Model-Based: End-to-End Molecular Communication System Through Deep Reinforcement Learning Auto Encoder
Model-Based: End-to-End Molecular Communication System Through Deep Reinforcement Learning Auto Encoder
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DOI：
10.1109/access.2019.2916701
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发表时间：
2019-05
期刊：
影响因子：
3.9
通讯作者：
Soha Mohamed;Jian Dong;Allah Rakhio Junejo;Decheng Zuo
中科院分区：
文献类型：
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作者：
Soha Mohamed;Jian Dong;Allah Rakhio Junejo;Decheng Zuo
分子通信系统是一种新兴的纳米网络技术。因此，有必要开发一种新的端到端MC模型，这可能会给这些纳米级网络带来新的看法。本文旨在将MC框架实现为端到端深度强化学习（DRL）自动编码器（AE）。该技术能够在没有任何有关实际通道（介质）模型的信息的情况下训练MC系统。对于训练接收器和发射器，所提出的技术分别是监督学习和DRL。结果表明，在高斯噪声信道下，基于DRL自编码器（AE）的系统在误码率方面与传统的调制解调方法性能相当，但复杂度较低。该技术还可以与其他编码方法相结合，以提高其性能。
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.