The impact of beamforming, hardware impairments, and signal propagation is considered. We comprehensively discuss the opportunities and challenges of RIS in THz future communication scenarios. [ABSTRACT FROM AUTHOR]

Next-generation internet of things (IoT) applications need numerous low-powered wireless mobile devices to connect with each other, having ultra-reliability and low latency. Non-orthogonal multiple access (NOMA) is a promising technology to address massive connectivity for 5G and beyond by accom- modating several users within the same orthogonal resource block. Therefore, this article explores hybrid NOMA (HNOMA) for massive multiple access in the uplink scenarios due to its higher spectral efficiency. The HNOMA includes both power domain and code domain NOMA method due to diverse channel conditions in practice. We highlight that polar coded based data transmission can achieve higher reliability and lower latency in HNOMA-based wireless networks. Further, at the base station (BS), channel state information (CSI) of each link is not perfectly available or very complex to estimate due to non-orthogonal connections. Therefore, we analyze and review the performance of uplink based system involving HNOMA transmission in imperfect CSI. Furthermore, we summarize some key technical challenges and their potential solutions in futuristic IoT applications using HNOMA transmission. Finally, we offer some design guidelines for HNOMA-based systems using a deep learning approach to implement adaptive and efficient wireless networks. [ABSTRACT FROM AUTHOR]