Diffusion based molecular communication: principle, key technologies, and challenges

Wang, Jiaxing; Yin, Bonan; Peng, Mugen

doi:10.1109/cc.2017.7868158

Cited by 33 publications

(17 citation statements)

References 3 publications

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“…For molecule-scarce nano-systems, the nano-devices are far more likely to transmit a small burst of a molecule cluster (due to finite reservoir) and the information is likely to be encoded in a simple (existence or no existence) 1-bit format, or in the composition of the chemical compound (such as DNA [12]) for higher data rate [13]. In either case, the erasure channel applies, where the molecule cluster is either received or not.…”

Section: Molecule-scarce Nano-systemmentioning

confidence: 99%

Hamming–Luby rateless codes for molecular erasure channels

Wei

et al. 2020

Nano Communication Networks

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Nano-scale molecular communications encode digital information into discrete macro-molecules. In many nano-scale systems, due to limited molecular energy, each information symbol is encoded into a small number of molecules. As such, information maybe lost in the process of diffusion-advection propagation through complex topologies and membranes. Existing work has considered Hamming-distance codes for additive counting noise and Hamming-weight codes for transposition errors. Current linear-group codes are not well suited to combating the aforementioned erasure errors. In this paper, we design a novel lowcomplexity erasure combating encoding scheme: the rateless Hamming-Luby Transform code. The proposed rateless code combines the superior efficiency of Hamming codes with the performance guarantee advantage of Luby Transform (LT) codes. We design an iterative soft decoding scheme via successive cancelation to further improve the performance. Numerical simulations show this new rateless code can outperform both standard LT and cascaded Hamming-LT (Raptor) codes, while incurring a lower decoder computational complexity, which is useful for the envisaged resources constrained nano-machines.

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Section: Molecule-scarce Nano-systemmentioning

confidence: 99%

Hamming–Luby rateless codes for molecular erasure channels

Wei

et al. 2020

Nano Communication Networks

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“…Owing to the random movement of molecules in the communication fluid, the number of observed molecules within the receiver is a random variable (RV) following a Poisson distribution, which can be approximated via a Gaussian distribution [4]. It should be noted that information molecules corresponding to a previous transmission may reach and be observed by the receiver at a later time, thus causing intersymbol interference (ISI) to the current transmission [3, 22]. Moreover, we assume that there exist sources emitting molecules that are of the same type as the information carrying ones.…”

Section: System Modelmentioning

confidence: 99%

“…Introduction: Nanomachines are devices with functional components on the order of nanometres in size and are capable of performing basic sensing, computing and actuation tasks. Inspired by communication mechanisms naturally occurring in living organisms, molecular communication utilises molecules for the transfer of information among nanomachines [1][2][3]. The ability of such devices to communicate would enable applications such as cooperative diagnostics, tissue engineering and drug delivery in biomedicine [1,4].…”

mentioning

confidence: 99%

Optimal detector design for molecular communication systems using an improved swarm intelligence algorithm

Ntouni¹,

Paschos

Kapinas³

et al. 2018

Micro & Nano Letters

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The authors optimise the detection process of diffusion-based molecular communication systems utilising the weighted sum detector with appropriate weight values. Interestingly, no optimisation technique has ever been proposed for the calculation of the weights. To this end, they build on the standard particle swarm optimisation (PSO) technique and propose a robust iterative optimisation algorithm, called acceleration-aided PSO (A-APSO). While modified swarm-based optimisation algorithms focus on slight variations of the standard mathematical formulas, in A-APSO, the acceleration variable of the particles in the swarm is also involved in the search space of the optimisation problem. Particularly, they implement the A-APSO algorithm to evaluate the detector's weights that minimise the closedform expression of the error probability. Their findings reveal that, when employing the A-APSO weights, the error performance is superior to that achieved by utilising the weight values already existing in the literature or those evaluated with the standard PSO algorithm.

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“…A nano‐scale, molecular communication system consists of a nano‐transmitter (emitter) and a nano‐receiver (passive, absorbing, ligand‐binding) in a fluid medium which are apart by a few micro‐meters; information transfer between them is realized via exchange of molecules . In a diffusion based molecular communication (DbMC) system, molecules undertake a brownian motion governed by the diffusion process.…”

Section: Introductionmentioning

confidence: 99%

“…The very slow diffusion of molecules through the fluid medium implies that the DbMC channel is a low‐rate, broadcast channel . DbMC has recently attracted a lot of attention as it helps realize a body‐centric network consisting of several (on‐body, inside‐body) autonomous bionano‐machines . Therefore, DbMC has the potential to revolutionize the healthcare system.…”

Section: Introductionmentioning

confidence: 99%

Channel impulse response‐based source localization in a diffusion‐based molecular communication system

Baidoo-Williams

Rahman

Abbasi

2020

Internet Technology Letters

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Molecular source localization finds its applications in future healthcare systems, including proactive diagnostics. This work localizes a molecular source in a diffusion based molecular communication (DbMC) system via a minimal set of passive anchor nodes and a fusion center. Two methods are presented which both utilize (the peak of) the channel impulse response measurements to uniquely localize the source, under the assumption that the molecular source of interest lies within the open convex‐hull of the sensor/anchor nodes. The first method is a one‐shot, triangulation‐based approach which estimates the unknown location of the molecular source using least‐squares method. The second method is an iterative approach, which utilizes the gradient‐descent control law to minimize a non‐convex cost function. The corresponding Cramer‐Rao bound (CRB) is also derived. Simulation results reveal that: (a) the gradient‐descent method outperforms the triangulation method (in terms of mean squared error performance) for a wide range of values of signal‐to‐noise ratio; (b) the gradient‐descent method converges to the true source location uniformly (in less than 100 iterations).

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Diffusion based molecular communication: principle, key technologies, and challenges

Abstract: 2 nd Given Name Surname dept. name of organization (of Aff.) name of organization (of Aff.) City, Country email address 3 rd Given Name Surname dept. name of organization (of Aff.) name of organization (of Aff.

Cited by 33 publications

References 3 publications

Hamming–Luby rateless codes for molecular erasure channels

Hamming–Luby rateless codes for molecular erasure channels

Optimal detector design for molecular communication systems using an improved swarm intelligence algorithm

Channel impulse response‐based source localization in a diffusion‐based molecular communication system

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