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.

翻译：定向天线系统因其高增益、远距离传输和增强的安全性，在空中网络中受到广泛关注。然而，波束对齐的要求使得邻居发现与连接任务面临挑战，尤其是在移动场景中。对于无线网络而言，隐私保护同样至关重要。然而，在确保网络全局连通性的同时，如何在探测过程中限制暴露程度的问题仍未得到充分探索。本文通过提出一种基于深度Q网络（DQN）框架的自适应收发器选择协议来解决这一权衡问题。每个节点作为独立的DQN智能体，与环境交互学习如何平衡权衡。由于定向节点仅基于局部观测运行，我们采用加权机制引导其通过自适应调整探测模式，优先考虑高可达性或隐私性。实验结果表明，DQN框架优于随机基线方法和Q-learning基线方法。侧重发现的权重可提升探测效率和可达性，而侧重隐私的权重则以较低可达性为代价限制暴露程度，最终获得更高的目标值。