A Survey on Intelligent Reflecting Surfaces: Wireless Communication Perspective

Abstract

Using intelligent reflecting surfaces (IRSs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey we supply a study review of IRS-assisted wireless communication starting with the principles of IRS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss, then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the IRS-assisted wireless network performance improvements. Despite its enormous promise, IRS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive IRS and the IRS deployments are compared under various wireless communication models and for single and multi-users. Lastly, we propose the challenges and potential future study areas for the IRS aided wireless communication systems.

Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology

“…The growing demand for new solutions is recognized and with the predicted technological development over the next decade (Jameel et al, 2020), it is already possible to visualize the need to relocate beyond 5G and design a new structure integrating the innovative system to meet new needs at both the individual social level (Strinati et al, 2019). An overview of 6G Wireless communications is given (Huang, Hu, et al, 2020; Zhao, 2019), including the hardware architectures that may be used to reconfigure such surfaces and the potential and problems of developing enabled wireless communications in various applications. These applications are given as follows.…”

“…In the literature, a few survey studies have been presented for the 6G. For example, Zhao (2019) presented a survey on intelligent reflecting surfaces toward 6G. Tang et al (2021) presented a survey study that addresses the applications of machine learning for end‐to‐end communications toward 6G.…”

Semantic Communication: A Survey on Research Landscape, Challenges, and Future Directions

“…The video, audio, and haptic signals are fed to the crossmodal semantic encoder, which performs explicit semantic extraction and implicit semantic inference [234]. Explicit semantic extraction leads to explicit semantics (clearly expressed / readily observable semantics) and implicit semantic inference leads to implicit semantics 15 (indirectly expressed / intentionrelated semantics), as can be seen in Fig. 25.…”

“…Interestingly, the message entropy as defined in (15) and the model entropy as defined in (13) can be related to one another. Consequently, the following theorem links the model (semantic) entropy and the message (syntactic) entropy of a source [95].…”

“…Theorem 1 (Relationship between the model entropy and the message entropy [95,Theorem 1]). The message entropy H(X) and the model entropy H(W ) that are defined in (15) and (13), respectively, are related as follows:…”