DQN-Driven Adaptive Neighbor Discovery for Directional Aerial Networks
This work addresses the unexplored trade-off between network-wide connectivity and privacy in directional aerial networks, offering a practical solution for mobile ad hoc networks.
The paper proposes a DQN-based adaptive neighbor discovery protocol for directional aerial networks that balances reachability and privacy. The DQN framework outperforms Random and Q-Learning baselines, with weighted mechanisms allowing trade-offs between probing efficiency and exposure.
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.