# Beyond Massive-MIMO: The Potential of Positioning with Large Intelligent Surfaces

## TL;DR

This paper explores the potential of large intelligent surfaces (LIS) for precise positioning, deriving bounds on accuracy, analyzing phase uncertainties, and comparing centralized versus distributed deployments to enhance future wireless environment intelligence.

## Contribution

It introduces closed-form Fisher-information bounds for LIS-based positioning, analyzes phase uncertainty effects, and compares deployment strategies for improved accuracy.

## Key findings

- CRLB decreases quadratically with LIS surface-area for most positions.
- Phase uncertainty significantly increases CRLB, but it decreases with larger LIS.
- Distributed LIS deployment improves coverage and positioning performance.

## Abstract

We consider the potential for positioning with a system where antenna arrays are deployed as a large intelligent surface (LIS), which is a newly proposed concept beyond massive-MIMO where future man-made structures are electronically active with integrated electronics and wireless communication making the entire environment \lq\lq{}intelligent\rq\rq{}. In a first step, we derive Fisher-information and Cram\'{e}r-Rao lower bounds (CRLBs) in closed-form for positioning a terminal located perpendicular to the center of the LIS, whose location we refer to as being on the central perpendicular line (CPL) of the LIS. For a terminal that is not on the CPL, closed-form expressions of the Fisher-information and CRLB seem out of reach, and we alternatively find approximations of them which are shown to be accurate. Under mild conditions, we show that the CRLB for all three Cartesian dimensions ($x$, $y$ and $z$) decreases quadratically in the surface-area of the LIS, except for a terminal exactly on the CPL where the CRLB for the $z$-dimension (distance from the LIS) decreases linearly in the same. In a second step, we analyze the CRLB for positioning when there is an unknown phase $\varphi$ presented in the analog circuits of the LIS. We then show that the CRLBs are dramatically increased for all three dimensions but decrease in the third-order of the surface-area. Moreover, with an infinitely large LIS the CRLB for the $z$-dimension with an unknown $\varphi$ is 6 dB higher than the case without phase uncertainty, and the CRLB for estimating $\varphi$ converges to a constant that is independent of the wavelength $\lambda$. At last, we extensively discuss the impact of centralized and distributed deployments of LIS, and show that a distributed deployment of LIS can enlarge the coverage for terminal-positioning and improve the overall positioning performance.