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Imitation Learning Backoff: Reinforcement Learning-based Channel Access for Guaranteeing Fairness (Student Abstract)
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Proposes Imitation Learning Backoff (ILB), a DQN-based contention window optimization for UAV channel access that imitates CSMA/CA policy and then adds weight adjustment to improve fairness.
Problem
Contention-based channel access in multi-UAV networks where a few nodes monopolize the shared channel, causing unfairness in reinforcement learning-based contention window optimization.
Approach
Agents initially imitate a fair CSMA/CA expert policy (with probability epsilon) to align behavior and avoid monopolization, then switch to weight adjustment using new experiences, with a DQN learner and reward combining fairness and collision-avoidance terms.
Key results
- In the 3-UAV scenario, ILB+WA reaches fairness index 0.99 with throughput 0.96, versus LB fairness 0.82 and throughput 0.98.
- In the 5-UAV scenario, ILB+WA achieves fairness 0.99 and throughput 0.90, compared with CSMA/CA fairness 0.99 and throughput 0.78 and LB fairness 0.70.
- The abstract reports a fairness improvement of approximately 20% to 41% across scenarios.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace LB with imitation-learning backoff (ILB) | LB · 3-UAVs, 4-UAVs, and 5-UAVs | fairness index: 0.82 -> 0.98; 0.80 -> 0.99; 0.70 -> 0.98 | Helped |
| replace LB with imitation-learning backoff (ILB) | LB · 3-UAVs, 4-UAVs, and 5-UAVs | throughput: 0.98 -> 0.92; 0.98 -> 0.84; 0.97 -> 0.80 | Hurt |
| replace LB with imitation-learning backoff (ILB) | LB · 3-UAVs, 4-UAVs, and 5-UAVs | delay (µs): 0.0031 -> 0.0015; 0.0038 -> 0.0020; 0.0047 -> 0.0027 | Helped |
| add weight adjustment (ablation) | ILB · 3-UAVs, 4-UAVs, and 5-UAVs | fairness index: 0.98 -> 0.99; 0.99 -> 0.99; 0.98 -> 0.99 | No clear effect |
| add weight adjustment (ablation) | ILB · 3-UAVs, 4-UAVs, and 5-UAVs | throughput: 0.92 -> 0.96; 0.84 -> 0.92; 0.80 -> 0.90 | Helped |
| add weight adjustment (ablation) | ILB · 3-UAVs, 4-UAVs, and 5-UAVs | delay (µs): 0.0015 -> 0.0014; 0.0020 -> 0.0020; 0.0027 -> 0.0025 | Helped |
Surprising findings
- The LB baseline achieves the highest throughput but the lowest fairness, because fixed priorities let a single UAV monopolize the channel.
Limitations
- Proposed models show slightly worse delay than LB.
- Throughput of ILB without weight adjustment is lower than LB.
- Evaluated only in simulated UAV scenarios with up to 5 UAVs.
Experimental setup
Benchmarks: UAV channel access simulation (3, 4, 5 UAVs)
Metrics: Jain's fairness index throughput delay
Baselines: CSMA/CA LB (Lee and Jo 2023) ILB
Builds on: Learning Backoff (LB) Deep Q-network CSMA/CA
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