Securing MIMO Wiretap Channel With DDPG-Based Friendly Jamming Under Non-Differentiable Channel

6G communication systems, particularly massive Internet of Things (IoT), face critical security challenges in safeguarding transmissions against eavesdropping attacks. These challenges are exacerbated by the presence of intelligent eavesdroppers capable of exploiting impairments in wiretap channels. Traditional physical layer security (PLS) techniques, such as friendly jamming (FJ), typically rely on the differentiability and accurate availability of channel state information (CSI) to optimize performance. However, in real-world scenarios, non-differentiable channels (NDCs) resulting from hardware imperfections, mobility, and complex multi-path fading, pose significant obstacles to conventional gradient-based optimization methods. In this paper, we propose a novel deep learning-based FJ approach tailored specifically for NDC environments, where gradient-based techniques prove ineffective. Leveraging the Deep Deterministic Policy Gradient (DDPG) algorithm, our framework dynamically generates jamming signals to optimize secrecy rates while simultaneously minimizing the block error rate (BLER) at the legitimate receiver. Through extensive evaluation of realistic channel models, including both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, our proposed approach demonstrates superior security enhancements and robust performance against eavesdropping threats. The results highlight its effectiveness in securing communications under the challenging and dynamic conditions inherent to NDC environments.