Autoencoder-based Optimization of Multi-user Molecule Mixture Communication Systems
🤖 AI Summary
This work addresses the challenge of achieving efficient and reliable communication in multi-user molecular mixture-based systems under complex conditions such as unknown or time-varying channels and heterogeneous user priorities. To this end, the paper introduces an autoencoder-based end-to-end trainable framework: the transmitter maps multi-user symbols into molecular mixtures, while the receiver employs a nonlinear cross-reactive sensor array for sampling, followed by a decoding network that jointly recovers each user’s data. Notably, the approach eliminates the need for explicit channel modeling, enables robust communication over dynamic channels, and flexibly supports differentiated quality-of-service priorities. Experimental results demonstrate that, at the same signal-to-noise ratio, the proposed method achieves lower symbol error rates for individual users compared to existing baselines and significantly enhances overall reliability and adaptability in multi-user scenarios.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
molecular communication
multi-user
molecule mixture
channel uncertainty
symbol error rate
Innovation
Methods, ideas, or system contributions that make the work stand out.
autoencoder
molecular communication
multi-user
end-to-end optimization
cross-reactive sensor array
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