Cooperative molecular communication (MC) is a key enabler for communication between nanomachines in the Internet of Bio-Nano Things (IoBNT). However, its performance is significantly constrained by the limited availability of free energy, which is essential for molecular transport. This paper introduces a novel transmitter model that encodes information by utilizing free energy to transport molecules from a reservoir to the external environment, creating specific concentration ratios in the reservoir for reliable information transmission. The transmitter’s performance is primarily influenced by energy consumption, which directly impacts the system’s bit error rate (BER) in IoBNT. To address these challenges, this study focuses on optimizing energy allocation among multiple transmitters in cooperative MC systems to enhance BER performance. For scenarios with two transmitters, a theoretical analysis of optimal energy allocation is performed, while reinforcement learning (RL) is utilized to determine optimal energy allocation strategies for systems with more than two transmitters. Numerical results demonstrate the effectiveness of the proposed strategies in minimizing BER and improving the overall performance of cooperative MC systems under energy constraints.

Molecular communication (MC) leverages molecules as information carriers, offering advantages such as biocompatibility and low energy consumption. Currently, MC’s research focuses on signal detection using chemical sensors, nanoparticles or biomolecules. However, challenges remain in accurately demodulating sequences of bits, particularly due to the influence of system parameters such as channel length, background flow rate, and transmitter-side actuation settings, including injection volume and valve control timing. To address these challenges, this paper introduces a MC testbed based on particle speed detection, which transforms molecular signals into particle speed signals for communication. Using hydrogen peroxide (H2O2) as the information carrier, the signal is demodulated by mixing the solution at the receiving end with specially prepared active particles and detecting the particle movement speed. Sequential transmission experiments were conducted to analyze the effects of various parameters on system performance. Experimental results demonstrate that the system accurately transmits information within a tested range, validating the theoretical model and highlighting its potential for microscopic communication applications.

Cooperative molecular communication (MC) is a promising technology for facilitating communication between nanomachines in the Internet of Bio-Nano Things (IoBNT) field. However, the performance of IoBNT is limited by the availability of energy for cooperative MC. This paper presents a novel transmitter design scheme that utilizes molecule movement between reservoirs, creating concentration differences through the consumption of free energy, and encoding information on molecule types. The performance of the transmitter is primarily influenced by energy costs, which directly impact the overall IoBNT system performance. To address this, the paper focuses on optimizing energy allocation in cooperative MC for enhanced transmitter performance. Theoretical analysis is conducted for two transmitters. For scenarios with more than two users, a genetic algorithm is employed in the energy allocation to minimize the total bit error rate (BER). Finally, numerical results show the effectiveness of the proposed energy allocation strategies in the considered cooperative MC system.

In some molecular communication (MC) designs, signaling molecules are held in reservoirs of different concentrations. This paper explores two thermodynamic implications of creating such a transmitter. First, it requires energy to generate reservoirs at different concentrations, since their chemical potential is different from the environment. Second, it requires an enormous energy cost to create a pure (or nearly pure) reservoir, so the transmitter is necessarily imperfect. Drawing from the Maxwell's Demon thought experiment, we consider the separation of an environmental mixture into reservoirs of differing concentrations, which requires free energy, but which allows information to be encoded in the difference of concentration. Both theoretical and simulation results indicate that the performance of the transmitter is positively correlated with the amount of consumed free energy. Furthermore, our simulation results indicate that there may be a fundamental thermodynamic tradeoff between energy per bit and bit error rate in MC.

In this paper, we consider a mobile molecular communication (MC) system consisting of two mobile nanomachines, a transmitter and a receiver, propelled by a positive drift velocity and Brownian motion in a realistic blood-vessel-type flow regime. Considering the nonlinear movement of the nanomachines, an extended Kalman filter is employed to estimate the distance from the transmitter. Furthermore, based on the predicted distance, to keep the number of received molecules for bit 1 at a stable level, we employ power control on the number of transmitted molecules based on the distance between the transmitter and the receiver and the residual molecules in the channel from the previous transmission. Finally, the optimal detection threshold is obtained by minimizing the error probability. It is verified that a fixed optimal detection threshold can be effective for the power control scheme in the mobile MC. The bit error rate (BER) performance of our scheme is verified via simulation results.

Distance and velocity estimation is challenging in mobile molecular communication, since estimates may be stale by the time the terminals gather the required information. To address both the difficulty and the delay of estimation, we propose an extended Kalman filter to predict both the distance between transmitter and receiver, and the drift velocity of the receiver, in a realistic blood-vessel-type flow regime. The extended Kalman filter is appropriate for the problem, as it can be used to predict quadratic quantities such as distance, and has manageable computational complexity. We derive the extended Kalman filter and show that it delivers excellent performance in predicting both distance and velocity in the presence of nonuniform flow.