Congestion State Estimation via Packet-Level RTT Gradient Analysis with Gradual RTT Smoothing

Accurately estimating congestion states of the bottleneck link in mobile networks (e.g., LTE and 5G) from round-trip times (RTTs) is an important task for congestion control algorithms (CCAs). In mobile networks, RTTs fluctuate easily because they are sensitive to stochastic behavior, e.g., congestion and radio quality, and deterministic behavior, e.g., scheduling, making it difficult to estimate the congestion state accurately. In this paper, we propose an RTT-gradient analysis method for accurately estimating the congestion state (i.e., overuse/normal/underuse) by filtering stochastic noise while reducing misestimation caused by deterministic delay behavior in mobile networks. The proposed method consists of two features. The first is gradual RTT smoothing, a two-stage Kalman filter designed in series for filtering noisy RTT variations. The second is adaptive threshold clipping, which eliminates estimator instability caused by deterministic delay variations in the mobile network. Our experimental results in a commercial 5G network show that our method is more robust than a conventional method (the state estimator of Google Congestion Control (GCC)). Furthermore, simulation results using an open dataset for mobile networks show that our method improves throughput by 1.6% on average for 13 scenarios out of 16 scenarios total from GCC.