Nanomachines are envisioned for a variety of applications in the industry and health sectors operating as sensors and actuators. Considering their potential mobility, it is relevant to study the capability of nanomachines to cooperate in molecular communication scenarios. To this end, we provide new insights into the leader-follower dynamics when a mobile leader node moves randomly in three-dimensional space and emits molecules into a diffusive environment to send information about its position to a follower node. In this paper, we investigate the random distance between the two nodes due to decision errors at the follower and analyze an upper bound for the average distance as a function of time. Simulations are provided to validate our analytical results. Moreover, by comparing to the benchmark scenario of uncoordinated movement of leader and follower, we investigate for which parameters the follower can reliably follow the leader.

With the rapid expansion of wireless technologies, monitoring and regulating the Radio Frequency (RF) spectrum usage becomes more important than ever. In this paper, we present a Deep Learning (DL) based approach to analyze the RF spectrum by detecting, localizing, and classifying active signals in RF frequency bands. We represent the radio signals in wideband spectrograms and formulate the signal detection and classification problem as an object detection task related to the computer vision field. To this end, You Only Look Once (YOLO), a state-of-the-art object detector, is adapted and optimized to detect, localize, and classify signals in spectrograms. For the experimental evaluation of YOLO as a signal detector, a rich dataset was simulated, consisting of diverse signals modulated with digital and analog modulation schemes and transmitted over channels with realistic propagation conditions. Our proposed method achieves an Average Precision (AP) of almost 87% and an average Intersection over Union (IoU) of 90%, thus demonstrating significant potential for analyzing RF spectral activity with high accuracy.

Many blind detection algorithms for paired carrier multiple access (PCMA) signals in satellite communications require information about the signal and channel parameters. A blind receiver needs to obtain knowledge of at least some of these prior to the symbol detection process. In this paper, schemes for the blind estimation of the symbol rates and carrier frequencies of the two overlapping co-channel signals in PCMA are proposed. The methods exploit the cyclic statistics of the PCMA signal for the estimation of the desired parameters. Effects of the mutual interference of the carriers in the estimation process are analyzed and the performance of the proposed methods is evaluated via numerical simulations.

The derivation of suitable analytical models is an important step for the design and analysis of molecular communication systems. However, many existing models have limited applicability in practical scenarios due to various simplifications (e.g., assumption of an unbounded environment). In this paper, we develop a realistic model for particle diffusion in a bounded sphere and particle transport through a semi-permeable boundary. This model can be used for various applications, such as modeling of inter-/intra-cell communication or the release process of drug carriers. The proposed analytical model is based on a transfer function approach, which allows for fast numerical evaluation and provides insights into the impact of the relevant molecular communication system parameters. The proposed solution of the bounded spherical diffusion problem is formulated in terms of a state-space description and the semi-permeable boundary is accounted for by a feedback loop. Particle-based simulations verify the proposed modeling approach.

In molecular communications, the direct detection of signaling molecules may be challenging due to the lack of suitable sensors and interference from co-existing substances in the environment. Motivated by examples in nature, we investigate an indirect detection mechanism using chemical reactions between the signaling molecules and a molecular probe to produce an easy-to-measure product at the receiver. The underlying reaction-diffusion equations that describe the concentrations of the reactant and product molecules in the system are non-linear and coupled, and cannot be solved in closed-form. To analyze these molecule concentrations, we develop an efficient iterative algorithm by discretizing the time variable and solving for the space variables in each time step. We also derive insightful closed-form solutions for a special case. The accuracy of the proposed algorithm is verified by particle-based simulations. Our results show that the concentration of the product molecules has a similar characteristic over time as the concentration of the signaling molecules. We analyze the bit error rate (BER) for a threshold detector and highlight that significant improvements in the BER can be achieved by carefully choosing the molecular probe and optimizing the detection threshold.

Nanoparticle drug carriers play an important role in facilitating efficient targeted drug delivery, i.e., improving treatment success and reducing drug costs and side effects. However, the mobility of nanoparticle drug carriers poses a challenge in designing drug delivery systems. Moreover, healing results critically depend on the rate and time duration of drug absorption. Therefore, in this paper, we aim to design a controlled-release drug delivery system with a mobile drug carrier that minimizes the total amount of released drugs while ensuring a desired rate of drug absorption during a prescribed time period. We model the mobile drug carrier as a mobile transmitter, the targeted diseased cells as an absorbing receiver, and the channel between the transceivers as a time-variant channel since the carrier mobility results in a time-variant absorption rate of the drug molecules. Based on this, we develop a molecular communication (MC) framework to design the controlled-release drug delivery system. In particular, we develop new analytical expressions for the mean, variance, probability density function, and cumulative distribution function of the channel impulse response (CIR). Equipped with the statistical analysis of the CIR, we design and evaluate the performance of the controlled-release drug delivery system. Numerical results show significant savings in the amount of released drugs compared to a constant-release rate design and reveal the necessity of accounting for drug carrier mobility for reliable drug delivery.

This paper considers particle propagation in a cylindrical molecular communication channel, e.g. a simplified model of a blood vessel. Emitted particles are influenced by diffusion, flow, and a vertical force induced e.g. by gravity or magnetism. The dynamics of the diffusion process are modeled by multi dimensional transfer functions in a spatio-temporal frequency domain. Realistic boundary conditions are incorporated by the design of a feedback loop. The result is a discrete-time semi-analytical model for the particle concentration in the channel. The model is validated by comparison to particle-based simulations. These numerical experiments reveal that the particle concentration of the proposed semi-analytical model and the particle-based model are in excellent agreement. The analytical form of the proposed solution provides several benefits over purely numerical models, e.g. high flexibility, existence of low run-time algorithms, extendability to several kinds of boundary conditions, and analytical connection to parameters from communication theory.

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 the channel models for time-varying 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 the simulation- and experiment-driven channel models. Finally, we provide a detailed discussion of potential challenges, open research problems, and future directions in channel modeling for diffusive MC systems.

In this paper, we consider target detection in suspicious tissue via diffusive molecular communications (MCs). If a target is present, it continuously and with a constant rate secretes molecules of a specific type, so-called biomarkers, into the medium, which are symptomatic for the presence of the target. Detection of these biomarkers is challenging since due to the diffusion and degradation, the biomarkers are only detectable in the vicinity of the target. In addition, the exact location of the target within the tissue is not known. In this paper, we propose to distribute several reactive nanosensors (NSs) across the tissue such that at least some of them are expected to come in contact with biomarkers, which cause them to become activated. Upon activation, an NS releases a certain number of molecules of a secondary type into the medium to alert a fusion center (FC), where the final decision regarding the presence of the target is made. In particular, we consider a composite hypothesis testing framework where it is assumed that the location of the target and the biomarker secretion rate are unknown, whereas the locations of the NSs are known. We derive the uniformly most powerful (UMP) test for the detection at the NSs. For the final decision at the FC, we show that the UMP test does not exist. Hence, we derive a genie-aided detector as an upper bound on performance. We then propose two sub-optimal detectors and evaluate their performance via simulations.

Active transport such as fluid flow is sought in molecular communication to extend coverage, improve reliability, and mitigate interference. Flow models are often over-simplified, assuming one-dimensional diffusion with constant drift. However, diffusion and flow are usually encountered in three-dimensional bounded environments where the flow is highly non-uniform such as in blood vessels or microfluidic channels. For a qualitative understanding of the relevant physical effects inherent to these channels, based on the Péclet number and the transmitter-receiver distance, we study when simplified models of uniform flow and advection-only transport are applicable. For these two regimes, analytical expressions for the channel impulse response are derived and validated by particle-based simulation. Furthermore, as advection-only transport is typically overlooked and hence not analyzed in the molecular communication literature, we evaluate the symbol error rate for exemplary on-off keying as performance metric.

In this paper, we propose to use magnetic nanoparticles as information carriers for molecular communication. This enables the use of an external magnetic field to guide information-carrying particles towards the receiver. We show that the particle movement can be mathematically modeled as diffusion with drift. Thereby, we reveal that the key parameters determining the magnetic force are particle size and magnetic field gradient. As an example, we consider magnetic nanoparticle based communication in a bounded two-dimensional environment. For this model, we derive an analytical expression for the channel impulse response subject to fluid flow and magnetic drift. Numerical results, obtained by particle-based simulation, validate the accuracy of the derived analytical expressions. Furthermore, adopting the symbol error rate as performance metric, we show that using magnetic nanoparticles facilitates reliable communication, even in the presence of fluid flow.

We consider the two-hop half-duplex (HD) relay network, where the source-to-relay and relay-to-destination links are impaired by block fading. The relay is equipped with a buffer, which enables the relay to receive or transmit in each time slot independent of previous time slots. As a practical constraint, source and relay can transmit only at rates taken from predefined and finite sets. Thereby, it is assumed that for each time slot, the instantaneous qualities of the two links are available. For this network, we derive the optimal scheduling of reception and transmission at the relay and the optimal rate selection at source and relay, such that the throughput is maximized. Since the optimal protocol introduces unbounded delay, we also propose a buffer-aided protocol, which limits the delay. For this delay-limited protocol, we study the achieved delay and throughput by modeling the queue at the buffer as a Markov chain. Our numerical results show that the throughputs achieved with the proposed buffer-aided protocols for discrete transmission rates are significantly larger than the throughputs achieved with conventional relaying protocols where the HD relay switches between reception and transmission in a strictly alternating manner.