Strengths and Weaknesses of the ETSI Adaptive DCC Algorithm: A Proposal for Improvement

TL;DR

The paper assesses the ETSI Adaptive DCC algorithm for ITS-G5, focusing on how parameter choices influence convergence speed and fairness. It details the LIMERIC-inspired update mechanism, including smoothed $CBR$ estimation, $\\delta$ adjustment via $\\delta_{offset}$, and bounded updates, highlighting how a small $\\alpha$ yields tighter convergence to $CBR_t$ but slows responses to high-utilization changes. To mitigate these trade-offs, it introduces Dual-$\\alpha$ DCC, switching between $\\alpha_{low}=0.016$ and $\\alpha_{high}=0.1$ based on the observed delta dynamics with a threshold $th=10^{-5}$. Numerical and simulation results demonstrate substantial improvements in convergence speed (down to roughly 25–35% of the original convergence time) and fairness across merging groups, suggesting practical gains for dynamic vehicular densities in ITS-G5 networks. The proposed approach, if validated at larger scales, could enhance reliability and efficiency of cooperative awareness and safety messages in real-world deployments by enabling faster adaptation to changing channel conditions.

Abstract

This letter studies the adaptive Decentralized Congestion Control (DCC) algorithm defined in the ETSI TS 102 687 V1.2.1 specification. We provide insights on the parameters used in the algorithm and explore the impact of those parameters on its performance. We show how the algorithm achieves good average medium utilization while protecting against congestion, but we also show how the chosen parameters can result in slow speed of convergence and long periods of unfairness in transitory situations. Finally, we propose a modification to the algorithm which results in significant improvements in speed of convergence and fairness.

Figures (3)

• Figure 1: Comparison of achieved CBR for $\alpha=0.1$ and $\alpha=0.016$
• Figure 2: Evolution of $CBR_{s}$ for 300 ITS-Ss starting in $\delta=0.03$.
• Figure 3: Evolution of $\delta$ for vehicles in two different groups that merge.