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

TL;DR

This work tackles URLLC in IoT under stringent reliability and latency with fragmented spectrum and no instantaneous CSI. It introduces GBA, a graph-based resource allocator using a bipartite channel–device graph and Hungarian matching to optimize orthogonal RU allocation, and extends it with Channel Shareability Graphs to enable SIC-based sharing (GBA-SIC) by forming equivalent paired devices. The approach leverages topology statistics and shareability to boost spectral efficiency by up to ~$50\%$ over benchmarks while improving fairness, with GBA-SIC yielding substantial gains in admitted devices and aging metrics in dense deployments. Overall, the paper provides a principled framework that bridges graph-based allocation and SIC-enabled sharing, offering a scalable solution for dense industrial URLLC scenarios without relying on full CSI.

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.

Figures (15)

• Figure 1: During a cycle, up to $CT$ RUs can be allocated, where each RU consists of $n_t$ subsequent OFDM symbols spread over $n_c$ adjacent subcarriers (forming a channel).
• Figure 2: Proposed approach for RU allocation with channel reuse. The highlighted part is optional, and provides the implementation of the resource sharing based on the Shareability Graph approach.
• Figure 3: Example of resource allocation using GBA. Three phases are requi-red; in the resulting allocation, devices $D_6$ and $D_3$ transmit on channel $c_1$, $D_2$ transmits on channel $c_2$, while $D_5$, $D_1$ and $D_4$ transmit on channel $C_3$.
• Figure 4: Example of device pairing. Two users $D_i$ (red) and $D_j$ (blue) have $\mathcal{S}(c,i,j,\rho)=5$ RUs available on the same channel. However, $N_c(i,j)=3$ RUs are enough for $D_i$ to attain the target reliability, meaning that the remaining $K_c(i,j)=2$ RUs are left for $D_j$ to transmit without sharing.
• Figure 5: Two examples of temporal sharing conditions on channel $c$, with $T=10$ and $\Delta=5$. We assume $N_c(1,2)=3$, and $N_c(3,4)=2$. Devices $D_1$ and $D_2$ can be paired, since $\Delta-N_c(1,2)=2$ and $|t_1-t_2|=2$. Also devices $D_3$ and $D_4$ can be paired on the same channel; in fact, even if $|t_3-t_4| =7$, which is greater than $\Delta-N_c(3,4)=3$, due to the time wrapping the value $|\min(t_3,t_4)+T-\max(t_3,t_4)|$ is equal to 3, thus low enough for the two devices to be paired, according to (\ref{['condtime']}).

Theorems & Definitions (4)

• Lemma 1
• proof
• Lemma 2
• proof