###### 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. biological nanonetworks; diffusion; learning automaton; transfer data rate; ligand-receptor Angelidos 202220220220220220220220220222022022022202220  
on the deployment of engineered biota, or other bio-inspired devices, or even artificially fabricated structures as part of a biocompatible nanomachine. According to the established field work, there are three different types of molecular communications (MCs) based on the way the information disseminates through the medium [1; 2]: walkaway-based, advection-based and diffusion-based. In this work, a diffusion-based communication system is under consideration. In this type of communication system, information propagation is realized mostly through spontaneous diffusion, inside a common Newtonian medium. A well-known type of this communication is the calcium signaling among cells, through the ligand-receptor binding mechanism [3; 4; 5]. While the characteristics of the aforementioned models are well studied, their application in a communication system entails the problem of signal detection due to the stochastic nature of the movement of the signaling molecules. Dictated by the aforementioned need, various signal detection schemes have been proposed [6; 7; 8]. The noise, acting as an unwanted signal, severely limits the ability of the receiver to detect the transmitted signal. The dominant noise considered in this type of nanonetwork is counting noise, which is considered as an additive signal-dependent noise with a zero mean [9]. The authors of [10] provided all the latest contributions related to modulation, coding and detection techniques for a typical transmitter-receiver architecture. [PERSON] et al. [11] provided a comprehensive review on mobile MCs. In [12], the authors presented the key research directions towards high-data-rate molecular communications. The authors of [13] studied a system with modified concentration-shift-keying-based modulation, to release a larger amount of signaling molecules for bit 1 than bit 0. The received signal is reconstructed using average distance variation, and it is subtracted from the total received signal in subsequent bit duration, by applying a maximum a posteriori rule. The results showed a lower bit error rate from solutions like the one by [PERSON] et al.