Intelligent Surfaces for 6G Wireless Networks: A Survey of Optimization and Performance Analysis Techniques

Abstract

This paper surveys the optimization frameworks and performance analysis methods for large intelligent surfaces (LIS), which have been emerging as strong candidates to support next generation wireless physical platforms (6G). Due to their ability to adjust the channels through intelligent manipulations of the reflections phase shifts, LIS have shown promising merits at improving the spectral efficiency of wireless networks. In this context, researchers have been recently exploring LIS technology in depth as a means to achieve programmable, virtualized, and distributed wireless network infrastructures. From a system level perspective, LIS have also been proven to be a low cost, green, sustainable, and energy-efficient 6G solution. This paper provides a unique blend that surveys the principles of operation of LIS, together with their optimization and performance analysis frameworks. The paper first introduces the LIS technology and its physical working principle. Then, it presents various optimization frameworks that aim to optimize specific objectives, namely, maximizing energy efficiency, sum-rate, secrecy-rate, and coverage. The paper afterwards discusses various relevant performance analysis works including capacity analysis, the impact of hardware impairments on capacity, uplink/downlink data rate analysis, and outage probability. The paper further presents the impact of adopting the LIS technology for positioning applications. Finally, we identify numerous exciting open challenges for LIS-aided 6G wireless networks, including resource allocation problems, hybrid RF/VLC systems, health considerations, and localization.

Reconfigurable Intelligent Surfaces for Localization: Position and Orientation Error Bounds

“…Differently from the parameter vector in (25), in direct localization, position and orientation are directly estimated from the received signals at the receiver, with Γ = s as in (26). In this specific case, the measurement noise standard deviation is the same for all the antennas and corresponds to the thermal noise, i.e.…”

“…When such metamaterials are deployed in metasurfaces, their effective parameters can be tailored to realize a desired transformation on the transmitted, received, or impinging waves [11]- [14]. With the availability of new degrees of freedom useful to improve the network performance, the environment will be no more perceived as a passive entity, but as a meaningful support for wireless communications based applications [15]- [19], e.g., energy transfer [20], vehicular networks [21], unmanned aerial vehicle (UAV) communications [22], physical layer security [20], cognitive radio [23], electromagnetic fields (EMF)-aware beamforming [24], and many others [25]. In this context, wireless localization with RISs [26], [27] has not yet received a large attention, albeit they represent a promising candidate for enhancing positioning and orientation estimation capabilities in nextgeneration cellular networks for various 6G applications, e.g., augmented reality and self-driving cars [21], [28]- [30].…”

Intelligent Reflecting Surface Assisted Localization: Opportunities and Challenges

“…Along with the passive element reflecting surface, active large intelligent surfaces (LISs) are also exploited to enhance localization. As in [12,32], authors investigated the distributed and centralized LIS systems in terms of Cramer-Rao lower bounds (CRLB) of all the dimensions. The proposed scheme aims to increase the robustness by subdividing the reflecting surface area into smaller units and to increase the coverage to have improved positioning.…”