Wi-Fi Rate Adaptation for Moving Equipment in Industrial Environments

TL;DR

The paper investigates how Minstrel-based rate adaptation behaves under mobility in industrial Wi-Fi, evaluating latency and packet loss across static and moving SUT in three interference scenarios. Using ns-3 simulations, it shows that increasing SUT speed degrades the accuracy of MCS selection, raising mean and 99th percentile latency and, in hidden-node configurations, increasing packet drops. The results highlight the vulnerability of conventional rate adaptation to fast dynamics and interference, motivating centralized digital-twin approaches to guide data-rate decisions in real time. These findings provide a benchmark for industrial RA and lay the groundwork for future enhancements that integrate spectrum-aware planning with device mobility.

Abstract

Wi-Fi is currently considered one of the most promising solutions for interconnecting mobile equipment (e.g., autonomous mobile robots and active exoskeletons) in industrial environments. However, relability requirements imposed by the industrial context, such as ensuring bounded transmission latency, are a major challenge for over-the-air communication. One of the aspects of Wi-Fi technology that greatly affects the probability of a packet reaching its destination is the selection of the appropriate transmission rate. Rate adaptation algorithms are in charge of this operation, but their design and implementation are not regulated by the IEEE 802.11 standard. One of the most popular solutions, available as open source, is Minstrel, which is the default choice for the Linux Kernel. In this paper, Minstrel performance is evaluated for both static and mobility scenarios. Our analysis focuses on metrics of interest for industrial contexts, i.e., latency and packet loss ratio, and serves as a preliminary evaluation for the future development of enhanced rate adaptation algorithms based on centralized digital twins.

Figures (8)

• Figure 1: Physical placement of devices (one-dimensional map).
• Figure 2: Average latency (NO_INT configuration).
• Figure 3: $99$th latency percentile (NO_INT configuration).
• Figure 4: Average latency (VISIBLE configuration).
• Figure 5: $99$th latency percentile (VISIBLE configuration).