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Transmitter and Receiver Architectures for Molecular Communications: A Survey on Physical Design with Modulation, Coding and Detection Techniques

Murat Kuscu, Ergin Dinc, Bilgesu A. Bilgin, Hamideh Ramezani, Ozgur B. Akan

arXiv:1901.05546v1cs.ET

TL;DR

Nanoscale molecular communication has extensive theory but no implemented nanoscale networks, motivating architectures and communication methods compatible with physical and biochemical constraints. This survey synthesizes transmitter and receiver designs with modulation, coding, and detection techniques, and identifies challenges and potential solutions. Its practical outcome is a set of physical-design guidelines intended to support experimental MC setups and realistic channel models.

  • Problem

    Nanoscale MC lacks implemented networks because nanoscale physics, fabrication challenges, and biochemical stochasticity complicate translating theory into physical systems.

  • Method

    The paper surveys biological and nanomaterial-based MC transmitter and receiver architectures together with modulation, coding, detection, design requirements, and implementation challenges.

  • Results

    The survey provides physical-design guidelines and a state-of-the-art account of MC transceiving methods, challenges, and future research directions.

  • Takeaways & Limitations

    The guidelines are intended to help researchers design experimental MC setups and develop realistic channel models that include transceiving processes.

Abstract

from arXiv · show

Inspired by Nature, molecular communications (MC), i.e., use of molecules to encode, transmit and receive information, stands as the most promising communication paradigm to realize nanonetworks. Even though there has been extensive theoretical research towards nanoscale MC, there are no examples of implemented nanoscale MC networks. The main reason for this lies in the peculiarities of nanoscale physics, challenges in nanoscale fabrication and highly stochastic nature of biochemical domain of envisioned nanonetwork applications. This mandates developing novel device architectures and communication methods compatible with MC constraints. To that end, various transmitter and receiver designs for MC have been proposed in literature together with numerable modulation, coding and detection techniques. However, these works fall into domains of a very wide spectrum of disciplines, including but not limited to information and communication theory, quantum physics, materials science, nanofabrication, physiology and synthetic biology. Therefore, we believe it is imperative for the progress of the field that, an organized exposition of cumulative knowledge on subject matter be compiled. Thus, to fill this gap, in this comprehensive survey we review the existing literature on transmitter and receiver architectures towards realizing MC amongst nanomaterial-based nanomachines and/or biological entities, and provide a complete overview of modulation, coding and detection techniques employed for MC. Moreover, we identify the most significant shortcomings and challenges in all these research areas, and propose potential solutions to overcome some of them.

I. INTRODUCTION

Molecular communications use molecules to encode, transmit, and receive information for nanoscale networks, but physical implementation remains limited by nanoscale, biochemical, and fabrication constraints. This survey organizes transmitter and receiver architectures alongside modulation, coding, detection methods, and their implementation challenges.

  • Molecular communications use molecules to encode, transmit, and receive information and are inherently bio-compatible, energy-efficient, and robust in physiological conditions.
  • MC physical designs follow biological architectures based on engineered bacteria or nanomaterial-based architectures, including approaches using graphene and biosensors.
  • The survey reviews MC transmitter and receiver designs, fundamental device requirements, and implementation opportunities across biological and artificial architectures.
  • MC modulation encodes information through molecule concentration, type, or release time, but high symbol counts are limited by intersymbol interference and detection sensitivity and selectivity.
  • Channel coding must address severe intersymbol interference and scarce nanomachine energy, making low-complexity codes important while conventional Turbo codes may be infeasible.
  • Detection methods remain difficult to validate because no implemented MC receiver provides realistic physical models for sampling, geometry, channel behavior, and reception noise.

II. MOLECULAR COMMUNICATION TRANSMITTER

MC transmitter design must satisfy micro/nanoscale constraints while controlling molecule supply and release. The section therefore considers miniaturization, molecule reservoirs, biocompatibility, and transmission performance.

  • MC transmitters encode information in molecular concentration, type, ratio, order, or release time and require coding, modulation, power, and molecule-supply components.
  • Design Requirements for MC-Tx: Miniaturization is required by many MC applications, but fully functional networked nanomachines remain difficult to fabricate.
  • Design Requirements for MC-Tx: Limited molecule reservoirs can deplete without replenishment, while replenishment rates directly affect achievable communication rates.
  • Design Requirements for MC-Tx: Biocompatibility is challenging for nanomaterial-based devices because nanoscale toxicity, corrosion, and mechanical mismatch can restrict implanted-device durability and function.
  • Design Requirements for MC-Tx: Transmitter performance depends on off-state leakage, transmission-rate precision, resolution, range, and transmission delay because leakage contributes to background channel noise.

B. Physical Design of MC-Tx

MC transmitters convert coded information into molecular signals through processing, modulation, molecule generation, and controlled release, but their physical realization remains challenging. Proposed architectures span biological and nanomaterial-based designs, with DNA-based systems offering high capacity while biological systems can provide energy efficiency or high data rates at substantial complexity.

  • MC-Tx components: An MC transmitter maps information to molecular properties such as concentration, type, ratio, order, or release time, then releases the resulting information molecules.Its processing path includes coding, modulation, and control of molecular release.
  • Propagation dependence: MC-Tx designs must account for propagation through diffusion, flow-assisted transport, or motor-powered mechanisms, each imposing different energy and range characteristics.Diffusion uses thermal energy, flow-assisted transport combines diffusion with flow, and molecular motors consume external energy.
  • DNA-based architectures: DNA/RNA-based systems encode symbols through strand properties and can support high-capacity links by selectively detecting more molecular symbols.Nanopore translocation takes from a few milliseconds to hundreds of milliseconds, which the survey reports does not bottleneck slow diffusion-based MC.
  • Implementation gap: Physical MC-Tx implementation remains largely open because idealized models often neglect stochastic molecule generation, transmitter geometry, and channel feedback.The literature on artificial micro/nanoscale MC transmitters is described as almost nonexistent apart from a few related systems.
  • Nanomaterial-based architectures: Microfluidic, nanopore, graphene-membrane, hydrogel, and wax-based designs offer routes toward miniaturized transmitters, while molecule leakage remains a significant microfluidic challenge.Active transport in microfluidic environments can improve achievable data rates by moving information molecules faster than passive transport.
  • Biological architectures: Biological architectures can achieve unprecedented data rates through DNA information density, but bacterial conjugation requires several coordinated modules and therefore has high complexity.For conjugation-based networks, cyclic shift coding provided higher link probability than forward-reverse coding, which outperformed straight encoding.

C. Modulation Techniques for MC

MC modulation encodes information in molecular concentration, type, combination, or release timing rather than conventional electromagnetic waveforms. The survey covers basic and multi-molecule schemes, timing-based methods, ISI-robust adaptation, and DNA sequence encoding to address limited symbols and slow diffusion.

  • Overview: MC modulation encodes information through molecular concentration, molecule type, or release time.These dimensions define the principal modulation families surveyed for molecular channels.
  • Concentration-based modulation: On-off keying releases molecules for high logic and none for low logic, while concentration shift keying uses concentration levels to increase the symbol count.Concentration shift keying is described as analogous to amplitude shift keying in traditional wireless channels.
  • Molecule-type modulation: Molecule shift keying assigns symbols to different molecule types, whereas depleted molecule shift keying uses simultaneous combinations and no release to represent 2^k symbols with k molecules.For 2-bit depleted molecule shift keying, two molecules represent four symbols: no release, molecule A, molecule B, and A+B.
  • Timing-based modulation: Pulse-position modulation and release-time shift keying encode information in when molecules are released, with channel noise distributions depending on flow conditions.The survey associates inverse Gaussian noise with flow and Levy noise without flow for release-time shift keying.
  • ISI mitigation: Adaptive modulation can exploit channel memory by controlling emission rates, making transmission more robust against intersymbol interference.Intersymbol interference is identified as an important degradation factor in diffusive channels.
  • DNA-based modulation: Nucleotide shift-keying addresses limited symbols and slow diffusion by encoding large amounts of information directly into DNA base sequences.The approach can use error-coding algorithms such as Reed–Solomon coding, although practical DNA reading and writing remain constrained by speed and cost.

D. Coding Techniques for MC

MC channel coding addresses severe ISI, noise, and nanoscale resource constraints through codes tailored to molecular channels rather than directly importing conventional schemes. Reviewed approaches include ISI-free, MoCo-based, Hamming, convolutional, and device-oriented codes, with performance depending on range, modulation, BER, energy, and channel assumptions.

  • Motivation: MC channel coding must address severe ISI and energy or computational constraints that make conventional high-complexity codes ill-suited.The review highlights lower-complexity codes and MC-specific designs as alternatives to conventional schemes.
  • ISI-free codes: The first MC-specific ISI-free code targets ISI under MoSK using two distinguishable molecules and an absorbing receiver.The (4,2,1) code maps 2-bit information into 4-bit codewords and tolerates specified level-1 crossovers.
  • ISI-free codes: ISI-free (n,k,l,s) codes significantly outperform ISI-free (n,k,l) codes under similar computational burdens and can outperform convolutional codes with fewer computational resources.The asymmetric probabilities of forward and backward crossover motivate selecting s lower than l.
  • MC-adapted codes: MoCo-based codes outperform Hamming codes in error-rate analysis but require channel-dependent detection probabilities and substantial computation for codebook and decoding updates.These requirements can also introduce overhead for synchronized code updating.
  • Hamming codes: Up to ≈1.7dB coding gain is reported for Hamming codes at 1µm and low BERs, while extra ISI can make uncoded transmission better at high BERs.Parity-bit energy makes coding inefficient at small distances, whereas coding becomes more efficient for larger distances.
  • Convolutional codes: Convolutional coding with high transmission rate and M = 1 outperforms uncoded transmission at short and medium ranges, whereas no coding performs better at long range MC.Increasing pulse-amplitude levels worsens BER because of increased ISI, favoring OOK over PAM in the reported setting.

III. MOLECULAR COMMUNICATION RECEIVER

An MC receiver recognizes target molecules and detects information encoded in molecular properties. Its molecular receiver antenna combines selective biorecognition with transduction, while physical implementations include biological and nanomaterial-based architectures.

  • Receiver architecture: An MC-Rx detects information encoded in molecular concentration, type, or release time after recognizing target molecules near the receiver.The receiver therefore interfaces directly with the molecular channel.
  • Receiver architecture: The molecular receiver antenna consists of a biorecognition unit followed by a transducer unit.The biorecognition unit selectively reacts with information-carrying molecules, and the transducer generates a processable signal.
  • Receiver architecture: MC-Rx physical designs are categorized into biological receivers based on synthetic gene circuits and nanomaterial-based artificial receiver structures.The review discusses these architectures alongside their communication-theoretical requirements and detection methods.

A. Design Requirements for MC-Rx

An MC-Rx must operate independently on a resource-limited nanomachine while remaining compatible with molecular environments and continuous sensing. The resulting requirements span processing, detection, energy, biological compatibility, and scale.

  • Functional requirements: MC-Rx processing must occur in situ because the receiver cannot rely on external macroscale devices or human controllers.This requirement supports autonomous operation within the MC setup.
  • Functional requirements: Label-free detection requires identifying information molecules from their intrinsic characteristics without additional molecular labeling or preparation.The requirement avoids an extra molecular preparation stage.
  • Functional requirements: Continuous operation requires uninterrupted channel observation and reusable receptors that return to their initial state after detection.This supports repeated channel use for signals encoded in concentration, type, ratio, order, or release time.
  • Functional requirements: Energy-efficient MC-Rx designs must optimize usage and may require energy-harvesting units because batteries may not support long-term nanomachine activity.The stated concern is especially relevant to implanted devices.
  • Functional requirements: In vivo MC-Rxs must be biocompatible, non-toxic, flexible, and biodurable to avoid harming living systems and cells.These requirements are motivated by life-science applications such as disease diagnosis and treatment.
  • Functional requirements: MC-Rx devices must use micro- or nanoscale components to integrate into a nanomachine.Miniaturization is the final listed physical-design requirement.

B. Communication Theoretical Performance Metrics

MC-Rx evaluation extends conventional communication metrics with biosensing measures that capture molecular detection, selectivity, operating range, concentration discrimination, and sampling speed. These metrics reflect receptor behavior and molecular transport limits.

  • Metric framework: MC-Rx evaluation uses conventional metrics such as SNR, BER, and mutual information alongside molecularly specific performance measures.The additional measures are needed because MC uses molecules as information carriers.
  • Molecular metrics: Limit of detection is the minimum nearby molecular concentration required to distinguish target-molecule presence from absence.For MC-Rxs, LoD corresponds to the sensitivity metric used for EM communication receivers.
  • Molecular metrics: Selectivity measures relative affinity for information versus interferer molecules, with high selectivity needed for unique target detection.Interferers may be non-information molecules or other information-molecule types in MoSK.
  • Molecular metrics: Operation range comprises a linear region before receptor saturation and a saturation region determined by finite receptor density.Concentration-encoded signals require linear operation, whereas MoSK receivers can operate in either region.
  • Molecular metrics: Molecular sensitivity measures the smallest detectable concentration difference and is especially important when information is encoded in concentration.It can be defined using changes in molecular-antenna output relative to concentration changes.
  • Molecular metrics: Temporal resolution measures molecular-concentration sampling speed, which is expected to be limited by diffusion and binding kinetics rather than electrical processes.A transport-limited biorecognition unit is needed to detect messages arriving near the receiver.

C. Physical Design of MC Receiver

MC receiver designs span biological circuits and nanomaterial-based bioFETs, but receiver performance depends on physical sensing and molecular-to-electrical transduction rather than idealized molecule counting alone. The survey reviews these architectures, their sensing mechanisms, and design requirements while identifying challenges in modeling and implementation.

  • Receiver design motivation: Idealized MC studies often assume perfect molecule counting, overlooking molecular-to-electrical transduction effects that influence receiver performance.The assumed receivers may have transparent, absorbing, or reactive boundaries.
  • Biological MC-Rx architectures: Biological MC receivers use engineered cellular networks to sense molecules and process signals through genetic circuits.Synthetic biology can implement sensing, transmission, reception, and processing functions within engineered cells.
  • Biological MC-Rx architectures: Genetic-circuit receivers face nonlinear, stochastic biochemical dynamics, and existing analyses report extremely limited information transfer under system noise.Their performance is also difficult to characterize analytically, motivating noise modeling and mitigation studies.
  • Nanomaterial-based MC-Rx architectures: Electrical biosensors are emphasized for MC reception because optical and mechanical alternatives require macroscale excitation or detection units incompatible with in-situ operation.Affinity-based and biocatalytic sensing provide different molecular-recognition mechanisms, with affinity-based sensing feasible for a wider range of targets.
  • Nanomaterial-based MC-Rx architectures: BioFETs detect target molecules through conductivity modulation and can use nanowires, nanotubes, graphene, MoS2, or conducting polymers as transducer channels.The channel material affects receiver geometry and electrical noise; SiNW bioFETs offer high sensitivity and low power consumption, but fabrication remains challenging.
  • Nanomaterial-based MC-Rx architectures: MC-Rx models must incorporate molecular transport, ligand-receptor binding kinetics, receiver geometry, operating voltage, and molecular-to-electrical transduction.These requirements extend beyond biosensor models designed for equilibrium measurements or macroscale readout.

D. Detection Methods for MC

MC detection methods are organized by receiver model, especially passive/absorbing versus reactive receivers, because physical sampling, receptor reactions, channel assumptions, and device constraints shape detector design. The surveyed methods include coherent and noncoherent approaches, instantaneous or historical receptor-state sampling, and arrival-time statistics.

  • Detection-model classification: MC detectors are classified into passive/absorbing and reactive-receiver methods according to their channel and received-signal models.These models reflect different device constraints and simplify the detection problem in different ways.
  • Passive and absorbing receivers: Passive receivers simplify detection by treating the receiver as a transparent spherical observer of molecules within its reception space.Absorbing receivers instead model a spherical entity that absorbs and degrades molecules hitting its surface.
  • Modeling assumptions: Many received-signal models use OOK, point-source transmitters, free diffusion or uniform flow, and simplified geometry or sampling assumptions.These assumptions make analysis tractable but can omit molecular propagation, sampling, receiver geometry, and receptor-reaction effects.
  • Noncoherent detection: Noncoherent detectors avoid instantaneous channel-state information, using comparisons with previous intervals, channel-response convexity, or statistical information instead.Constant-composition codes can also enable maximum-likelihood detection without instantaneous or statistical channel-state information when intersymbol interference is neglected.
  • Arrival-time detection: The first-arrival detector approaches optimal maximum-likelihood performance for small released-particle counts, whereas maximum-likelihood detection remains significantly better for high counts.In diffusion-based molecular channels without flow, linear filtering can increase dispersion, making multiple-particle release worse than single-particle release in the cited setting.
  • Reactive receivers: Reactive-receiver detection samples either instantaneous bound-receptor counts or the continuous history of receptor binding and unbinding events.History-based methods use unbound intervals for total ligand concentration and bound intervals for bound-molecule type.
  • Reactive-receiver detection: A reactive-receiver likelihood method based on bound-time intervals substantially outperforms one-shot maximum-likelihood schemes under high interference from similar ligands.The method estimates the ratio of messenger to interfering ligands from bound-time samples at each receptor.

IV. CHALLENGES AND FUTURE RESEARCH DIRECTIONS

The paper identifies physical architecture and unrealistic transmitter, receiver, and channel assumptions as major obstacles to realizing and evaluating micro/nanoscale MC. It calls for theoretical and experimental work using more realistic device, geometry, molecule-generation, and stochastic-noise models.

  • Physical realization: Physical MC-Tx/Rx architecture is understudied yet significantly affects the accuracy of theoretical studies and requires experimental validation.The survey discusses challenges toward realizing micro/nanoscale setups and evaluating their ICT-based performance.
  • Realistic modeling: Existing MC-Tx/Rx and channel models often use non-realistic assumptions that produce imprecise performance estimates not applicable to real scenarios.The paper highlights stochastic molecule generation, transmitter and receiver geometry, and stochastic noise dynamics as modeling requirements.
  • Research directions: Future research should combine theoretical studies with experimental investigations to validate models and address open problems in modulation, coding, and detection.The survey emphasizes factors that must be considered in developing MC communication techniques.

A. Challenges for Physical Design and Implementation of MC-Tx

MC transmitter design is constrained by limited empirical evidence, energy and data-rate requirements, molecule leakage, and the complexity of biological implementations. These constraints make realistic end-to-end modeling and practical implementation difficult.

  • Modeling constraints: Micro/nanoscale MC transmitter models often assume an ideal point source, neglecting stochastic molecule generation, transmitter geometry, and channel feedback.Empirical transmitter models are needed because communication parameters depend strongly on the channel model.
  • Energy: Standalone MC transmitters and receivers may require energy harvesting because battery-powered devices have limited lifetimes.Harvestable energy can be limited enough that complex algorithms are infeasible in realistic scenarios.
  • Data rate: MC is mainly suited to applications without high data-rate requirements because propagation is slow, although DNA/RNA strands can encode up to 100s of MB per information molecule.DNA/RNA-based storage can support data rates on the order of Mbps through large information payloads per molecule.
  • Molecule leakage: Molecule leakage during low or no-information transmissions increases intersymbol interference and reduces the transmitter’s molecule budget.Proposed leakage solutions have not been implemented, leaving their feasibility as an open research issue.
  • Biological complexity: Biological transmitter architectures are difficult to design because cellular mechanisms, synthetic-biology manipulation, and electronic interfacing must be coordinated.Genetically engineered cells may also die faster because engineered metabolisms complicate survival.

B. Challenges for Physical Design and Implementation of MC-Rx

MC receiver implementation remains limited by sparse physical-design research and by the stochastic, nonlinear behavior of molecular sensing and biological circuits. Practical receivers also require realistic transient models and interfaces to cyber-networks.

  • Implementation gap: The literature lacks comprehensive investigation of micro/nanoscale MC receiver structures despite theoretical studies and some macroscale experiments.This gap creates open challenges for realizing proposed MC applications.
  • Molecular sensing: Ligand-receptor selection must account for binding, dissociation, sensitivity, and interferer affinities to reduce background noise.Both genetic-circuit and artificial receivers use ligand-receptor reactions to sense target-molecule concentration.
  • Artificial receiver modeling: Artificial biosensor receivers need transient, stochastic models rather than equilibrium-only models because MC concentration signals vary over time.Models should also capture operating voltage, geometry, gate configuration, ionic concentration, and Debye screening, with wet-lab validation.
  • Biological circuits: Genetic-circuit receivers produce nonlinear input-output behavior with system-evolution-dependent stochastic effects, complicating analytical performance analysis.Existing circuit noise also requires comprehensive study and mitigation methods.
  • Bio-cyber interface: Bio-cyber interfaces must decode molecular messages, process them, and transmit the resulting information to macroscale network nodes.BioFET-based receivers can provide molecular-to-electrical transduction for wireless transmission to cyber-networks.

C. Challenges for Developing MC Modulation Techniques

MC modulation research has not yet established how proposed schemes perform under realistic transmitter and receiver conditions. Synchronization and energy constraints further limit practical deployment.

  • Realistic evaluation: Existing modulation schemes mostly rely on ideal point-source transmitters and receivers that perfectly detect multiple molecule types, leaving realistic performance unknown.Experimental implementation is needed to evaluate the schemes under practical conditions.
  • Practical constraints: Synchronization is difficult for some modulation schemes because diffusive channels are error-prone and micro/nanoscale devices have low complexity.Energy efficiency is also a major challenge when transmitters rely on limited harvested energy.

D. Challenges for Developing MC Channel Coding Techniques

MC channel coding remains an emerging area whose schemes must adapt to changing channel conditions and irregular transmissions. Existing evaluations also rely on simplified signaling and channel models.

  • Research maturity: MC channel coding is still a new research area with several pressing open directions.The field requires coding methods suited to the variable propagation and detection conditions of MC channels.
  • Adaptive coding: Adaptive codes should respond to temperature, diffusion speed, channel contents, reaction rates, and node distance, but existing schemes do not probe channel characteristics.These environmental parameters affect propagation time and receiver detection probabilities.
  • Irregular signaling: Coding schemes evaluated under regular time slots must address irregular transmission because inter-symbol-duration fluctuations affect intersymbol interference and BER.Interference from irregular signaling is especially important because ISI is the primary source of BER in MC.
  • Model simplicity: Existing MC coding studies use very simple models, including one-dimensional channels and a single released molecule per transmission period.More channel codes need to be developed beyond these restricted assumptions.

E. Challenges for Developing MC Detection Techniques

MC detection techniques remain difficult to realize because they rely on idealized synchronization, channel, receiver, and interference assumptions that do not match practical biochemical environments. Key unresolved issues include selective detection, reactive-receiver modeling, receptor saturation, and time-varying channels.

  • Many proposed detection techniques remain infeasible for envisioned MC devices because theoretical assumptions diverge from practical channels and receivers.Preliminary macroscale airborne MC studies also reveal this theory–practice discrepancy.
  • Synchronization: Perfect synchronization is commonly assumed, but existing synchronization methods may be too complex for nanomachines or depend on unstable channel state information.Their impact on detection performance under nonideal synchronization remains unrevealed; asynchronous detection is a potential alternative.
  • Physical Properties of the Channel: Most studies simplify channel propagation as free diffusion or uniform flow, whereas practical channels may be bounded, time-varying, obstructed, or affected by disruptive nonuniform flows.Recent channel-estimation and noncoherent-detection approaches still rely on simplifying assumptions about channel and receiver architecture.
  • Reactive Receivers: Reactive-receiver models remain incomplete because receptor interactions, cooperative clustering, and coupled diffusion–reaction dynamics are often neglected.These omissions hinder analysis of spatio-temporal receptor correlations and coupling between transport and reception.
  • Receiver Saturation: Strong intersymbol interference or external interference can saturate receptors, limiting receiver dynamic range and discrimination of different signal levels.Adaptive thresholds and transmission schemes are potential mitigation strategies, but saturation is often ignored through an infinite-receptor assumption.
  • Receiver Selectivity: Molecular interference and nonideal ligand–receptor coupling make selective detection necessary in environments containing similar molecules.Bound-time information and immune-system-inspired kinetic proofreading or adaptive sorting are proposed, but one reported estimator requires interferer knowledge and handles only one interferer type.

V. CONCLUSION

The paper concludes that MC theory has advanced while physical transmitter and receiver implementation remains insufficiently integrated with communication models. It surveys device architectures and ICT techniques, then provides design guidance intended to support realistic experiments and channel models that narrow this gap.

  • MC information-theoretic, modulation, and detection research is well studied, but often isolates communication channels from physical transmission and reception processes.Consequently, the feasibility and performance of proposed ICT techniques could not be validated.
  • The survey covers physical MC-Tx/Rx designs, modulation, coding, and detection techniques, including nanomaterial-enabled artificial devices and synthetic-biology-enabled biological devices.It also examines their implementation opportunities and challenges.
  • The paper’s physical-design guidelines are intended to help researchers design experimental MC setups and develop realistic channel models that include transceiving processes.This is presented as a route toward overcoming the long-standing discrepancy between MC theory and practice.
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