Physical layer key generation (PLKG) has emerged as a promising solution for achieving highly secured and low-latency key distribution, offering information-theoretic security that is inherently resilient to quantum attacks. However, simultaneously ensuring a high data transmission rate and a high secret key generation rate under eavesdropping attacks remains a major challenge. In time-division duplex (TDD) systems with multiple antennas, we derive closed-form expressions for both rates by modeling the legitimate channel as a time-correlated autoregressive (AR) process. This formulation leads to a highly nonconvex and time-coupled optimization problem, rendering traditional optimization methods ineffective. To address this issue, we propose a multi-agent soft actor-critic (SAC) framework equipped with a long short-term memory (LSTM) adversary prediction module to cope with the partial observability of the eavesdropper's mode. Simulation results demonstrate that the proposed approach achieves superior performance compared with other benchmark algorithms, while effectively balancing the trade-off between secret key generation rate and data transmission rate. The results also confirm the robustness of the proposed framework against intelligent eavesdropping and partial observation uncertainty.

翻译：物理层密钥生成已成为实现高安全、低延迟密钥分发的有前景解决方案，其提供的信息论安全性天然具备抗量子攻击能力。然而，在窃听攻击下同时保障高数据传输速率与高密钥生成速率仍面临重大挑战。在多天线时分双工系统中，我们通过将合法信道建模为时间相关自回归过程，推导出两种速率的闭式表达式。该建模导致了一个高度非凸且时间耦合的优化问题，使得传统优化方法难以奏效。为解决此问题，我们提出了一种配备长短期记忆窃听模式预测模块的多智能体柔性演员-评论家框架，以应对窃听者模式的部分可观测性。仿真结果表明，相较于其他基准算法，所提方法在有效平衡密钥生成速率与数据传输速率之间权衡的同时，实现了更优的性能。结果同时证实了该框架对智能窃听与部分观测不确定性的鲁棒性。