Large intelligent reflecting surfaces (IRSs) possess a large near-field (NF) range, which makes their configuration challenging. To reduce the potentially significant overhead associated with configuring these large IRSs, phase shift codebooks are a promising approach. However, most existing IRS codebooks focus on maximizing the beamforming gain of the IRS, rather than optimizing the signal-to-noise ratio (SNR) at the receiver, which is more critical to the quality of service (QoS). In this paper, we introduce an IRS codebook design that maximizes the minimum SNR within a target receiver volume by optimizing the IRS phase shifts. We observe that, in the considered NF range, maximizing the IRS reflection gain for the farthest surface of the target receiving volume is equivalent to maximizing the SNR within the entire volume. Based on this observation, we derive an analytical phase shift design, where each unit cell (UC) of the IRS focuses on a specific point. Next, we formulate a nonconvex optimization problem, for a second, improved codebook design, and find a local optimum for the minimum SNR based on sucessive convex approximation (SCA). The optimized design reveals a trade-off between the minimum SNR and the codebook size, while achieving excellent performance across the entire considered NF. Numerical evaluations show that both proposed designs outperform several baseline schemes from the literature.

The recently emerged molecular communication (MC) paradigm intends to leverage communication engineering tools for the design of synthetic chemical communication systems. These systems are envisioned to operate at nanoscale and in biological environments, such as the human body, and catalyze the emergence of revolutionary applications in the context of early disease monitoring and drug targeting. Despite the abundance of theoretical (and recently also experimental) MC system designs proposed over the past years, some fundamental questions remain unresolved, hindering the breakthrough of MC in real-world applications. One of these questions is: What can be a useful measure of information in the context of MC applications? While most existing works on MC build upon the concept of syntactic information as introduced by Shannon, in this paper, we explore the framework of semantic information as introduced by Kolchinsky and Wolpert for the information-theoretic analysis of a natural MC system, namely bacterial chemotaxis. Exploiting computational agent-based modeling (ABM), we are able to quantify, for the first time, the amount of information that the considered chemotactic bacterium (CB) utilizes to adapt to and survive in a dynamic environment. In other words, we show how the flow of information between the environment and the CB is related to the effectiveness of communication. Effectiveness here refers to the adaptation of the CB to the dynamic environment in order to ensure survival. Our analysis reveals that it highly depends on the environmental conditions how much information the CB can effectively utilize for improving their survival chances. Encouraged by our results, we envision that the proposed semantic information framework can open new avenues for the development of theoretical and experimental MC system designs for future nanoscale applications.

In this paper, we present a fluid-based experimental molecular communication (M C) testbed that, similar to the human cardiovascular system, operates in a closed circuit tube system. The proposed system is designed to be biocompatible, resource-efficient, and controllable from outside the tube. As signaling molecule, the testbed employs the green fluorescent protein variant “Dreiklang” (GFPD). GFPDs can be reversibly switched via light of different wavelengths between a bright fluorescent state and a less fluorescent state. Hence, this property allows for writing and erasing information encoded in the state of the G FPDs already present in the fluid via radiation from outside the tube. The concept of modulating the GFPDs existing in the channel at the transmitter for information transmission, instead of releasing new molecules, is a form of media modulation. In our testbed, due to the closed loop setup and the long experiment durations of up to 250 min, we observe new forms of inter-symbol interferences (ISI), which do not occur in short experiments and open loop systems. In particular, up to four different forms of ISI, namely channel ISI, inter-loop ISI, offset ISI, and permanent ISI, occur in the considered system. To mitigate inter-loop ISI and offset ISI, we propose a light based eraser unit. We experimentally demonstrate reliable information transmission in our testbed achieving error-free transmission of 500 bit at a data rate of 6 bit min−1based on a sub-optimal low-complexity detection scheme.

Integrating different functionalities, conventionally implemented as dedicated systems, into a single platform allows utilising the available resources more efficiently. We consider an integrated sensing and power transfer (ISAPT) system and propose the joint optimisation of the rectangular pulse-shaped transmit signal and the beamforming vector to combine sensing and wireless power transfer (WPT) functionalities efficiently. In contrast to prior works, we adopt an accurate non-linear circuit-based energy harvesting (EH) model. We formulate and solve a non-convex optimisation problem for a general number of EH receivers to maximise a weighted sum of the average harvested powers at the EH receivers while ensuring the received echo signal reflected by a sensing target (ST) has sufficient power for estimating the range to the ST with a prescribed accuracy within the considered coverage region. The average harvested power is shown to monotonically increase with the pulse duration when the average transmit power budget is sufficiently large. We discuss the trade-off between sensing performance and power transfer for the considered ISAPT system. The proposed approach significantly outperforms a heuristic baseline scheme based on a linear EH model, which linearly combines energy beamforming with the beamsteering vector in the direction to the ST as its transmit strategy.

Future communication systems are envisioned to employ intelligent reflecting surfaces (IRSs) and the millimeter wave (mmWave) frequency band to provide reliable high-rate services. For mobile users, the time-varying channel state information (CSI) requires adequate adjustment of the reflection pattern of the IRS. We propose a novel codebook-based user tracking (UT) algorithm for IRS-assisted mmWave communication, allowing suitable reconfiguration of the IRS unit cell phase shifts, resulting in a high reflection gain. The presented algorithm acquires the direction information of the user based on a peak maximum likelihood (ML)-based direction estimation. Using the direction information, the user’s trajectory is extrapolated to proactively update the adopted codeword and adjust the IRS phase shift configuration accordingly. Furthermore, we conduct a theoretical analysis of the direction estimation error and utilize the obtained insights to design a codebook specifically optimized for direction estimation. Our results show that the proposed ML-based direction estimation algorithm outperforms a multiple signal classification (MUSIC)-based reference scheme. The proposed direction estimation codebook improves the direction estimation error for both these schemes as compared to when a reference codebook is used. Also, the accuracy of the proposed UT algorithm is shown to be competitive with Kalman filter-based UT, while the proposed scheme requires fewer a priori assumptions on the user movement. Furthermore, the average achieved signal-to-noise ratio (SNR) as well as the average effective rate of the proposed UT algorithm are analyzed. The proposed UT algorithm requires only a low overhead for direction and channel estimation and avoids outdated IRS phase shifts. Furthermore, it is shown to outperform three benchmark schemes based on direct phase shift optimization, optimal codeword selection, and hierarchical codebook search, respectively, via computer simulations.

Controlled drug delivery (CDD), the controlled release and delivery of therapeutic drugs inside the human body, is a promising approach to increase the efficacy of drug administration and reduce harmful side effects to the body. CDD has been a major research focus in the field of molecular communications (MC) with the goal to aid the design and optimization of CDD systems with communication theoretical analysis. However, the existing studies of CDD under the MC framework are purely theoretical, and the potential of MC for the development of practical CDD applications remains yet to be shown. This paper presents a step towards filling this research gap. Specifically, we present a novel MC-based model for a specific CDD system in which drugs are embedded into microparticles and released gradually towards the target site. It is demonstrated that the proposed model is able to faithfully reproduce experimental data. Furthermore, statistical analysis is conducted to explore the impact of the microparticle size on the drug release. The presented results reveal the sensitivity of the drug release to changes in the microparticle size. In this way, the proposed model can be used for the design of future microparticle-based CDD systems.

Synaptic communication is studied by communication engineers for two main reasons. One is to enable novel neuroengineering applications that require interfacing with neurons. The other reason is to draw inspiration for the design of synthetic molecular communication systems. Both of these goals require understanding of how the chemical synaptic signal is sensed and transduced at the synaptic receiver (Rx). While signal reception in synaptic molecular communication (SMC) depends heavily on the kinetics of the receptors employed by the synaptic Rxs, existing channel models for SMC either oversimplify the receptor kinetics or employ complex, high-dimensional kinetic schemes limited to specific types of receptors. Both approaches do not facilitate a comparative analysis of different types of natural synapses. In this paper, we propose a novel deterministic channel model for SMC which employs a generic three-state receptor model that captures the characteristics of the most important receptor types in SMC. The model is based on a transfer function expansion of Fick's diffusion equation and accounts for release, diffusion, and degradation of neurotransmitters as well as their reversible binding to finitely many generic postsynaptic receptors. The proposed SMC model is the first that allows studying the impact of the characteristic dynamics of the main postsynaptic receptor types on synaptic signal transmission. Numerical results indicate that the proposed model indeed exhibits a wide range of biologically plausible dynamics when specialized to specific natural receptor types.

In conventional molecular communication (MC) systems, the signaling molecules used for information transmission are stored, released, and then replenished by a transmitter (TX). However, the replenishment of signaling molecules at the TX is challenging in practice. Furthermore, in most envisioned MC applications, e.g., in the medical field, it is not desirable to insert the TX into the MC system, as this might impair natural biological processes. In this paper, we propose the concept of media modulation based MC where the TX is placed outside the channel and utilizes signaling molecules already existing inside the system. We consider signaling molecules that can be in different states which can be switched by external stimuli. Hence, in media modulation based MC, for information transmission, the TX stimulates the signaling molecules to encode information into their state. In particular, we elaborate media modulation for the group of photochromic molecules, which undergo light-induced reversible transformations, and study the usage of these molecules for information transmission in a three-dimensional duct system. We develop a statistical model for the received signal which depends on the distribution of the signaling molecules in the system, the reliability of the state control mechanism, and the randomness of molecule propagation. Furthermore, we analyze the performance of media modulation based MC in terms of the bit error rate (BER). We show that media modulation enables reliable information transmission, which renders a TX inside the channel unnecessary.

In synaptic molecular communication, the activation of postsynaptic receptors by neurotransmitters (NTs) is governed by a stochastic reaction-diffusion process and, hence, inherently random. It is currently not fully understood how this randomness impacts downstream signaling in the target cell and, ultimately, neural computation and learning. The statistical characterization of the reaction-diffusion process is difficult because the reversible bi-molecular reaction of NTs and receptors renders the system nonlinear. Consequently, existing models for the receptor occupancy in the synaptic cleft rely on simplifying assumptions and approximations which limit their practical applicability. In this work, we propose a novel statistical model for the reaction-diffusion process governing synaptic signal transmission in terms of the chemical master equation (CME). We show how to compute the CME efficiently and verify the accuracy of the obtained results with stochastic particle-based computer simulations (PBSs). Furthermore, we compare the proposed model to two benchmark models proposed in the literature and show that it provides more accurate results when compared to PBSs. Finally, the proposed model is used to study the impact of the system parameters on the statistical dependence between binding events of NTs and receptors. In summary, the proposed model provides a step forward towards a complete statistical characterization of synaptic signal transmission.

In conventional molecular communication (MC) systems, the signaling molecules used for information transmission are stored, released, and then replenished by a transmitter (TX). However, the replenishment of signaling molecules at the TX is challenging in practice. Furthermore, in most envisioned MC applications, e.g., in the medical field, it is not desirable to insert the TX into the MC system, as this might impair natural biological processes. In this paper, we propose the concept of media modulation based MC where the TX is placed outside the channel and utilizes signaling molecules already present inside the system. The signaling molecules can assume different states which can be switched by external stimuli. Hence, in media modulation based MC, the TX modulates information into the state of the signaling molecules. In particular, we exploit the group of photochromic molecules, which undergo light-induced reversible state transitions, for media modulation. We study the usage of these molecules for information transmission in a three-dimensional duct system, which contains an eraser, a TX, and a receiver for erasing, writing, and reading of information via external light, respectively. We develop a statistical model for the received signal which accounts for the distribution of the signaling molecules in the system, the initial states of the signaling molecules, the reliability of the state control mechanism, the randomness of irrepressible, spontaneous state switching, and the randomness of molecule propagation. We adopt a maximum likelihood detector and show that it can be reduced to a threshold based detector. Furthermore, we derive analytical expressions for the optimal threshold value and the resulting bit error rate (BER), respectively. Both the statistical model and BER results are verified by computer simulations. Our results reveal that media modulation enables reliable information transmission, validating it as a promising alternative to MC based on molecule emitting TXs.

Synaptic communication is a natural Molecular Communication (MC) system which may serve as a blueprint for the design of synthetic MC systems. In particular, it features highly specialized mechanisms to enable inter-symbol interference (ISI)-free and energy efficient communication. The understanding of synaptic MC is furthermore critical for disruptive innovations in the context of brain-machine interfaces. However, the physical modeling of synaptic MC is complicated by the possible saturation of the molecular receiver arising from the competition of postsynaptic receptors for neurotransmitters. Saturation renders the system behavior nonlinear and is commonly neglected in existing analytical models. In this work, we propose a novel model for receptor saturation in terms of a nonlinear, state-dependent boundary condition for Fick’s diffusion equation. We solve the resulting boundary-value problem using an eigenfunction expansion of the Laplace operator and the incorporation of the receiver memory as feedback system into the corresponding state-space description. The presented solution is numerically stable and computationally efficient. Furthermore, the proposed model is validated with particle-based stochastic computer simulations.

In Diffusive Molecular Communication (DMC), information is transmitted by diffusing molecules. Synaptic signaling, as a natural implementation of this paradigm, encompasses functional components that, once understood, can facilitate the development of synthetic DMC systems. To unleash this potential, however, a thorough understanding of the synaptic communication channel based on biophysical principles is needed. Since synaptic transmission critically depends also on non-neural cells, such understanding requires the consideration of the so-called tripartite synapse. In this paper, we develop a comprehensive channel model of the tripartite synapse encompassing a three-dimensional, finite-size spatial model of the synaptic cleft, molecule uptake at the presynaptic neuron and at glial cells, reversible binding to individual receptors at the postsynaptic neuron, and spillover to the extrasynaptic space. Based on this model, we derive analytical time domain expressions for the channel impulse response (CIR) of the synaptic DMC system and for the number of molecules taken up at the presynaptic neuron and at glial cells, respectively. These expressions provide insight into the impact of macroscopic physical channel parameters on the decay rate of the CIR and the reuptake rate, and reveal fundamental limits for synaptic signal transmission induced by chemical reaction kinetics and the channel geometry. Adapted to realistic parameters, our model produces plausible results when compared to previous experimental and simulation studies and we provide results from particle-based computer simulations to further validate the analytical model. The proposed comprehensive channel model admits a wide range of synaptic configurations making it suitable for the investigation of many practically relevant questions, such as the impact of glial cell uptake and spillover on signal transmission in the tripartite synapse.

In Diffusive Molecular Communication (DMC), information is transmitted by diffusing molecules. Synaptic signaling is a natural implementation of this paradigm. It is responsible for relaying information from one neuron to another, but also provides support for complex functionalities, such as learning and memory. Many of its features are not yet understood, some are, however, known to be critical for robust, reliable neural communication. In particular, some synapses feature a re-uptake mechanism at the presynaptic neuron, which provides a means for removing neurotransmitters from the synaptic cleft and for recycling them for future reuse. In this paper, we develop a comprehensive channel model for synaptic DMC encompassing a spatial model of the synaptic cleft, molecule re-uptake at the presynaptic neuron, and reversible binding to individual receptors at the postsynaptic neuron. Based on this model, we derive an analytical time domain expression for the channel impulse response (CIR) of the synaptic DMC system. Our model explicitly incorporates macroscopic physical channel parameters and can be used to evaluate the impact of re-uptake, receptor density, and channel width on the CIR of the synaptic DMC system. Furthermore, we provide results from particlebased computer simulation, which validate the analytical model. The proposed comprehensive channel model for synaptic DMC systems can be exploited for the investigation of challenging problems, like the quantification of the inter-symbol interference between successive synaptic signals and the design of synthetic neural communication systems.