Beyond Massive-MIMO: The Potential of Data-Transmission with Large Intelligent Surfaces

TL;DR

This paper explores the concept of large intelligent surfaces (LIS) as a transformative technology surpassing massive MIMO, analyzing capacity, signal dimensions, and practical implementations for future wireless communication environments.

Contribution

It introduces the LIS concept, analyzes its capacity and signal dimensions, and discusses optimal antenna arrangements and low-complexity demodulation methods.

Findings

Capacity per surface area is derived for large LIS.

Number of independent signal dimensions is proportional to surface wavelength.

Hexagonal lattice is optimal for antenna element placement.

Abstract

In this paper, we consider the potential of data-transmission in a system with a massive number of radiating and sensing elements, thought of as a contiguous surface of electromagnetically active material. We refer to this as a large intelligent surface (LIS). The "LIS" is a newly proposed concept, which conceptually goes beyond contemporary massive MIMO technology, that arises from our vision of a future where man-made structures are electronically active with integrated electronics and wireless communication making the entire environment "intelligent". We consider capacities of single-antenna autonomous terminals communicating to the LIS where the entire surface is used as a receiving antenna array. Under the condition that the surface-area is sufficiently large, the received signal after a matched-filtering (MF) operation can be closely approximated by a sinc-function-like intersymbol interference (ISI) channel. We analyze the capacity per square meter (m^2) deployed surface, \hat{C}, that is achievable for a fixed transmit power per volume-unit, \hat{P}. Moreover, we also show that the number of independent signal dimensions per m deployed surface is 2/\lambda for one-dimensional terminal-deployment, and \pi/\lambda^2 per m^2 for two and three dimensional terminal-deployments. Lastly, we consider implementations of the LIS in the form of a grid of conventional antenna elements and show that, the sampling lattice that minimizes the surface-area of the LIS and simultaneously obtains one signal space dimension for every spent antenna is the hexagonal lattice. We extensively discuss the design of the state-of-the-art low-complexity channel shortening (CS) demodulator for data-transmission with the LIS.