Resource Allocation and Sharing in URLLC for IoT Applications using Shareability Graphs

TL;DR

This paper introduces a graph-theoretical resource allocation method for URLLC in IoT, improving spectral efficiency and fairness in fragmented spectrum scenarios without requiring channel state information.

Contribution

It proposes a novel resource allocation approach based on shareability graphs for URLLC in IoT, addressing spectrum fragmentation and lack of CSI.

Findings

Spectral efficiency increased by up to 50% over benchmarks.

Fairness was improved alongside spectral efficiency.

Method effective in dense smart factory scenarios.

Abstract

The current development trend of wireless communications aims at coping with the very stringent reliability and latency requirements posed by several emerging Internet of Things (IoT) application scenarios. Since the problem of realizing Ultra Reliable Low-Latency Communications (URLLC) is becoming more and more important, it has attracted the attention of researchers, and new efficient resource allocation algorithms are necessary. In this paper, we consider a challenging scenario where the available spectrum might be fragmented across non-adjacent portions of the band, and channels are differently affected by interference coming from surrounding networks. Furthermore, Channel State Information (CSI) is assumed to be unavailable, thus requiring an allocation of resources based only on topology information and channel statistics. To address this challenge in a dense smart factory scenario where devices periodically transmit their data to a common receiver, we present a novel resource allocation methodology based on a graph-theoretical approach originally designed to allocate mobility resources in on-demand, shared transportation. The proposed methodology is compared with two benchmark allocation strategies, showing its ability of increasing spectral efficiency of as much as 50% with respect to the best performing benchmark. Contrary to what happens in many resource allocation settings, this increase in spectrum efficiency does not come at the expense of fairness, which is also increased as compared to benchmark algorithms.