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
“…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].…”
“…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
Molecular communication (MC) is an emerging new communication paradigm where information is conveyed by chemical signals. It has been recognized as one of the most promising physical layer techniques for the future Internet of Bio-Nano Things (IoBNT), which enables revolutionary applications beyond our imagination. Compared with conventional communication systems, MC typically demands a higher security level as the IoBNT is deeply associated with the biochemical process. Against this background, this article first discusses the security and privacy issues of IoBNT with MC. Then, the physicallayer countermeasures against the threat are presented from an interdisciplinary perspective concerning data science, signal processing techniques, and the biochemical properties of MC. Correspondingly, both the keyless and key-based schemes are conceived and revisited. Finally, some open research issues and future research directions for secrecy enhancement in IoBNT with MC are put forward.
“…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].…”
“…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
Molecular communication (MC) is an emerging new communication paradigm where information is conveyed by chemical signals. It has been recognized as one of the most promising physical layer techniques for the future Internet of Bio-Nano Things (IoBNT), which enables revolutionary applications beyond our imagination. Compared with conventional communication systems, MC typically demands a higher security level as the IoBNT is deeply associated with the biochemical process. Against this background, this article first discusses the security and privacy issues of IoBNT with MC. Then, the physicallayer countermeasures against the threat are presented from an interdisciplinary perspective concerning data science, signal processing techniques, and the biochemical properties of MC. Correspondingly, both the keyless and key-based schemes are conceived and revisited. Finally, some open research issues and future research directions for secrecy enhancement in IoBNT with MC are put forward.
“…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].…”
Diffusion-based molecular nanonetworks exploit the diffusion of molecules, e.g., in free space or in blood vessels, for the purpose of communication. This article comprehensively surveys coding approaches for communication in diffusion-based molecular nanonetworks. In particular, all three main purposes of coding for communication, namely source coding, channel coding, and network coding, are covered. We organize the survey of the channel coding approaches according to the different channel codes, including linear block codes, convolutional codes, and inter-symbol interference (ISI) mitigation codes. The network coding studies are categorized into duplex network coding, physical-layer network coding, multi-hop nanonetwork coding, performance improvements of network-coded nanosystems, and network coding in mobile nanonetworks. We also present a comprehensive set of future research directions for the still nascent area of coding for diffusion-based molecular nanonetworks; specifically, we outline research imperatives for each of the three main coding purposes, i.e., for source, channel, and network coding, as well as for overarching research goals.
“…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].…”
Molecular communication (MC) aims to use signaling molecules as information carriers to achieve communication between biological entities. However, MC systems severely suffer from inter symbol interference (ISI) and external noise, making it virtually difficult t o obtain accurate mathematical models. Specifically, the mathematically intractable channel state information (CSI) of MC motivates the deep learning (DL) based signal detection methods. In this paper, a modified t emporal convolutional network (TCN) is proposed for signal detection for a special MC communication system which uses magnetotactic bacteria (MTB) as information carriers. Results show that the TCN-based detector demonstrates the best overall performance. In particular, it achieves better bit error rate (BER) performance than sub-optimal maximum a posteriori (MAP) and deep neural network (DNN) based detectors. However, it behaves similar with the bidirectional long short term memory (BiLSTM) based detector that have been previously proposed and worse than the optimal MAP detector. When both BER performance and computational complexity are taken into account, the proposed TCN-based detector outperforms BiLSTM-based detectors. Furthermore, in terms of robustness evaluation, the proposed TCN-based detector outperforms all other DL-based detectors.
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