Autoencoder-based Optimization of Multi-user Molecule Mixture Communication Systems
This work addresses the challenge of deploying molecular communication systems in real-world scenarios by providing an end-to-end optimization approach, though it appears incremental as it builds on existing autoencoder methods for a specific domain.
The paper tackles the problem of optimizing multi-user molecule mixture communication systems by introducing an autoencoder-based scheme that maps user symbols to molecule mixtures and decodes them via a sensor array, achieving lower symbol error rates than a baseline in single-user settings and enabling reliable communication in unknown or changing channels.
In this paper, we introduce an autoencoder (AE)-based scheme for end-to-end optimization of a multi-user molecule mixture communication system. In the proposed scheme, each transmitter leverages an encoder network that maps the user symbol to a molecule mixture. The mixtures then propagate through the channel to the receiver, which samples the channel using a non-linear, cross-reactive sensor array. A decoder network then estimates the symbol transmitted by each user based on the sensor observations. The proposed scheme achieves, for a given signal-to-noise ratio, lower symbol error rates than a baseline scheme from the literature in a single-user setting with full channel state information. We additionally demonstrate that the proposed AE-based scheme allows reliable communication when the channel is unknown or changing. Finally, we show that for multiple access the system can account for different user priorities. In summary, the proposed AE-based scheme enables end-to-end system optimization in complex scenarios unsuitable for analytical treatment and thereby brings molecular communication systems closer to real-world deployment.