Reconfigurable intelligent surfaces (RISs) are envisioned to transform the propagation space into a smart radio environment (SRE) to realize the diverse applications of sixth-generation (6G) wireless communication. By smartly tuning the massive number of elements via controller, an RIS can passively phase-shift the electromagnetic (EM) waves to enhance the system performance. The absence of radio-frequency (RF) chains makes RIS an energy-efficient and cost-effective solution for future wireless networks. In this paper, we explore the state-of-the-art research on different aspects of RIS-assisted communication. Specifically, we first introduce the fundamentals of RIS, including the RIS’s structure, operating principle, and deployment strategies. We then comprehensively discuss the emerging applications of RISs for 6G wireless networks. In addition, we elaborate on the crucial challenges for RIS-assisted networks, namely, RIS channel state information (CSI) acquisition and passive beamforming optimization. Furthermore, we present the recent research contributions leveraging the artificial intelligence (AI) based techniques for channel estimation, phase-shift optimization, and resource allocation in RIS-assisted networks. Finally, to provide effective guidance for future research, we highlight important research directions for realizing RIS-assisted network.

Intelligent reflecting surfaces (IRSs) are anticipated to provide reconfigurable propagation environment for next generation communication systems. In this paper, we investigate a downlink IRS-aided multi-carrier (MC) non-orthogonal multiple access (NOMA) system, where the IRS is deployed to especially assist the blocked users to establish communication with the base station (BS). To maximize the system sum rate under network quality-of-service (QoS), rate fairness and successive interference cancellation (SIC) constraints, we formulate a problem for joint optimization of IRS elements, sub-channel assignment and power allocation. The formulated problem is mixed non-convex. Therefore, a novel three stage algorithm is proposed for the optimization of IRS elements, sub-channel assignment and power allocation. First, the IRS elements are optimized using the bisection method based iterative algorithm. Then, the sub-channel assignment problem is solved using one-to-one stable matching algorithm. Finally, the power allocation problem is solved under the given sub-channel and optimal number of IRS elements using Lagrangian dual-decomposition method based on Lagrangian multipliers. Moreover, in an effort to demonstrate the low-complexity of the proposed resource allocation scheme, we provide the complexity analysis of the proposed algorithms. The simulated results illustrate the various factors that impact the optimal number of IRS elements and the superiority of the proposed resource allocation approach in terms of network sum rate and user fairness. Furthermore, we analyse the proposed approach against a new performance metric called computational efficiency (CE).