Dimensioning and Optimization of Reliability Coverage in Local 6G Networks

TL;DR

This work defines reliability coverage as the fraction of a defined service area where a wireless system satisfies a target reliability $\alpha^\star$ under a latency constraint $\gamma$, enabling a vertical-centric objective for local 6G deployments. It introduces a two-time-scale framework: non-real-time resource dimensioning to set network configuration $\theta$, and near-real-time resource optimization using reliability maps that incorporate tail-end SINR via extreme value theory and spatial radio maps. Through a 200×200 m case study with a $w$-$n$ resource space, the authors demonstrate how reliability coverage grows with bandwidth but may be hampered by interference in dense, interference-limited regimes, while higher density improves edge reliability. The paper also discusses practical steps for optimization, including EVT-enabled tail modeling, radio maps, and RIS-enabled propagation control, and outlines open challenges and future directions, such as advanced tail estimation, risk measures, and digital-twin driven multi-modal data fusion for continual reliability-coverage prediction.

Abstract

Enabling vertical use cases for the sixth generation (6G) wireless networks, such as automated manufacturing, immersive extended reality (XR), and self-driving fleets, will require network designs that meet reliability and latency targets in well-defined service areas. In order to establish a quantifiable design objective, we introduce the novel concept of reliability coverage, defined as the percentage area covered by communication services operating under well-defined reliability and performance targets. Reliability coverage allows us to unify the different network design tasks occurring at different time scales, namely resource orchestration and allocation, resulting in a single framework for dimensioning and optimization in local 6G networks. The two time scales, when considered together, yield remarkably consistent results and allow us to observe how stringent reliability/latency requirements translate into the increased wireless network resource demands.

Figures (6)

• Figure 1: Visualization of the localized network coverage of reliability-based services within an automated factory scenario with autonomous vehicles and robots.
• Figure 2: Framework overview: A vertical sets its targets ($\gamma,\alpha^\star,\eta^\star$) which are input to the Resource Orchestration that uses reliability coverage to obtain the necessary network configuration, and Resource Allocation that uses these to allocate resources based on dynamic information about the channel it obtains from a reliability coverage map. Both processes result in changes monitored through control loops of varying time scales.
• Figure 3: Reliability coverage for different $\alpha^\star$-reliability levels as a function of the bandwidth for a network density of $N = 20$AP. Note: $\eta \in [0, 1]$ and $\alpha^\star \in [0, 1]$.
• Figure 4: Reliability coverage for different network densities as a function of the bandwidth with respect to $\alpha^\star = 0.99999$ (or $99.999\%$). Note: $\eta \in [0, 1]$.
• Figure 5: Map showing $\log_{10}(\mathcal{O})$, representing the achieved outage $\mathcal{O}$ with transmissions under $\gamma \leq 1 \, \mathrm{ms}$ across the coverage area. The target $10^{-3}$ is met in 99.85% of the coverage area with a bandwidth of [50]MHz. The markers denote the positions of AP.