We present a comprehensive approach to the modeling, performance analysis, and design of clustered molecular nanonetworks in which nano-machines of different clusters release an appropriate number of molecules to transmit their sensed information to their respective fusion centers. The fusion centers decode this information by counting the number of molecules received in the given time slot. Owing to the propagation properties of the biological media, this setup suffers from both inter- and intra-cluster interference that needs to be carefully modeled. To facilitate rigorous analysis, we first develop a novel spatial model for this setup by modeling nano-machines as a Poisson cluster process with the fusion centers forming its parent point process. For this setup, we first derive a new set of distance distributions in the three-dimensional space, resulting in a remarkably simple result for the special case of the Thomas cluster process. Using this, total interference from previous symbols and different clusters is characterized and its expected value and Laplace transform are obtained. The error probability of a simple detector suitable for biological applications is analyzed, and approximate and upper-bound results are provided. The impact of different parameters on the performance is also investigated.

翻译：本文提出了一种针对分子簇纳米网络的建模、性能分析与设计的综合性方法，其中不同簇的纳米机器释放适当数量的分子，将其感知信息传输至对应的融合中心。融合中心通过统计指定时隙内接收到的分子数量来解码该信息。由于生物介质的传播特性，该结构同时面临簇间与簇内干扰，需进行精细建模。为便于严格分析，我们首先为该结构开发了一种新型空间模型，将纳米机器建模为泊松簇过程，而融合中心则构成其父点过程。基于此模型，我们首先推导出三维空间中的一组新距离分布，针对托马斯簇过程的特例得到了极为简洁的结果。利用该结果，表征了来自前序符号与不同簇的总干扰，并获得了其期望值与拉普拉斯变换。针对适用于生物应用的简单检测器，分析了其误码概率，并给出了近似结果与上界结果。此外，还研究了不同参数对性能的影响。