发表机构
Technische Universität Dortmund（多特蒙德工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究下一代无线网络中通信系统需求，提出基于3GPP系统模型，用穷举搜索确定最佳波束成形配置，在有攻击者和不同网络条件下评估。结果显示基于强化学习的方法优于随机选择，Q学习在检测准确率和计算效率间权衡最佳，为无线环境提供有效决策方案。
AI 中文摘要
在下一代无线网络中，通信系统有望超越简单的数据传输，同时提供高数据速率、效率和安全性。这促使广泛采用机器学习方法来开发智能实时网络管理框架，使系统能在保持高效信息传递的同时，持续监测并应对信道变化和用户行为。在此背景下，机器学习与波束成形的集成实现了自适应和数据驱动的波束方向选择，提升了无线链路的效率和安全性。本文首先在无攻击者场景下实现了基于3GPP的系统模型，并采用穷举搜索作为参考来确定最佳波束成形配置。然后在有攻击者和不同网络可扩展性条件下对所提框架进行评估。结果表明，基于强化学习的方法，即Q学习和SARSA，在总信道容量、攻击者检测准确率和性能稳定性方面始终优于随机选择。在评估的强化学习方法中，Q学习在检测准确率和计算效率之间实现了最佳的整体权衡。我们的结果表明，所提框架为动态对抗无线环境中的联合波束成形和安全感知决策提供了一个稳定、可扩展且有效的解决方案。
英文摘要
In next-generation wireless networks, communication systems are expected to go beyond simple data transmission and simultaneously provide high data rates, efficiency, and security. This requirement has motivated the extensive adoption of machine learning methods to develop intelligent and real-time network management frameworks, enabling the system to continuously monitor and react to channel variations and user behavior while maintaining efficient information delivery. In this context, the integration of machine learning with beamforming enables adaptive and data-driven beam direction selection, improving both the efficiency and security of wireless links. In this work, a 3GPP-based system model is first implemented under a no-attacker scenario, and an exhaustive search is employed as a reference to identify the best beamforming configurations. The proposed framework is then evaluated in the presence of an attacker and under different network scalability conditions. We demonstrate that the reinforcement learning-based approaches, namely Q-learning and SARSA (State-Action-Reward-State-Action), consistently outperform random selection in terms of total channel capacity, attacker detection accuracy, and performance stability. Among the evaluated reinforcement learning methods, Q-learning achieves the best overall trade-off between detection accuracy and computational efficiency. Our results indicate that the proposed framework provides a stable, scalable, and effective solution for joint beamforming and security-aware decision-making in dynamic and adversarial wireless environments.
