DQN-Driven Adaptive Neighbor Discovery for Directional Aerial Networks
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911677020110848 |
|---|---|
| author | Sarder, Md Asif Ishrak Yuksel, Murat Bentley, Elizabeth |
| author_facet | Sarder, Md Asif Ishrak Yuksel, Murat Bentley, Elizabeth |
| contents | Directional antenna systems are gaining substantial traction for aerial networks due to their higher gain, extended transmission range, and enhanced security. However, the requirement of beam alignment makes the task of finding and reaching neighbors challenging, particularly in a mobile setting. For wireless networks, privacy concerns play an equally critical role. However, the problem of ensuring network-wide connectivity while maintaining limited exposure when probing around is still unexplored. We address this trade-off by proposing an adaptive transceiver selection protocol based on the Deep Q-Network (DQN) framework. Each node acts as an independent DQN agent and interacts with the environment to learn how to balance the trade-off. Since the directional nodes operate only based on local observations, we adopt a weighted mechanism that guides them in prioritizing either high reachability or privacy by adaptively tuning the probing patterns. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12552 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DQN-Driven Adaptive Neighbor Discovery for Directional Aerial Networks Sarder, Md Asif Ishrak Yuksel, Murat Bentley, Elizabeth Networking and Internet Architecture Signal Processing Directional antenna systems are gaining substantial traction for aerial networks due to their higher gain, extended transmission range, and enhanced security. However, the requirement of beam alignment makes the task of finding and reaching neighbors challenging, particularly in a mobile setting. For wireless networks, privacy concerns play an equally critical role. However, the problem of ensuring network-wide connectivity while maintaining limited exposure when probing around is still unexplored. We address this trade-off by proposing an adaptive transceiver selection protocol based on the Deep Q-Network (DQN) framework. Each node acts as an independent DQN agent and interacts with the environment to learn how to balance the trade-off. Since the directional nodes operate only based on local observations, we adopt a weighted mechanism that guides them in prioritizing either high reachability or privacy by adaptively tuning the probing patterns. 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. |
| title | DQN-Driven Adaptive Neighbor Discovery for Directional Aerial Networks |
| topic | Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2605.12552 |