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

Deep Deterministic Policy Gradient for End-to-End Communication Systems without Prior Channel Knowledge