Capacity analysis for signal propagation in nanomachine-to-neuron communications

Nanomachine-to-neuron communications are envisaged as a futuristic communication scenario with enormous impact especially on medical applications. One of the crucial aspects to be considered in the study of any communication environment is represented by the amount of information that can be reliably transmitted over the channel, that is, its capacity. In this paper we evaluate such capacity. To this purpose we consider that the communication channel between a nanomachine and a neuron has been proved to introduce a noise. This however cannot be modeled by means of a traditional additive white Gaussian noise as assumed in the traditional Shannon communication theory. Capacity and bit error rate are useful metrics to characterize the theoretical bounds for communications and the maximum achievable throughput.