UAV-Adapted Congestion Control

• UAV-adapted congestion control algorithms optimize data flow by dynamically adjusting transmission thresholds to manage interference and queueing losses.
• Cross-layer protocols incorporating altitude-adaptive delay targeting and telemetry headroom reservation ensure low latency and robust command/control transmission.
• Reinforcement learning and Markov-based models enhance system resilience to environmental uncertainties and support real-time urban traffic signal management.

A UAV-adapted congestion control algorithm refers to a class of methodologies and protocol refinements specifically tailored for unmanned aerial vehicle (UAV) systems, where the unique constraints of aerial deployment—including wireless link intermittency, mobility-induced loss/latency, interference, onboard resource limitations, and mission-critical packet priorities—require adaptations beyond classical congestion control schemes. This article synthesizes recent advances, including queuing theory for distributed data-plane regulation in UAV mesh networks, cross-layer delay control for video/telemetry transmission, traffic flow stabilization under environment uncertainty, and @@@@1@@@@ for urban congestion mitigation using UAVs as instrumental sensors and actuators.

1. Interference- and Queue-Aware Transmission Control in UAV Networks

Interference-aware congestion control in UAV wireless networks aims to optimize expected throughput under both queueing constraints and spectral interference. The primary technical framework, introduced in "Interference-Aware Queuing Analysis for Distributed Transmission Control in UAV Networks" (Ghazikor et al., 2024), models each node as having an M/M/1 queue with finite buffer capacity $B_n$ and a packet arrival process of intensity $\lambda_n$. Transmission is regulated by a threshold policy, controlling the probability of sending under current channel conditions, with each packet subject either to buffer overflow or deadline expiration losses.

Analytical Model and Loss Probabilities

• Delay Loss: For each node $n$, the probability a packet exceeds its delay threshold $T_n^{th}$ is

$$P_n^{dly}(\beta_n) = \exp\left[ - (\mu_n(\beta_n) - \lambda_n) T_n^{th} \right]$$

with $\mu_n(\beta_n)$ the effective slot service probability dependent on threshold $\beta_n$.

• Buffer Overflow: The probability of buffer overflow is approximated via Erlang loss:

$$P_n^{ov}(\beta_n) \approx \frac{[1-\rho_n(\beta_n)]\,e^{-B_n\eta_n[1-\rho_n(\beta_n)]}}{1-\rho_n(\beta_n) e^{-B_n\eta_n[1-\rho_n(\beta_n)]}}$$

where $\rho_n$ is the offered load.

• Interference-induced Outage Probability: Outage due to low SINR is modeled as

$$P_n^{out}(\boldsymbol\beta) = \int_{\beta_n}^\infty f_{\tilde h}(x)\, \Pr\left\{ I_n^f > \frac{P_n \hat h_n^2 x^2}{\gamma_{th} - \sigma^2} \right\}\, dx$$

with in-band interference $I_n^f$ modeled by a Gamma distribution.

• Expected Throughput: Aggregate slot-wise throughput is

$$R_n(\boldsymbol\beta) = \lambda_n [1 - P_n^{ov}(\beta_n) - P_n^{dly}(\beta_n) - P_n^{out}(\boldsymbol\beta)]$$

Algorithmic Approaches

Interference-Aware Transmission Control (IA-TC): A coordinate-descent algorithm optimizing channel selection thresholds $\boldsymbol\beta$ per link, forcing competing sessions to be conservative under high interference conditions, then tuning the subject link for best trade-off between queuing and channel loss.

Interference-Aware Distributed Transmission Control (IA-DTC): A distributed consensus method, where each node operates as both actor and neighbor. Each runs local search, shares its optimal action, and the procedure iterates toward a Pareto-optimal $\boldsymbol\beta^\star$ vector, maintaining fairness and maximizing network-wide expected throughput.

Tuning Guidelines

• Arrival rate $\lambda_n \uparrow$: Decrease $\beta_n$, transmit more aggressively to avoid queue drops.
• Buffer size $B_n \uparrow$: Raise $\beta_n$, allow more waiting for better channel conditions.
• Interferer density $N \uparrow$ or SINR threshold $\gamma_{th} \uparrow$: Increase $\beta$, transmit more selectively.

2. Cross-Layer Congestion Control for UAV Multimedia and Telemetry

The AQUILA architecture integrates a UAV-adapted congestion control algorithm, SCReAM-FPV, extending the delay-based SCReAM (RFC 8298) paradigm with two critical adaptations: altitude-adaptive delay targeting and telemetry headroom reservation (Huang et al., 7 Dec 2025).

Protocol Enhancements

• Altitude-Adaptive Delay Targeting: Rather than fixing a queueing-delay setpoint, the target delay is dynamically scaled to $$d_{\rm target}(t) = \max(d_{\rm base},\, \kappa\, \min_{k \in [t-W, t]} RTT(k))$$, utilizing the minimum RTT observed over a window $W$ to absorb handover-induced spikes.
• Telemetry Headroom Reservation: Command-and-control (C2) traffic (e.g., MAVLink) is allocated a statically reserved rate $R_{\rm safe}$, subtracted from the instantaneous transmission rate $R_{tx}$. The remnant is sent to the video encoder, and any unused "credit" is banked for latency-sensitive packets.

Stepwise Logic

• On ACK receipt, compute queueing delay estimate $\hat d_q(t) = RTT(t) - RTT_{\min}(t)$.
• If $\hat d_q(t) \leq d_{\rm target}(t)$: increase congestion window (additive).
• Otherwise: decrease window multiplicatively based on the delay overshot fraction.
• Set encoder rate $R_{enc}$ subject to bounds, damping, and reserved headroom.
• Credit is accumulated to let C2 packets bypass pacing and maintain bounded latency.

Performance Summary

• SCReAM-FPV yields:
  • Video one-way latency as low as 213 ms and VMAF 92.20 on LTE traces (vanilla SCReAM: 484 ms, VMAF 56.68).
  • PLR for C2 traffic $\approx 0$ throughout; C2 latency ≤ 150 ms, even during link saturation.
  • Reconnection time after handover (QUIC 0-RTT) is halved versus TCP/TLS.
  • Survives severe bottlenecks (1 Mbps/20 ms): VMAF 39.85, while standard protocols collapse.

3. Resilient UAV Traffic Congestion Control under Environmental Uncertainty

Traffic congestion control for UAV fleets in the airspace leverages continuous-time fluid queuing models, where link capacities fluctuate as a Markov chain driven by weather uncertainty (Zhou et al., 2019). This aligns capacity allocation and inflow regulation for single-point, tandem, and merge linkage topologies.

Mathematical Formulation

• Queue Dynamics: For a queue with inflow $a$ and mode-dependent capacity $c_i$:

$$\dot Q(t) = a - f(I(t), Q(t)),\quad f(i, q) = \begin{cases} c_i, & q > 0 \ \min(c_i, a), & q=0 \end{cases}$$

• Markov Chain: Weather state $I(t)$ follows a CTMC with generator $Q = (\lambda_{ij})$.
• Stability (Resilience) Condition: Long-term stochastic stability if

$\sum_{i=1}^m p_i c_i \ge a$

for a single queue, or for tandem/merge links, analogous aggregate inequalities.

• Throughput Optimization: The largest inflow $a_s$ guaranteeing stability can be found via bisection on feasible bilinear Lyapunov inequalities.

Real-Time Congestion Control

Discrete-time event-driven pseudocode at each $\Delta t$:

• Measure queue lengths and current mode.
• Compute provisional capacities, allocate flows proportionally.
• Update queue states and advance CTMC.

Practical Insights

• Sufficient stability bound $a_s$ is typically below the necessary average $\bar a$ during sustained adverse weather modes.
• Mode-switching intensity $\lambda_{ij}$ "smooths" short-term variance.
• Offline cataloguing of safe inflow regimes enables operational enforcement of $a \le a_s$.

4. UAV-Actuated Urban Traffic Signal Congestion Control

The AVARS system explores UAVs as rapid-response actuators for urban traffic signals, employing deep reinforcement learning (DRL) for congestion alleviation (Guo et al., 2023). UAV-mounted cameras enable high-frequency, high-resolution traffic monitoring at targeted intersections.

MDP and Algorithmic Framework

• MDP Structure: Each UAV–traffic intersection pair is modeled as $(S, A, P, R, \gamma)$.
  • State space $S$: Concatenated current phase, per-lane occupancy $O_i(t)$, and speed $v_i(t)$.
  • Action space $A$: Binary, $\{0,1\}$ (hold/switch phase).
  • Reward: $r_t = - \max_{i \in I} O_i(t)$, directly penalizing peak congestion.
• UAV Constraints: Battery lifetime as episode horizon ($B_0$); coverage limited to $R_{max}$.
• DRL Methods: DQN (value-based) and PPO (policy-gradient), both with lightweight MLP architectures.

Control Procedure Outline

• Pre-train policy networks in simulation.
• On event detection, deploy UAVs to highest-impact intersections.
• At each time step:
  • Observe local state.
  • Select/execute action (signal phase switch or hold).
  • Update local experience buffer.
  • Decrement battery.
• Post-episode, aggregate trajectories and update central DRL model.
• Repeat as untoward events arise and for continual fine-tuning.

Empirical Results

Metric	Original	Congestion	SCATS	IntelliLight	AVARS
Travel time (s)	275.8	597.3	429.6	232.7	195.4
Fuel cons. (l/100 km)	20.14	45.45	31.69	14.60	12.31
CO$_2$ (g/km)	468.6	1057.2	737.1	339.6	286.3

AVARS achieved up to 73% reductions in delay and emissions, outperforming fixed-time, loop-adaptive, and conventional DRL baselines within regular UAV battery duration.

5. Design Trade-Offs and Tuning Guidelines

UAV-specific congestion control algorithms are characterized by critical trade-offs:

• Lowering transmission or control activation thresholds reduces in-band interference or control-induced queueing loss, but risks increased packet drop or reduced control responsiveness.
• Buffer sizes permit more tolerance for channel variability, allowing more aggressive congestion control strategies.
• Environmental variables (weather, interferer density, SINR thresholds) must be actively measured and incorporated into control adaptation logic.
• Offline parameter space exploration (Lyapunov-based feasibility checks) should precede deployment; online algorithms require slot-wise adaptation based on measured arrival rates, delay constraints, and network interference profile (Ghazikor et al., 2024, Zhou et al., 2019).

6. Impact and Future Directions

UAV-adapted congestion control is central to the practical deployment of multi-UAV systems for communication, sensing, cloud offloading, and autonomous mobility. Methodological advances—such as interference-aware queuing, cross-layer delay-based protocols, resilience modeling via Markov fluid queues, and real-time DRL for urban applications—significantly improve both safety-critical link integrity and end-user quality of service. Future directions include:

• Integration of UAV congestion control with next-generation cellular and satellite networks.
• Joint optimization of energy, spectrum, and data plane for SWaP (size, weight, and power)-limited airborne platforms.
• Robust control under adversarial or non-stationary environments.
• Extension to coordinated multi-agent UAV traffic management in dense urban or disaster-response scenarios.

These frameworks lay the technical foundation for reliable, scalable UAV operations under realistic wireless and traffic conditions.