Delivering Ultra-Reliable Low-Latency Communications via Statistical Radio Maps

TL;DR

This paper introduces a framework using statistical radio maps to predict channel conditions and ensure ultra-reliable low-latency communications by leveraging spatial correlation of channel statistics.

Contribution

It proposes a novel approach to exploit long-range spatial correlation of channel statistics through radio maps, including location-based and channel chart methods, for URLLC.

Findings

Predicts channel capacity distribution using radio maps.

Demonstrates rate selection for reliability in new locations.

Discusses future research directions in the field.

Abstract

High reliability guarantees for Ultra-Reliable Low-Latency Communications (URLLC) require accurate knowledge of channel statistics, used as an input for rate selection. Exploiting the spatial consistency of channel statistics arises as a promising solution, allowing a base station to predict the propagation conditions and select the communication parameters for a new user from samples collected from previous users of the network. Based on this idea, this article provides a timely framework to exploit long-range channel spatial correlation through so-called statistical radio maps, enabling URLLC communications with given statistical guarantees. The framework is exemplified by predicting the channel capacity distribution both in a location-based radio map and in a latent space rendered by a channel chart, the latter being a localization-free approach based on channel state information (CSI). It is also shown how to use the maps to select the transmission rate in a new location that achieves a target level of reliability. Finally, several future directions and research challenges are also discussed.