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TCP BBR Performance over Wi-Fi~6: AQM Impacts and Cross-Layer Insights

Shyam Kumar Shrestha, Shiva Raj Pokhrel, Jonathan Kua

Abstract

We evaluate TCP BBRv3 on Wi-Fi 6 home networks under modern AQM schemes using a fully wireless testbed and a simple cross-layer model linking Wi-Fi scheduling, router queueing, and BBRv3's pacing dynamics. Comparing BBR Internet traffic with CUBIC across different AQMs (FIFO, FQ-CoDel, and CAKE) for uplink, downlink, and bidirectional traffic, we find that FIFO destabilizes pacing and raises delay, often letting CUBIC dominate; FQ-CoDel restores fairness and controls latency; and CAKE delivers the best overall performance by keeping delay low and aligning BBRv3's sending and delivered rates. We also identify a Wi-Fi-specific effect where CAKE's rapid queue draining, while improving pacing alignment, can trigger brief retransmission bursts during BBRv3's bandwidth probes. These results follow from the interaction of variable Wi-Fi service rates, AQM delay control, and BBRv3's inflight limits, leading to practical guidance to use FQ-CoDel or CAKE and avoid unmanaged FIFO in home Wi-Fi, with potential for Wi-Fi-aware tuning of BBRv3's probing.

TCP BBR Performance over Wi-Fi~6: AQM Impacts and Cross-Layer Insights

Abstract

We evaluate TCP BBRv3 on Wi-Fi 6 home networks under modern AQM schemes using a fully wireless testbed and a simple cross-layer model linking Wi-Fi scheduling, router queueing, and BBRv3's pacing dynamics. Comparing BBR Internet traffic with CUBIC across different AQMs (FIFO, FQ-CoDel, and CAKE) for uplink, downlink, and bidirectional traffic, we find that FIFO destabilizes pacing and raises delay, often letting CUBIC dominate; FQ-CoDel restores fairness and controls latency; and CAKE delivers the best overall performance by keeping delay low and aligning BBRv3's sending and delivered rates. We also identify a Wi-Fi-specific effect where CAKE's rapid queue draining, while improving pacing alignment, can trigger brief retransmission bursts during BBRv3's bandwidth probes. These results follow from the interaction of variable Wi-Fi service rates, AQM delay control, and BBRv3's inflight limits, leading to practical guidance to use FQ-CoDel or CAKE and avoid unmanaged FIFO in home Wi-Fi, with potential for Wi-Fi-aware tuning of BBRv3's probing.

Paper Structure

This paper contains 27 sections, 22 equations, 11 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. BBRv3
  4. MU--OFDMA and IEEE 802.11ax MAC Behaviour
  5. Active Queue Management in Wi-Fi Networks
  6. Related Work and Research Gaps
  7. TCP Congestion Control and BBR Evolution
  8. Active Queue Management Systems
  9. MU-OFDMA MAC Modeling and Cross-Layer Performance
  10. Research Gaps
  11. System Model
  12. MU-OFDMA Throughput Modelling
  13. BBRv3 Fluid Modeling
  14. Cross-Layer RTT and Queue Dynamics
  15. Active Queue Management (AQM) Modeling
  16. ...and 12 more sections

Figures (11)

  • Figure 1: A residential Wi-Fi network with multiple IP cameras uploading surveillance footage and end-user devices consuming downlink content. Uplink, downlink, and bidirectional traffic compete for airtime at the access point (AP), creating realistic congestion conditions for evaluating BBRv3 and CUBIC under PFIFO, FQ-CoDel and CAKE.
  • Figure 2: BBRv3 state diagram. After Startup and Drain, the sender repeatedly cycles through the ProbeBW sub-phases (Up, Down, Refill, Cruise), adjusting $pacing\_gain$ and $cwnd\_gains$ to track bottleneck bandwidth. ProbeRTT is entered periodically ($\approx 5\,$s) to refresh the minimum RTT using a $inflight\_lo$ probing window.
  • Figure 3: Details of the modules of the Cross-layer analytical framework integrating MU–OFDMA-based MAC throughput modelling, queue dynamics, congestion signaling, and BBRv3 packet dynamics control.
  • Figure 4: Experimental testbed setup.
  • Figure 5: Throughput comparison of BBRv1, BBRv2, and BBRv3 competing with CUBIC over Wi-Fi.
  • ...and 6 more figures