基于扩散的分子通信网络最优解调的马尔可夫方法
A Markovian Approach to the Optimal Demodulation of Diffusion-based Molecular Communication Networks
摘要：
在基于扩散的分子通信网络中，发射器和接收器通过使用流体介质中的信号分子（或配体）进行通信。本文假设发射器利用不同的化学反应产生不同的信号分子发射模式来代表不同的传输符号，接收器由受体组成。当信号分子到达接收器时，它们可以与受体反应以形成配体-受体复合物。我们的目标是研究解调在这种设置假设发射机和接收机同步。我们推导出一个最佳的解调器，使用连续的历史的复杂的数量在接收机作为输入到解调器。我们首先推导出一个通信模型，其中包括发射器中的化学反应，传输介质中的扩散和接收器中的配体-受体过程。该模型采用连续时间马尔可夫过程的形式，由于化学反应和扩散的随机性，捕获了接收器信号中的噪声。然后，我们采用了最大后验框架，并使用贝叶斯滤波来获得最佳解调器。我们用数值例子来说明这种最佳解调器的性能。
英文摘要：
In a diffusion-based molecular communication network, transmitters and receivers communicate by using signalling molecules (or ligands) in a fluid medium. This paper assumes that the transmitter uses different chemical reactions to generate different emission patterns of signalling molecules to represent different transmission symbols, and the receiver consists of receptors. When the signalling molecules arrive at the receiver, they may react with the receptors to form ligand-receptor complexes. Our goal is to study the demodulation in this setup assuming that the transmitter and receiver are synchronised. We derive an optimal demodulator using the continuous history of the number of complexes at the receiver as the input to the demodulator. We do that by first deriving a communication model which includes the chemical reactions in the transmitter, diffusion in the transmission medium and the ligand-receptor process in the receiver. This model, which takes the form of a continuous-time Markov process, captures the noise in the receiver signal due to the stochastic nature of chemical reactions and diffusion. We then adopt a maximum a posterior framework and use Bayesian filtering to derive the optimal demodulator. We use numerical examples to illustrate the properties of this optimal demodulator.
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。
