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This paper presents measurements and statistical characterization to compare three potential bands of the low-THz channel, namely, the 300 to 319 GHz, 340 to 359 GHz and 380 to 399 GHz bands. From the large set of measurements performed in line-of-sight (LoS) and non-LoS (NLoS) environments, parameters for path loss model with shadowing are evaluated. Our results show that the path loss exponents for the band around 310 GHz, 350 GHz, and 390 GHz is 2.07, 1.90 and 1.96, respectively. The impacts of different materials acting as surfaces in LoS channels and reflectors in NLoS environments are also examined. Additionally, the statistical analysis due to temporal, spatial and multipath propagation is performed to determine the best fit distributions. Finally, we look at some networking scenarios in THz Band communication to derive the expressions for the number of connections a user can make based on antenna characteristics, data rate requirements and antenna mobility as well as network density. Our results suggest fundamental parameters that can be used in future THz Band analysis with applications in both macro and micro scale Internet of Things (IoT)., Scientific and Technological Research Council of Turkey (TÜBİTAK)

In this paper, we develop a realistic model of the synaptic multiple-input single-output (MISO) communication channel for cortical neurons. The synaptic channel weights change adaptively according to the rules of spike timing-dependent plasticity (STDP) to enable learning and memory within neuronal connections. We calculate the ergodic capacity of the synaptic multiple-input multiple-output (MIMO) communication channel, and investigate its performance using the statistical properties of neuro-spike communication. Moreover, we analyze the communication performance of synaptic channels in terms of decoding error probability, and define a lower bound on the synaptic multiple-input single-output (MISO) communication channel.

To realize molecular nanonetworks, the foundations of molecular information theory should be established through identification of the existing molecular communication mechanisms, and architectures and networking techniques for nanomachines should be developed, which demand novel engineering efforts. Luckily, these engineering skills and technology have been prepared for us by the natural evolution in the last several billions of years. Indeed, the human body is a massive nanoscale molecular communications network as it is composed of billions of interacting nanomachines, i.e., cells. Intra-body biological systems are closely linked to each other and communicate primarily through molecular transactions. Thus, vital activities inside the human body are regulated by everlasting communication performance and operations of intra-body molecular nanonetworks. However, natural intra-body molecular nanonetworks are yet to be explored with the elegant tools of information and communication theories. In this paper, first, the elementary models for significant intra-body molecular communication channels, i.e., nanoscale neuro-spike communication channel, action potential-based cardiomyocyte molecular communication channel, and hormonal molecular communication channel, are introduced. Next, molecular nanonetworks belonging to multi-terminal extensions of channel models, i.e., nervous, cardiovascular molecular, and endocrine nanonetworks are discussed. Furthermore, heterogeneous communication network of intra-body molecular nanonetworks together with five senses, i.e., nanosensory networks, is explored from the perspectives of communication and network theories. Moreover, open research challenges, such as extension of molecular channel models to multi-terminal cases, and developing a communication theory perspective to understand the physiology and to capture potential communication failures of intra-body biological systems, are provided. Our objectives are to learn from the elegant molecular communication mechanisms inside us for engineering practical communication techniques for emerging nanonetworks, as well as to pave the way for the advancement of revolutionary diagnosis and treatment techniques inspired from information and communication technologies, which is promising for future nanomedicine and bio-inspired molecular communication applications.

A number of nanomachines that cooperatively communicate and share molecular information in order to achieve specific tasks is envisioned as a nanonetwork. Due to the size and capabilities of nanomachines, the traditional communication paradigms cannot be used for nanonetworks in which network nodes may be composed of just several atoms or molecules and scale on the orders of few nanometers. Instead, molecular communication is a promising solution approach for the nanoscale communication paradigm. However, molecular communication must be thoroughly investigated to realize nanoscale communication and nanonetworks for many envisioned applications such as nanoscale body area networks, and nanoscale molecular computers. In this paper, a simulation framework (NanoNS) for molecular nanonetworks is presented. The objective of the framework is to provide a simulation tool in order to create a better understanding of nanonetworks and facilitate the development of new communication techniques and the validation of theoretical results. The NanoNS framework is built on top of core components of a widely used network simulator (ns-2). It incorporates the simulation modules for various nanoscale communication paradigms based on a diffusive molecular communication channel. The details of NanoNS are discussed and some functional scenarios are defined to validate NanoNS. In addition to this, the numerical analyses of these functional scenarios and their experimental results are presented. The validation of NanoNS is shown via comparative evaluation of these experimental and numerical results.

Molecular communication enables nanomachines to exchange information with each other by emitting molecules to their surrounding environment. Molecular nanonetworks are envisioned as a number of nanomachines that are deployed in an environment to share specific molecular information such as odor, flavor, or any chemical state. In this paper, using the stochastic model of molecular reactions in biochemical systems, a realistic channel model is first introduced for molecular communication. Then, based on the realistic channel model, we introduce a deterministic capacity expression for point-to-point, broadcast, and multiple-access molecular channels. We also investigate information flow capacity in a molecular nanonetwork for the realization of efficient communication and networking techniques for frontier nanonetwork applications. The results reveal that molecular point-to-point, broadcast, and multiple-access channels are feasible with a satisfactorily high molecular communication rate, which allows molecular information flow in nanonetworks. Furthermore, the derived molecular channel model with input-dependent noise term also reveals that unlike a traditional Gaussian communication channel, achievable capacity is affected by both lower and upper bounds of the channel input in molecular communication channels.