DQN-Driven Adaptive Neighbor Discovery for Directional Aerial Networks

TL;DR

This paper introduces a DQN-based adaptive protocol for neighbor discovery in directional aerial networks, balancing reachability and privacy through local observations and learned probing strategies.

Contribution

It presents a novel reinforcement learning approach enabling nodes to adaptively optimize neighbor discovery while considering privacy and connectivity trade-offs.

Findings

DQN outperforms random and Q-learning baselines.

Adaptive weighting improves discovery efficiency and privacy control.

The protocol effectively balances reachability and privacy in mobile aerial networks.

Abstract

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.