A Learning Automaton-Based Algorithm for Maximizing the Transfer Data Rate in a Biological Nanonetwork

Abstract

Biological nanonetworks have been envisaged to be the most appropriate alternatives to classical electromagnetic nanonetworks for applications in biological environments. Due to the diffusional method of the message exchange process, transfer data rates are not proportional to their electromagnetic counterparts. In addition, the molecular channel has memory affecting the reception of a message, as the molecules from previously transmitted messages remain in the channel, affecting the number of information molecules that are required from a node to perceive a transmitted message. As a result, the ability of a node to receive a message is directly connected to the transmission rate from the transmitter. In this work, a learning automaton approach has been followed as a way to provide the receiver nodes with an algorithm that could firstly enhance their reception capability and secondly boost the performance of the transfer data rate between the biological communication parties. To this end, a complete set of simulation scenarios has been devised, simulating different distances between nodes and various input signal distributions. Most of the operational parameters, such as the speed of convergence for different numbers of ascension and descension steps and the number of information molecules per message, have been tested pertaining to the performance characteristics of the biological nanonetwork. The applied analysis revealed that the proposed protocol manages to adapt to the communication channel changes, such as the number of remaining information molecules, and can be successfully employed at nanoscale dimensions as a tool for pursuing an increased transfer data rate, even with time-variant channel characteristics.