DQN-Driven Adaptive Neighbor Discovery for Directional Aerial Networks

2026-05-10

AI Summary

This study addresses the challenge of balancing connectivity and privacy under limited probing in highly dynamic directional airborne networks. The work proposes an adaptive transceiver selection protocol that, for the first time, integrates deep Q-networks (DQN) into the neighbor discovery process. Each node operates as an independent agent, learning a probing strategy from local observations and dynamically trading off connectivity against privacy through a configurable weighting mechanism. Experimental results demonstrate that the proposed approach significantly outperforms random and conventional Q-learning baselines: when prioritizing connectivity, it enhances discovery efficiency and reachability; when emphasizing privacy, it effectively constrains exposure range while achieving superior overall objective values.

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.

Problem

• Directional Aerial Networks
• Neighbor Discovery
• Privacy
• Connectivity
• Beam Alignment

Innovation

• Deep Q-Network
• Directional Aerial Networks
• Adaptive Neighbor Discovery
• Privacy-Connectivity Trade-off
• Beam Alignment