A. O. Bicen, J. J. Lehtomäki and I. F. Akyildiz, "Statistical Modeling and Bit Error Rate Analysis for Bio-Sensor Receivers in Molecular Communication," in IEEE Sensors Journal, vol. 20, no. 1, pp. 261-268, 1 Jan.1, 2020. doi: 10.1109/JSEN.2019.2933222
Statistical modeling and bit error rate analysis for bio-sensor receivers in molecular communication
|Author:||Bicen, A. Ozan1; Lehtomäki, Janne J.2; Akyildiz, Ian F.3|
1Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
2Centre for Wireless Communications (CWC), University of Oulu, Oulu FI-90014, Finland
3School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
|Online Access:||PDF Full Text (PDF, 1.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019121146689
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-12-11
The behavior of bio-sensor receivers is studied for molecular communication (MC). Bacteria can be engineered as a bio-sensor receiver to produce an output signal, e.g., produce green fluorescent protein, with respect to an external concentration pulse (MC signal). The signal transduction of bacteria, i.e., bacteria response, can be used to detect the pulse-amplitude modulated MC signals. In this work, a statistical model for the bacteria-based bio-sensor receivers is developed. Statistical signal models are useful to evaluate the reliability of the communication systems. The bacteria response is modeled by approximating a first-order model of signal transduction in the linear ramp-up region. The bacteria response is found to be a function of the response rate (linear ramp-up slope) and the time. Bacterial signal transduction is inherently noisy due to the cascades of biochemical reactions to produce the output signal. Therefore, the first-order model is extended incorporating the noise in both the rate and the timing (random delay) of the bacteria response. The bit error rate performance is studied to reveal the impact of the timing noise against the response rate noise. The developed statistical signal model can aid performance evaluation of bacteria-based bio-sensor receivers in MC and biological sensing.
IEEE sensors journal
|Pages:||261 - 268|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
The work of I. F. Akyildiz was supported by the U.S. National Science Foundation (NSF) under the Grant CNS-1763969. The work of J. J. Lehtomäki was supported by the Academy of Finland 6Genesis Flagship program (grant no. 318927).
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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