Symbol Synchronization for Diffusion-Based Molecular Communications

Jamali, Vahid; Ahmadzadeh, Arman; Schober, Robert

doi:10.1109/tnb.2017.2782761

Cited by 57 publications

(42 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We focus on concentration shift keying (CSK) modulation where the transmitter releases s[k]N tx molecules at the beginning of the k-th symbol interval to convey information symbol s[k] ∈ [0, 1] [3]. We assume synchronous transmission and that the receiver counts the number of observed molecules multiple times in each symbol interval with sampling interval ∆t [101]. Because of the memory of the MC channel, ISI occurs.…”

Section: E Continuous Transmissionmentioning

confidence: 99%

See 1 more Smart Citation

Channel Modeling for Diffusive Molecular Communication—A Tutorial Review

et al. 2019

Self Cite

View full text Add to dashboard Cite

Molecular communication (MC) is a new communication engineering paradigm where molecules are employed as information carriers. MC systems are expected to enable new revolutionary applications such as sensing of target substances in biotechnology, smart drug delivery in medicine, and monitoring of oil pipelines or chemical reactors in industrial settings. As for any other kind of communication, simple yet sufficiently accurate channel models are needed for the design, analysis, and efficient operation of MC systems. In this paper, we provide a tutorial review on mathematical channel modeling for diffusive MC systems. The considered end-to-end MC channel models incorporate the effects of the release mechanism, the MC environment, and the reception mechanism on the observed information molecules. Thereby, the various existing models for the different components of an MC system are presented under a common framework and the underlying biological, chemical, and physical phenomena are discussed. Deterministic models characterizing the expected number of molecules observed at the receiver and statistical models characterizing the actual number of observed molecules are developed. In addition, we provide channel models for timevarying MC systems with moving transmitters and receivers, which are relevant for advanced applications such as smart drug delivery with mobile nanomachines. For complex scenarios, where simple MC channel models cannot be obtained from first principles, we investigate simulation-driven and experimentallydriven channel models. Finally, we provide a detailed discussion of potential challenges, open research problems, and future directions in channel modeling for diffusive MC systems.

show abstract

Section: E Continuous Transmissionmentioning

confidence: 99%

“…Sample Correlation: Multiple-sample detectors are used in the MC literature to improve the detection performance [35], [32], [31], [58], [37], [46], [90], [101], [83]. It is typically assumed that different samples are statistically independent from each other.…”

Section: Challenges and Directions For Future Workmentioning

confidence: 99%

Channel Modeling for Diffusive Molecular Communication—A Tutorial Review

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…by a chemical gradient, is an interesting topic for future research. 3 In [20], symbol synchronization in MC systems is studied. Optimal maximum likelihood synchronization and several practical suboptimal lowcomplexity synchronization schemes are proposed, see [20] for details.…”

Section: System Modelmentioning

confidence: 99%

Stochastic Channel Modeling for Diffusive Mobile Molecular Communication Systems

Ahmadzadeh

Jamali

Schober

2018

IEEE Trans. Commun.

Self Cite

View full text Add to dashboard Cite

In this paper, we consider mobile molecular communication (MC) systems which are expected to find application in several fields including targeted drug delivery and health monitoring. We develop a mathematical framework for modeling the time-variant stochastic channels of diffusive mobile MC systems. In particular, we consider a diffusive mobile MC system consisting of a pair of transmitter and receiver nano-machines suspended in a fluid medium with a uniform bulk flow, where we assume that either the transmitter, or the receiver, or both are mobile and we model the mobility by Brownian motion. The transmitter and receiver nano-machines exchange information via diffusive signaling molecules. Due to the random movements of the transmitter and receiver nano-machines, the statistics of the channel impulse response (CIR) change over time. We derive closed-form expressions for the mean, the autocorrelation function (ACF), the cumulative distribution function (CDF), and the probability density function (PDF) of the time-variant CIR. Exploiting the ACF, we define the coherence time of the time-variant MC channel as a metric for characterization of the variations of the CIR. The derived CDF is employed for calculation of the outage probability of the system. We also show that under certain conditions, the PDF of the CIR can be accurately approximated by a Log-normal distribution. Based on this approximation, we derive a simple model for outdated channel state information (CSI). Moreover, we derive an analytical expression for evaluation of the expected error probability of a simple detector for the considered MC system. In order to investigate the impact of CIR decorrelation over time, we compare the performances of a detector with perfect CSI knowledge and a detector with outdated CSI knowledge. The accuracy of the proposed analytical expressions is verified via particle-based simulation of the Brownian motion.

show abstract

“…Unfortunately, this becomes one of the disadvantages of blind synchronization, as the implementation of such special estimator is very complex. The authors of [18] highlight another drawback of this method, which is the assumption that the clocks of both transmitter and receiver are perfect, have identical frequencies and only a constant clock offset may exist between them. Hence, they propose a blind synchronization mechanism that uses two symbols and suboptimal estimators to determine both clock offset and clock skew from the received sequence, which does not need to be regularly timed (in fact, no clock is assumed at the transmitter side).…”

Section: Introductionmentioning

confidence: 99%

Effects of Framing Errors on the Performance of Molecular Communications With Memory

Atakan

Galmés

2020

IEEE Access

View full text Add to dashboard Cite

In conventional digital communication systems, synchronous transmission is achieved by embedding the clocking information into the data signal. However, the implementation of this technique in molecular communication systems, which rely on the diffusion of molecules as information carriers, becomes very complex due to the randomness of the diffusion process. Hence, in this paper we consider the molecular communication between two nanoscale devices with no exchange of any clock signal. To initiate the communication, the transmitter sends a special molecular symbol called beacon in order to trigger the detection process in the receiver. Therefore, this beacon symbol is equivalent to the start bit used for framing in asynchronous serial communication systems. We assume that both transmitter and receiver clocks are perfect, but not synchronized. Accordingly, the analysis focuses on the effects of framing errors on the performance of the molecular channel, measured via the symbol error probability. These errors are inherent to the random nature of the beacon arrival instant, which tends to degrade the alignment between the transmitter and receiver frames. We also assume a molecular channel with any level of inter-symbol interference and the use of different types of molecules to encode information symbols. We validate the derived SEP expression by means of extensive simulation experiments, and finally we develop a design scheme for the beacon symbol that satisfactorily mitigates the effects of framing errors. INDEX TERMS Molecular communication, symbol framing error, channel memory, beacon message, performance evaluations.

show abstract

Symbol Synchronization for Diffusion-Based Molecular Communications

Cited by 57 publications

References 26 publications

Channel Modeling for Diffusive Molecular Communication—A Tutorial Review

Channel Modeling for Diffusive Molecular Communication—A Tutorial Review

Stochastic Channel Modeling for Diffusive Mobile Molecular Communication Systems

Effects of Framing Errors on the Performance of Molecular Communications With Memory

Contact Info

Product

Resources

About