Research map / Reinforcement learning / Visual and representation RL
Cellular Network Traffic Scheduling With Deep Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| RL scheduler vs proportional-headroom heuristic | IoT traffic scheduling · Melbourne cell | IoT traffic volume: 2.06× | Helped |
| RL vs stochastic MPC with the same LSTM forecasts | IoT traffic scheduling · representative test day | reward relative to oracle: 62% -> just below 80% | Helped |
| add LSTM congestion forecasts to RL state (ablation) | HVFT-RL · representative test day | reward relative to oracle | Helped |
| use more past states (C3 instead of C1) in RL state (ablation) | HVFT-RL · representative test day | reward relative to oracle | Hurt |
| shorter LSTM forecast horizon (k=2,5 instead of 7) (ablation) | HVFT-RL · representative test day | reward relative to oracle | Hurt |
| reward weight favoring IoT traffic (alpha=2 vs 1) (ablation) | HVFT-RL · Melbourne cell-days | utilization gain | Helped |
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