A machine learning-based concentration-encoded molecular communication system

Kim, Sujin; Singh, Pankaj; Jung, Sung-Yoon

doi:10.1016/j.nancom.2022.100433

Cited by 10 publications

(1 citation statement)

References 29 publications

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“…This includes an extensive background on machine learning techniques and an examination of machine learning in IoT across different protocol layers, along with discussions on hardware implementation feasibility. The study proposes a concentration-encoded molecular communication system using a modulation scheme called concentration position-shift keying (CPSK) [15]. Moreover, the paper delves into an end-to-end learning method for signal recovery in a mathematical multiple-input--multipleoutput (MIMO) molecular communication (MC) system with drift, based on a Kullback-Leibler divergence (KLD) evolutionary generative adversarial network (EGAN) [16].…”

Section: Related Workmentioning

confidence: 99%

Classification of Diffusion Constants of Transmitter and Receiver and Distance Between Them Using Mobile Molecular Communication via Diffusion Model

Er,

Isik,

Kuran

et al. 2024

Arab J Sci Eng

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Molecular communication (MC) holds promise for enabling communication in scenarios where traditional wireless methods may be impractical or ineffective, offering unique capabilities for a range of applications in both natural and engineered systems. In this research, a novel approach to MC is explored, diverging from the standard use of stationary transmitter and receiver models typically found in the field. The study introduces a dynamic MC model, where both the transmitter and receiver are mobile within a diffusion environment. This model operates using a 5-bit system. The key finding is that the mobility of these nanodevices alters their distance, which in turn impacts the likelihood of molecule reception at the receiver. The study employs deep learning techniques, specifically a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to categorize the mobility patterns of the receiver (Rx) and transmitter (Tx). By analyzing various mobility rates (Drx and Dtx) and distances between the Tx and Rx, the research successfully identifies the most efficient mobile MC model in terms of molecule reception rates. The use of Linear Support Vector Machine alongside the CNN and LSTM hybrid feature vector resulted in an 87.68% accuracy in predicting diffusion coefficients. Moreover, using a Cubic Support Vector with the same hybrid feature vector, the study achieved an 88.09% accuracy in estimating the distance between the transmitter and receiver. The study concludes that an increase in the mobilities of Rx and Tx correlates with a higher rate of molecule reception.

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Section: Related Workmentioning

confidence: 99%