pith:A2RFAJQX
DQN-Driven Adaptive Neighbor Discovery for Directional Aerial Networks
DQN agents let directional aerial nodes adapt probing to balance reachability against privacy using only local observations.
arxiv:2605.12552 v1 · 2026-05-11 · cs.NI · eess.SP
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Claims
Results show that DQN framework surpasses the Random and Q-Learning baselines. Weights favoring discovery provide higher probing efficiency and reachability, while weights prioritizing privacy ensure limited exposure at the cost of low reachability, eventually attaining higher objective value.
Each node acts as an independent DQN agent and interacts with the environment to learn how to balance the trade-off based solely on local observations in a mobile setting.
DQN agents learn to adaptively tune probing in directional aerial networks to optimize the reachability-privacy trade-off, outperforming random and Q-learning baselines.
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| First computed | 2026-05-18T03:10:02.115483Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
06a2502617a13911e25c2eebda7f80f9687e88c2ed158d84e9258239fdec420d
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/A2RFAJQXUE4RDYS4F3V5U74A7F \
| jq -c '.canonical_record' \
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# expect: 06a2502617a13911e25c2eebda7f80f9687e88c2ed158d84e9258239fdec420d
Canonical record JSON
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