Downlink Single-Snapshot Localization and Mapping with a Single-Antenna Receiver

TL;DR

This paper introduces a novel method for accurate downlink localization and environment mapping in 5G mmWave systems where the user has only a single antenna, leveraging sparsity and joint ML estimation.

Contribution

It proposes a practical joint ML estimation approach for single-antenna users in mmWave systems, extending localization capabilities to more challenging MISO scenarios.

Findings

Accurate localization possible even with severe LOS attenuation.

Exploits channel sparsity for computationally feasible estimation.

Derives Cramér-Rao bounds confirming theoretical limits.

Abstract

5G mmWave MIMO systems enable accurate estimation of the user position and mapping of the radio environment using a single snapshot when both the base station (BS) and user are equipped with large antenna arrays. However, massive arrays are initially expected only at the BS side, likely leaving users with one or very few antennas. In this paper, we propose a novel method for single-snapshot localization and mapping in the more challenging case of a user equipped with a single-antenna receiver. The joint maximum likelihood (ML) estimation problem is formulated and its solution formally derived. To avoid the burden of a full-dimensional search over the space of the unknown parameters, we present a novel practical approach that exploits the sparsity of mmWave channels to compute an approximate joint ML estimate. A thorough analysis, including the derivation of the Cram\'er-Rao lower bounds, reveals that accurate localization and mapping can be achieved also in a MISO setup even when the direct line-of-sight path between the BS and the user is severely attenuated.