The use of deep learning-based techniques for approximating secure encoding functions has attracted considerable interest in wireless communications due to impressive results obtained for general coding and decoding tasks for wireless communication systems. Of particular importance is the development of model-free techniques that work without knowledge about the underlying channel. Such techniques utilize for example generative adversarial networks to estimate and model the conditional channel distribution, mutual information estimation as a reward function, or reinforcement learning. In this paper, the approach of reinforcement learning is studied and, in particular, the policy gradient method for a model-free approach of neural network-based secure encoding is investigated. Previously developed techniques for enforcing a certain co-set structure on the encoding process can be combined with recent reinforcement learning approaches. This new approach is evaluated by extensive simulations, and it is demonstrated that the resulting decoding performance of an eavesdropper is capped at a certain error level.

翻译：由于在无线通信系统的一般编码和解码任务方面取得了令人印象深刻的成果,使用深层次的基于学习的技术来接近安全编码功能引起了对无线通信的极大兴趣,尤其重要的是开发了在不了解基本频道的情况下运作的无模式技术,例如,利用基因对抗网络来估计和模拟有条件的频道分配、作为奖励功能的相互信息估计或强化学习;在本文件中,研究了强化学习方法,特别是调查了神经网络安全编码无模式方法的政策梯度方法;以前开发的在编码过程中执行某种共同设置结构的技术可以与最近的强化学习方法相结合;这一新方法通过广泛的模拟加以评价,并证明一个窃听器由此产生的解码性功能被锁定在一定的错误水平上。