Model-Based: End-to-End Molecular Communication System Through Deep Reinforcement Learning Auto Encoder

Abstract

Molecular communication (MC) system is an emerging technology for nanoscale networks. Therefore, there is a requirement to develop a new end-to-end MC model, which may deliver new perceptions into the aspect of these nanoscale networks. This paper aims to implement the MC framework as an end-to-end deep reinforcement learning (DRL) auto encoder (AE). The technique enables training of the MC system without any information about the actual channel (medium) model. For training the receiver and transmitter, the proposed techniques are supervised learning and DRL, respectively. The results show that the performance of the DRL autoencoder (AE) based system achieves nearly the same performance as the traditional modulation and demodulation methods in term of bit-error-rate (BER) under the Gaussian noise channel but with less complexity. The proposed technique can also be joint with the other coding methods to improve their performance.