Nano/Bio-Receiver Architectures and Detection Methods for Molecular Communications

Internet of Nano Things (IoNT) is an emerging technology, which aims at extending the connectivity into nanoscale and biological environments with collaborative networks of artificial nanomachines and biological entities integrated into the Internet. To enable the IoNT and its groundbreaking applications, such as real-time intrabody health monitoring, it is imperative to devise nanoscale communication techniques with low-complexity transceiver architectures. Bio-inspired molecular communications (MC), which uses molecules to transfer information, is the most promising technique to realise IoNT due to its inherent biocompatibility and reliability in physiologically-relevant environments. Despite the substantial body of work concerning MC, the implications of an interface between MC channel and practical MC transceiver architectures are largely neglected, leading to a major gap between theory and practice. As the first step to remove this discrepancy, in this thesis, I develop a realistic analytical ICT model for microfluidic MC with surface-based receivers as a convection-diffusion-reaction system. In the second part, I focus on biological MC receivers, which can be implemented in living cells using synthetic biology tools. In this direction, I theoretically develop low-complexity and reliable MC detection methods exploiting the various statistics of the stochastic ligand-receptor interactions at the membrane of biological MC receivers. The estimation and detection theoretical analysis of these detection methods demonstrate that even single type of receptors can provide sufficient statistics to overcome the receptor saturation problem, cope with the interference of non-cognate molecules, and simultaneously sense the concentration of multiple types of ligands. I also propose synthetic receptor designs for the transduction of decision statistics into a representation by concentration of intracellular molecules, and design chemical reaction networks performing decoding with intracellular reactions. Finally, I fabricate a micro/nanoscale MC receiver based on graphene field-effect transistor biosensors and perform its ICT characterisation in a custom-designed microfluidic MC system with the information encoded into the concentration of DNAs. This experimental platform is the first practical demonstration of micro/nanoscale MC, and can serve as a testbed for developing realistic MC methods

Frequency-Domain Model of Microfluidic Molecular Communication Channels with Graphene BioFET-based Receivers

Molecular Communication (MC) is a bio-inspired communication paradigm utilizing molecules for information transfer. Research on this unconventional communication technique has recently started to transition from theoretical investigations to practical testbed implementations, primarily harnessing microfluidics and sensor technologies. Developing accurate models for input-output relationships on these platforms, which mirror real-world scenarios, is crucial for assessing modulation and detection techniques, devising optimized MC methods, and understanding the impact of physical parameters on performance. In this study, we consider a practical microfluidic MC system equipped with a graphene field effect transistor biosensor (bioFET)-based MC receiver as the model system, and develop an analytical end-to-end frequency-domain model. The model provides practical insights into the dispersion and distortion of received signals, thus potentially informing the design of new frequency-domain MC techniques, such as modulation and detection methods. The accuracy of the developed model is verified through particle-based spatial stochastic simulations of pulse transmission in microfluidic channels and ligand-receptor binding reactions on the receiver surface

Modeling convection-diffusion-reaction systems for microfluidic molecular communications with surface-based receivers in Internet of Bio-Nano Things.

We consider a microfluidic molecular communication (MC) system, where the concentration-encoded molecular messages are transported via fluid flow-induced convection and diffusion, and detected by a surface-based MC receiver with ligand receptors placed at the bottom of the microfluidic channel. The overall system is a convection-diffusion-reaction system that can only be solved by numerical methods, e.g., finite element analysis (FEA). However, analytical models are key for the information and communication technology (ICT), as they enable an optimisation framework to develop advanced communication techniques, such as optimum detection methods and reliable transmission schemes. In this direction, we develop an analytical model to approximate the expected time course of bound receptor concentration, i.e., the received signal used to decode the transmitted messages. The model obviates the need for computationally expensive numerical methods by capturing the nonlinearities caused by laminar flow resulting in parabolic velocity profile, and finite number of ligand receptors leading to receiver saturation. The model also captures the effects of reactive surface depletion layer resulting from the mass transport limitations and moving reaction boundary originated from the passage of finite-duration molecular concentration pulse over the receiver surface. Based on the proposed model, we derive closed form analytical expressions that approximate the received pulse width, pulse delay and pulse amplitude, which can be used to optimize the system from an ICT perspective. We evaluate the accuracy of the proposed model by comparing model-based analytical results to the numerical results obtained by solving the exact system model with COMSOL Multiphysics

Maximum Likelihood Detection With Ligand Receptors for Diffusion-Based Molecular Communications in Internet of Bio-Nano Things.

Molecular Communication (MC) is a bio-inspired communication technique that uses molecules as a method of information transfer among nanoscale devices. MC receiver is an essential component having profound impact on the communication system performance. However, the interaction of the receiver with information bearing molecules has been usually oversimplified in modeling the reception process and developing signal detection techniques. In this paper, we focus on the signal detection problem of MC receivers employing receptor molecules to infer the transmitted messages encoded into the concentration of molecules, i.e., ligands. Exploiting the observable characteristics of ligand-receptor binding reaction, we first introduce a Maximum Likelihood (ML) detection method based on instantaneous receptor occupation ratio, as aligned with the current MC literature. Then, we propose a novel ML detection technique, which exploits the amount of time the receptors stay unbound in an observation time window. A comprehensive analysis is carried out to compare the performance of the detectors in terms of bit error probability. In evaluating the detection performance, emphasis is given to the receptor saturation problem resulting from the accumulation of messenger molecules at the receiver as a consequence of intersymbol interference. The results reveal that detection based on receptor unbound time is quite reliable even in saturation, whereas the reliability of detection based on receptor occupation ratio substantially decreases as the receiver gets saturated. Finally, we also discuss the potential methods of implementing the detectors

Microfluidic Molecular Communication Transmitter Based on Hydrodynamic Gating

Molecular Communications (MC) is a bio-inspired paradigm for transmitting information using chemical signals, which can enable novel applications at the junction of biotechnology, nanotechnology, and information and communication technologies. However, designing efficient and reliable MC systems poses significant challenges due to the complex nature of the physical channel and the limitations of the micro/nanoscale transmitter and receiver devices. In this paper, we propose a practical microfluidic transmitter architecture for MC based on hydrodynamic gating, a widely utilized technique for generating chemical waveforms in microfluidic channels with high spatiotemporal resolution. We develop an approximate analytical model that can capture the fundamental characteristics of the generated molecular pulses, such as pulse width, pulse amplitude, and pulse delay, as functions of main system parameters, such as flow velocity and gating duration. We validate the accuracy of our model by comparing it with finite element simulations using COMSOL Multiphysics under various system settings. Our analytical model can enable the optimization of microfluidic transmitters for MC applications in terms of minimizing intersymbol interference and maximizing data transmission rate

Frequency-Domain Detection for Molecular Communication with Cross-Reactive Receptors

Molecular Communications (MC) is a bio-inspired communication paradigm that uses molecules as information carriers, requiring unconventional transceivers and modulation/detection techniques. Practical MC receivers (MC-Rxs) can be implemented using field-effect transistor biosensor (bioFET) architectures, where surface receptors reversibly react with ligands. The time-varying concentration of ligand-bound receptors is translated into electrical signals via field effect, which is used to decode the transmitted information. However, ligand-receptor interactions do not provide an ideal molecular selectivity, as similar ligand types, i.e., interferers, co-existing in the MC channel, can interact with the same type of receptors. Overcoming this molecular cross-talk in the time domain can be challenging, especially when Rx has no knowledge of the interferer statistics or operates near saturation. Therefore, we propose a frequency-domain detection (FDD) technique for bioFET-based MC-Rxs that exploits the difference in binding reaction rates of different ligand types reflected in the power spectrum of the ligand-receptor binding noise. We derive the bit error probability (BEP) of the FDD technique and demonstrate its effectiveness in decoding transmitted concentration signals under stochastic molecular interference compared to a widely used time-domain detection (TDD) technique. We then verified the analytical performance bounds of the FDD through a particle-based spatial stochastic simulator simulating reactions on the MC-Rx in microfluidic channels.Comment: Submitted to the IEEE for possible publication. arXiv admin note: text overlap with arXiv:2301.0104

D-DSC: Decoding Delay-based Distributed Source Coding for Internet of Sensing Things.

Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST)

Channel Sensing in Molecular Communications with Single Type of Ligand Receptors

Molecular Communications (MC) uses molecules as information carriers between nanomachines. MC channel in practice can be crowded with different types of molecules, i.e., ligands, which can have similar binding properties causing severe cross-talk on ligand receptors. Simultaneous sensing of multiple ligand types provides opportunities for eliminating interference of external molecular sources and multi-user interference (MUI), and developing new multiple access techniques for MC nanonetworks. In this paper, we investigate channel sensing methods that use only a single type of receptors and exploit the amount of time receptors stay bound and unbound during ligand-receptor binding reaction to concurrently estimate the concentration of multiple types of ligands. We derive the Cram\'er-Rao Lower Bound (CRLB) for multi-ligand estimation, and propose practical and low-complexity suboptimal estimators for channel sensing. We analyze the performance of the proposed methods in terms of normalized mean squared error (NMSE), and show that they can efficiently estimate the concentration of ligands up to $10$ different types with an average NMSE far below $10^{-2}$. Lastly, we propose a synthetic receptor design based on modified kinetic proofreading (KPR) scheme to sample the unbound and bound time durations, and a Chemical Reaction Network (CRN) to perform the required computations in synthetic cells.This work was supported in part by the ERC projects MINERVA (ERC-2013-CoG #616922), and MINERGRACE (ERC-2017- PoC #780645)