Spatial spectrum and energy efficiency of random cellular networks

It is a great challenge to evaluate the network performance of cellular mobile communication systems. In this paper, we propose new spatial spectrum and energy efficiency models for Poisson-Voronoi tessellation (PVT) random cellular networks. To evaluate the user access the network, a Markov chain based wireless channel access model is first proposed for PVT random cellular networks. On that basis, the outage probability and blocking probability of PVT random cellular networks are derived, which can be computed numerically. Furthermore, taking into account the call arrival rate, the path loss exponent and the base station (BS) density in random cellular networks, spatial spectrum and energy efficiency models are proposed and analyzed for PVT random cellular networks. Numerical simulations are conducted to evaluate the network spectrum and energy efficiency in PVT random cellular networks.Comment: appears in IEEE Transactions on Communications, April, 201

Research on Reliable Low-Power Wide-Area Communications Utilizing Multi-RAT LPWAN Technologies for IoT Applications

Předkládaná disertační práce je zaměřena na „Výzkum spolehlivé komunikace pro IoT aplikace v bezdrátových sítích využívajících technologie Multi-RAT LPWAN“. Navzdory značnému pokroku v oblasti vývoje LPWA technologií umožňující masivní komunikace mezi zařízeními (mMTC), nemusí tyto technologie výkonnostně dostačovat pro nově vznikající aplikace internetu věcí. Hlavním cílem této disertační práce je proto nalezení a vyhodnocení limitů současných LPWA technologií. Na základě těchto dat jsou nevrženy nové mechanismy umožňující snazší plánování a vyhodnocování síťového pokrytí. Navržené nástroje jsou vyladěny a validovány s využitím dat získaných z rozsáhlých měřících kampaních provedených v zákaznických LPWA sítích. Tato disertační práce dále obsahuje návrh LPWA zařízení vybavených více komunikačními rozhraními (multi-RAT) které mohou umožnit překonání výkonnostních limitů jednotlivých LPWA technologií. Současná implementace se zaměřuje zejména na snížení spotřeby zařízení s více rádiovými rozhraními, což je jejich největší nevýhodou. K tomuto účelu je využito algoritmů strojového učení, které jsou schopné dynamicky vybírat nejvhodnější rozhraní k přenosu.This doctoral thesis addresses the “Research on Reliable Low-Power Wide-Area Communications Utilizing Multi-RAT LPWAN Technologies for IoT Applications”. Despite the immense progress in massive Machine-Type Communication (mMTC) technology enablers such as Low-Power Wide-Area (LPWA) networks, their performance does not have to satisfy the requirements of novelty Internet of Things (IoT) applications. The main goal of this Ph.D. work is to explore and evaluate the limitations of current LPWA technologies and propose novel mechanisms facilitating coverage planning and assessment. Proposed frameworks are fine-tuned and cross-validated by the extensive measurement campaigns conducted in public LPWA networks. This doctoral thesis further introduces the novelty approach of multi-RAT LPWA devices to overcome the performance limitation of individual LPWA technologies. The current implementation primarily focuses on diminishing the greatest multi-RAT solutions disadvantage, i.e., increased power consumption by employing a machine learning approach to radio interface selection.

Targeting Tight Junctions in Nanomedicine: a Molecular Modeling Perspective

Molecular Dynamics Simulations of Claudin Paracellular Channel

Efficient algorithms for simulation and analysis of many-body systems

This thesis introduces methods to efficiently generate and analyze time series data of many-body systems. While we have a strong focus on biomolecular processes, the presented methods can also be applied more generally. Due to limitations of microscope resolution in both space and time, biomolecular processes are especially hard to observe experimentally. Computer models offer an opportunity to work around these limitations. However, as these models are bound by computational effort, careful selection of the model as well as its efficient implementation play a fundamental role in their successful sampling and/or estimation. Especially for high levels of resolution, computer simulations can produce vast amounts of high-dimensional data and in general it is not straightforward to visualize, let alone to identify the relevant features and processes. To this end, we cover tools for projecting time series data onto important processes, finding over time geometrically stable features in observable space, and identifying governing dynamics. We introduce the novel software library deeptime with two main goals: (1) making methods which were developed in different communities (such as molecular dynamics and fluid dynamics) accessible to a broad user base by implementing them in a general-purpose way, and (2) providing an easy to install, extend, and maintain library by employing a high degree of modularity and introducing as few hard dependencies as possible. We demonstrate and compare the capabilities of the provided methods based on numerical examples. Subsequently, the particle-based reaction-diffusion simulation software package ReaDDy2 is introduced. It can simulate dynamics which are more complicated than what is usually analyzed with the methods available in deeptime. It is a significantly more efficient, feature-rich, flexible, and user-friendly version of its predecessor ReaDDy. As such, it enables---at the simulation model's resolution---the possibility to study larger systems and to cover longer timescales. In particular, ReaDDy2 is capable of modeling complex processes featuring particle crowding, space exclusion, association and dissociation events, dynamic formation and dissolution of particle geometries on a mesoscopic scale. The validity of the ReaDDy2 model is asserted by several numerical studies which are compared to analytically obtained results, simulations from other packages, or literature data. Finally, we present reactive SINDy, a method that can detect reaction networks from concentration curves of chemical species. It extends the SINDy method---contained in deeptime---by introducing coupling terms over a system of ordinary differential equations in an ansatz reaction space. As such, it transforms an ordinary linear regression problem to a linear tensor regression. The method employs a sparsity-promoting regularization which leads to especially simple and interpretable models. We show in biologically motivated example systems that the method is indeed capable of detecting the correct underlying reaction dynamics and that the sparsity regularization plays a key role in pruning otherwise spuriously detected reactions

Super-resolution mapping of receptor engagement during HIV entry

The plasma membrane (PM) serves as a major interface between the cell and extracellular stimuli. Studies indicate that the spatial organisation and dynamics of receptors correlate with the regulation of cellular responses. However, the nanoscale spatial organisation of specific receptor molecules on the surface of cells is not well understood primarily because these spatial events are beyond the resolving power of available tools. With the development in super-resolution microscopy and quantitative analysis approaches, it optimally poises me to address some of these questions. The human immunodeficiency virus type-1 (HIV-1) entry process is an ideal model for studying the functional correlation of the spatial organisation of receptors. The molecular interactions between HIV envelope glycoprotein (Env) and key receptors, CD4 and co-receptor CCR5/CXCR4, on the PM of target cells have been well characterised. However, the spatial organisation that receptors undergo upon HIV-1 binding remains unclear. In this project, I established a Single Molecule Localisation Microscopy (SMLM) based visualisation and quantitative analysis pipeline to characterise CD4 membrane organisation in CD4+ T cells, the main host cell target for HIV-1 infection. I found that prior to HIV engagement, CD4 and CCR5 molecules are organised in small distinct clusters across the PM. Upon HIV-1 engagement, I observed dynamic congregation and subsequent dispersal of virus-associated CD4 clusters within 10min. I further incorporated statistical modelling to show that this reorganisation is not random. This thesis provides one of the first nanoscale imaging and quantitative pipelines for visualising and quantifying membrane receptors. I showed that this quantitative approach provides a robust methodology for understanding the recruitment of HIV-1 receptors before the formation of a fusion pore. This methodology can be applied to the analyses of the nanoscale organisation of PM receptors to link the spatial organisation to function

The Impact of Dynamics in Protein Assembly

Predicting the assembly of multiple proteins into specific complexes is critical to understanding their biological function in an organism, and thus the design of drugs to address their malfunction. Consequently, a significant body of research and development focuses on methods for elucidating protein quaternary structure. In silico techniques are used to propose models that decode experimental data, and independently as a structure prediction tool. These computational methods often consider proteins as rigid structures, yet proteins are inherently flexible molecules, with both local side-chain motion and larger conformational dynamics governing their behaviour. This treatment is particularly problematic for any protein docking engine, where even a simple rearrangement of the side-chain and backbone atoms at the interface of binding partners complicates the successful determination of the correct docked pose. Herein, we present a means of representing protein surface, electrostatics and local dynamics within a single volumetric descriptor, before applying it to a series of physical and biophysical problems to validate it as representative of a protein. We leverage this representation in a protein-protein docking context and demonstrate that its application bypasses the need to compensate for, and predict, specific side-chain packing at the interface of binding partners for both water-soluble and lipid-soluble protein complexes. We find little detriment in the quality of returned predictions with increased flexibility, placing our protein docking approach as highly competitive versus comparative methods. We then explore the role of larger, conformational dynamics in protein quaternary structure prediction, by exploiting large-scale Molecular Dynamics simulations of the SARS-CoV-2 spike glycoprotein to elucidate possible high-order spike-ACE2 oligomeric states. Our results indicate a possible novel path to therapeutics following the COVID-19 pandemic. Overall, we find that the structure of a protein alone is inadequate in understanding its function through its possible binding modes. Therefore, we must also consider the impact of dynamics in protein assembly

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Performance analysis of Poisson-Voronoi tessellated random cellular networks using Markov chains

© 2014 IEEE. Compared with the conventional hexagonal cellular network structure, Poisson-Voronoi tessellated (PVT) random cellular network models can better capture the topology of real cellular networks. However, the random cellular network models are often complicated to analyze. To overcome this gap, in this paper we propose to analyze the performance of PVT random cellular networks using Markov chains. Using this technique, the blocking probability and the area spectral efficiency (ASE) models are obtained. Numerical results are demonstrated which show that our proposed techniques are effective approaches to evaluate the performance of random cellular networks

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum