基于自动编码器的多用户分子混合通信系统优化
Autoencoder-based Optimization of Multi-user Molecule Mixture Communication Systems
AI总结：
本文提出基于自动编码器的多用户分子混合通信系统端到端优化方案，通过编码器将用户符号映射为分子混合物，利用非线性传感器阵列进行信道采样，并通过解码器估计用户符号，实现更低的误码率和可靠通信。
摘要：
本文介绍了一种基于自动编码器（AE）的端到端优化方案，用于多用户分子混合通信系统。在所提出的方案中，每个发射机利用编码器网络将用户符号映射为分子混合物，这些混合物通过信道传播到接收端，接收端使用非线性、交叉反应性传感器阵列采样信道。解码器网络则根据传感器观测估计每个用户传输的符号。对于给定的信噪比，该方案在单用户设置下，当具有完整信道状态信息时，比文献中的基线方案具有更低的符号误码率。此外，我们还证明了所提出的基于AE的方案在信道未知或变化时也能实现可靠通信。最后，我们展示了在多址接入情况下，系统可以考虑不同用户的优先级。总结来说，所提出的基于AE的方案在复杂场景中实现了端到端系统优化，这些场景不适合分析处理，从而将分子通信系统推向实际应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
