  End-to-End (E2E) learning-based concept has been recently introduced to
jointly optimize both the transmitter and the receiver in wireless
communication systems. Unfortunately, this E2E learning architecture requires a
prior differentiable channel model to jointly train the deep neural networks
(DNNs) at the transceivers, which is hardly obtained in practice. This paper
aims to solve this issue by developing a deep deterministic policy gradient
(DDPG)-based framework. In particular, the proposed solution uses the loss
value of the receiver DNN as the reward to train the transmitter DNN. The
simulation results then show that our proposed solution can jointly train the
transmitter and the receiver without requiring the prior channel model. In
addition, we demonstrate that the proposed DDPG-based solution can achieve
better detection performance compared to the state-of-the-art solutions.
