TY - GEN
T1 - The Gaussian approximation in soft detection for molecular communication via biological circuits
AU - Marcone, Alessio
AU - Pierobon, Massimiliano
AU - Magarini, Maurizio
N1 - Funding Information:
In this paper, we focused on the impact of approximated noise models on the bit error rate performance of analog soft detection for MC based on biological circuits. Soft detection in the context of MC has been recently proposed by the same authors in previous work using a Gaussian model for the noise sources, while in reality diffusion processes and chemical reactions tend to deviate from this assumption. Starting from the analysis of diffusion processes and biochemical reactions, we discussed Poisson models and Gaussian-approximated models for noise distributions. We then derived corresponding expressions to realize soft detection, and evaluate the resulting performance using computer simulations. Numerical results show that the soft detection computations based on Poisson and Gaussian models provide similar BER performance, thus validating the Gaussian approximation in the design of MC systems based on biological circuits. ACKNOWLEDGMENT This work was supported by the US National Science Foundation (NSF) through grant MCB-1449014, and the NSF EPSCoR First Award EPS-1004094. REFERENCES
Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/19
Y1 - 2017/12/19
N2 - The programming of biological cells by genetic circuit engineering is enabling the development of man-made devices and systems in the biochemical environment, with applications in the areas of biomedicine, security, and environmental sensing and control, amongst others. The exchange of information through biochemical reactions and molecule transport, i.e., Molecular Communication (MC), stands as one of the foundational paradigms for the design and characterization of these systems. In a previous work, the same authors developed an analog soft decoder design for MC based on biological circuits inspired by the analog information processing in biochemical reactions. While such a design was optimized for an MC channel affected by Gaussian noise, realistic noise models in molecule transport processes and biochemical reactions tend to deviate from this assumption. In this paper, these models are discussed, together with the validity of their Gaussian approximations, in light of the performance of the log-likelihood ratio calculation of the aforementioned design, numerically evaluated through biochemical simulation. These models, which are directly derived from the theory of molecular diffusion and stochastic chemical reaction analysis, are formulated with a general validity with respect to any future MC system design based on biological circuits.
AB - The programming of biological cells by genetic circuit engineering is enabling the development of man-made devices and systems in the biochemical environment, with applications in the areas of biomedicine, security, and environmental sensing and control, amongst others. The exchange of information through biochemical reactions and molecule transport, i.e., Molecular Communication (MC), stands as one of the foundational paradigms for the design and characterization of these systems. In a previous work, the same authors developed an analog soft decoder design for MC based on biological circuits inspired by the analog information processing in biochemical reactions. While such a design was optimized for an MC channel affected by Gaussian noise, realistic noise models in molecule transport processes and biochemical reactions tend to deviate from this assumption. In this paper, these models are discussed, together with the validity of their Gaussian approximations, in light of the performance of the log-likelihood ratio calculation of the aforementioned design, numerically evaluated through biochemical simulation. These models, which are directly derived from the theory of molecular diffusion and stochastic chemical reaction analysis, are formulated with a general validity with respect to any future MC system design based on biological circuits.
KW - Biochemical simulation
KW - Biological circuit
KW - Diffusion channel
KW - Langevin equation
KW - Molecular communication
KW - Poisson noise
KW - Soft detection
KW - Synthetic biology
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U2 - 10.1109/SPAWC.2017.8227764
DO - 10.1109/SPAWC.2017.8227764
M3 - Conference contribution
AN - SCOPUS:85044189278
T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
SP - 1
EP - 6
BT - 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017
Y2 - 3 July 2017 through 6 July 2017
ER -