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2021
DOI: 10.1109/mcom.001.2000957
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Signal Detection for Molecular Communication: Model-Based vs. Data-Driven Methods

Abstract: Multi-scale molecular communication (MC) employs the characteristics of information molecules for information exchange. The received signal in MC inevitably encounters severe inter-symbol interference and signal-dependent noise due to the stochastic diffusion mechanism. Focusing on the critical signal detection in MC, first this article reviews the commonly used model-based detectors, and exposes their limitations in practical implementation. Then, the emerging data-driven detectors that can make up for some d… Show more

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Cited by 19 publications
(4 citation statements)
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References 16 publications
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“…It then employs the received signal vector, achieving error-free detection and a relatively high localization performance when the eavesdropper is in the vicinity of the legitimate transceiver pair. Inspired by data science, similar problems in other complicated channels can be solved as well provided that the training data is available and accurate [10].…”
Section: Isotropic Diffusion Anisotropic Diffusionmentioning
confidence: 99%
See 1 more Smart Citation
“…It then employs the received signal vector, achieving error-free detection and a relatively high localization performance when the eavesdropper is in the vicinity of the legitimate transceiver pair. Inspired by data science, similar problems in other complicated channels can be solved as well provided that the training data is available and accurate [10].…”
Section: Isotropic Diffusion Anisotropic Diffusionmentioning
confidence: 99%
“…In this case, conven-tional secure techniques, including artificial noise generation, beamforming, etc., cannot be implemented for MC networks. The signal processing techniques in optical communication systems are mature due to their long history compared to those in MC, and they can be inspiring since both photonic and molecular signals share similar properties with respect to signal constraint and noise characteristics [10]. As MC bears the biochemical properties, the underlying cell signaling may provide novel signal processing methods against attacks and wiretapping, where interdisciplinary efforts are required to reach this goal.…”
Section: Open Challenges and Future Directionsmentioning
confidence: 99%
“…We note for completeness that several other surveys have covered complementary aspects of DBMC nanonetworks, including synthetic biological building blocks [66], channel modeling [67], modulation techniques [51], detection techniques [68], estimation techniques [69], retroactivity aspects [70], [71], interfaces [72], [73], and security aspects [74].…”
Section: B Related Surveysmentioning
confidence: 99%
“…DL-based approaches use dataset containing samples of transmitted and received signal to train a detector and eventually achieve information recovery without analyzing the underlying channel model. Compared with other machine learning methods, DL has stronger robustness to noise, and has associative memory function, which can fully approximate complex nonlinear relationships [8].…”
Section: Introductionmentioning
confidence: 99%