We consider a multiuser diffusion-based molecular communication (MC) system where multiple spatially distributed transmitter (TX)-receiver (RX) pairs establish point-to-point communication links employing the same type of signaling molecules. To realize the full potential of such a system, an in-depth understanding of the interplay between the spatial user density and inter-user interference (IUI) and its impact on system performance in an asymptotic regime with large numbers of users is needed. In this paper, we adopt a three-dimensional (3-D) system model with multiple independent and spatially distributed point-to-point transmission links, where both the TXs and RXs are positioned according to a regular hexagonal grid, respectively. Based on this model, we first derive an expression for the channel impulse responses (CIRs) of all TX-RX links in the system. Then, we provide the maximum likelihood (ML) decision rule for the RXs and show that it reduces to a threshold-based detector. We derive an analytical expression for the corresponding detection threshold which depends on the statistics of the MC channel and the statistics of the IUI. Furthermore, we derive an analytical expression for the bit error rate (BER) and the achievable rate of a single transmission link. Finally, we propose a new performance metric, which we refer to as area rate efficiency (ARE), that captures the tradeoff between the user density and IUI. The ARE characterizes how efficiently given TX and RX areas are used for information transmission and is given in terms of bits per area unit. We show that there exists an optimal user density for maximization of the ARE. Results from particle-based and Monte Carlo simulations validate the accuracy of the expressions derived for the CIR, optimal detection threshold, BER, and ARE.
翻译:我们认为需要多用户扩散基分子通信系统(MC),在这个系统中,多个空间分布式发射机(TX)接收接收器(RX)配对建立点对点通信联系,使用相同的信号分子。为了实现这种系统的全部潜力,我们首先深入理解空间用户密度和用户间干扰(IUI)之间的相互作用及其对系统性能的影响,在一个用户众多的零时间制度下,我们采用一个三维(3-D)系统模型,具有多个独立和空间分布式点对点传输链,TX和RX分别按照普通的六角方信号分子网络定位。基于这个模型,我们首先对系统所有TX-RX链接的空间用户密度和用户间干扰(IU)之间的相互作用进行深入了解。然后,我们为RX提供了最大的可能性(ML)决定,并显示它降低到一个基于门槛的点对点对点传输链的传输链,我们从BX的精确度和RX的深度数据区域到一个可实现的传输区域,我们从BER的精确度数据中,我们从一个可实现的传输率数据流流流数据区域到一个显示一个可实现的传输率数据,我们用来显示一个可实现的传输率和新传输率。