A Planning and Optimization Framework for Hybrid Ultra-Dense Network Topologies

The deployment of small cells has been a critical upgrade in Fourth Generation (4G) mobile networks as they provide macrocell traffic offloading gains, improved spectrum reuse and reduce coverage holes. The need for small cells will be even more critical in Fifth Generation (5G) networks due to the introduction of higher spectrum bands, which necessitate denser network deployments to support larger traffic volumes per unit area. A network densification scenario envisioned for evolved fourth and fifth generation networks is the deployment of Ultra-Dense Networks (UDNs) with small cell site densities exceeding 90 sites/km2 (or inter-site distances of less than 112 m). The careful planning and optimization of ultra-dense networks topologies have been known to significantly improve the achievable performance compared to completely random (unplanned) ultra-dense network deployments by various third-part stakeholders (e.g. home owners). However, these well-planned and optimized ultra-dense network deployments are difficult to realize in practice due to various constraints, such as limited or no access to preferred optimum small cell site locations in a given service area. The hybrid ultra-dense network topologies provide an interesting trade-off, whereby, an ultra-dense network may constitute a combination of operator optimized small cell deployments that are complemented by random small cell deployments by third-parties. In this study, an ultra-dense network multiobjective optimization framework and post-deployment power optimization approach are developed for realization and performance comparison of random, optimized and hybrid ultra-dense network topologies in a realistic urban case study area. The results of the case study demonstrate how simple transmit power optimization enable hybrid ultra-dense network topologies to achieve performance almost comparable to optimized topologies whilst also providing the convenience benefits of random small cell deployments

Reconfiguring Colloidal Solids with Defects Using Active Matter

Engineering defect configurations within atomic crystalline materials, particularly metals, is a cornerstone of material science. Crystalline defects affect every facet of a material's properties, and in this regard, crystals composed of colloidal particles are no different from their atomic analogues. What is different, at the colloidal scale, is that new techniques have been developed to allow for the application of local forces by independently operating um-scale particles. The capacity of such active matter to manipulate, produce, and remove colloidal defects is only just starting to be explored. This dissertation seeks to establish the feasibility of directly controlling crystalline defects through the action of a particle species that exerts work locally, via computer simulation. The goals of such microstructure manipulation are to create materials with dynamic properties that change in response to external stimuli. Dynamic modification of crystallite shape, optical properties, or mechanical properties such as resistance to deformation are examples of what can be achieved through the action of active particles strongly coupled to colloidal defects. This dissertation is built around four studies of the behavior of defects in colloidal materials. First, I examine the nature of dislocations in crystals composed of particles interacting through repulsive pair potentials. By comparing attractive potentials to a family of repulsive ones with differing slopes, I explore the changes to mechanical properties and dislocation structure that occur as entropy comes to dominate the deformation free energy of a material. By varying the confining pressure, I find that attractive and repulsive systems can be matched in material properties and defect strain fields. Second, I study the interactions between colloidal dislocations and anisotropic interstitial particles that are capable of exerting local forces. By representing this interstitial by the strain field it produces when embedded in the crystal, I formulate a method of optimizing the interaction of the dislocation and interstitial by allowing the strain field to fluctuate. The optimization can be carried out very quickly compared to schemes requiring molecular dynamics simulation to assess a trial geometry's fitness. By molecular dynamics simulation of optimized particles with dislocations I show that such defects can be induced to glide by the action of bound active interstitials. Third, I explore the interaction of active rod-like interstitial particles with stacking faults in face-centered cubic colloidal crystals of repulsive spheres. I find that certain geometries of active interstitials are capable of efficiently searching through a crystal and binding strongly to a stacking fault. They rapidly encounter the stacking faults that link partial dislocations in the FCC crystal, and when absorbed provide an additional barrier to dislocation glide. The presence of such optimized active, stacking-fault seeking interstitials can be detected in the shear deformation properties of dislocated crystals even at concentrations as small as 64 per million host particles. Fourth, I explore how a crystalline colloidal robot could be reconfigured using shear displacements resulting from the biased migration of dislocations. I propose a means of creating and controlling the migration of dislocations in 2D colloidal crystals based on embedded clusters of particles capable of changing size. I show that for clusters of particular geometries, cyclic expansion and contraction of their constituent particles produce dislocations that accumulate slip. Single or multiple slip planes can be used to reshape the boundaries of a 2D colloidal crystallite.PHDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153451/1/bvansade_1.pd

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma

Machine learning for quantum and complex systems

Machine learning now plays a pivotal role in our society, providing solutions to problems that were previously thought intractable. The meteoric rise of this technology can no doubt be attributed to the information age that we now live in. As data is continually amassed, more efficient and scalable methods are required to yield functional models and accurate inferences. Simultaneously we have also seen quantum technology come to the forefront of research and next generation systems. These technologies promise secure information transfer, efficient computation and high precision sensing, at levels unattainable by their classical counterparts. Although these technologies are powerful, they are necessarily more complicated and difficult to control. The combination of these two advances yields an opportunity for study, namely leveraging the power of machine learning to control and optimise quantum (and more generally complex) systems. The work presented in thesis explores these avenues of investigation and demonstrates the potential success of machine learning methods in the domain of quantum and complex systems. One of the most crucial potential quantum technologies is the quantum memory. If we are to one day harness the true power of quantum key distribution for secure transimission of information, and more general quantum computating tasks, it will almost certainly involve the use of quantum memorys. We start by presenting the operation of the cold atom workhorse: the magneto-optical trap (MOT). To use a cold atomic ensemble as a quantum memory we are required to prepare the atoms using a specialised cooling sequence. During this we observe a stable, coherent optical emission exiting each end of the elongated ensemble. We characterise this behaviour and compare it to similar observations in previous work. Following this, we use the ensemble to implement a backward Raman memory. Using this scheme we are able to demonstrate an increased efficiency over that of previous forward recall implementations. While we are limited by the optical depth of the system, we observe an efficiency more than double that of previous implementations. The MOT provides an easily accessible test bed for the optimisation via some machine learning technique. As we require an efficient search method, we implement a new type of algorithm based on deep learning. We design this technique such that the artificial neural networks are placed in control of the online optimisation, rather than simply being used as surrogate models. We experimentally optimise the optical depth of the MOT using this method, by parametrising the time varying compression sequence. We identify a new and unintuitive method for cooling the atomic ensemble which surpasses current methods. Following this initial implementation we make substantial improvements to the deep learning approach. This extends the approach to be applicable to a far wider range of complex problems, which may contain extensive local minima and structure. We benchmark this algorithm against many of the conventional optimisation techniques and demonstrate superior capability to optimise problems with high dimensionality. Finally we apply this technique to a series of preliminary problems, namely the tuning of a single electron transistor and second-order correlations from a quantum dot source

Architectures for photon-mediated quantum information processing

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 173-186).In this thesis, I present architectures for quantum information processing where photons are used as the quantum bit (qubit) or for mediating entanglement between other qubits. The emphasis of this research is to simplify the basic building blocks required in such processors. The all-photonic repeater and computing architectures do not require material nonlinearities, and their resource requirements are reduced by several orders of magnitude. The photon-mediated atomic memory architecture is designed to work with faulty memories and experimentally demonstrated values of coherence time and photonic coupling efficiency. In the quantum network architecture, the only operation at every node is probabilistic Bell measurement.by Mihir Pant.Ph. D

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

The TOTEM Experiment at the CERN Large Hadron Collider

The TOTEM Experiment will measure the total pp cross-section with the luminosity independent method and study elastic and diffractive scattering at the LHC. To achieve optimum forward coverage for charged particles emitted by the pp collisions in the interaction point IP5, two tracking telescopes, T1 and T2, will be installed on each side in the pseudorapidity region 3,1 <h< 6,5, and Roman Pot stations will be placed at distances of 147m and 220m from IP5. Being an independent experiment but technically integrated into CMS, TOTEM will first operate in standalone mode to pursue its own physics programme and at a later stage together with CMS for a common physics programme. This article gives a description of the TOTEM apparatus and its performance

Study of continuous-phase four-state modulation for cordless telecommunications. Assessment by simulation of CP-QFSK as an alternative modulation scheme for TDMA digital cordless telecommunications systems operating in indoor applications

One of the major driving elements behind the explosive boom in wireless revolution is the advances in the field of modulation which plays a fundamental role in any communication system, and especially in cellular radio systems. Hence, the elaborate choice of an efficient modulation scheme is of paramount importance in the design and employment of any communications system. Work presented in this thesis is an investigation (study) of the feasibility of whether multilevel FSK modulation scheme would provide a viable alternative modem that can be employed in TDMA cordless communications systems. In the thesis the design and performance analysis of a non-coherent multi-level modem that offers a great deal of bandwidth efficiency and hardware simplicity is studied in detail. Simulation results demonstrate that 2RC pre-modulation filter pulse shaping with a modulation index of 0.3, and pre-detection filter normalized equivalent noise bandwidth of 1.5 are optimum system parameter values. Results reported in chapter 5 signify that an adjacent channel rejection factor of around 40 dB has been achieved at channel spacing of 1.5 times the symbol rate while the DECT system standards stipulated a much lower rejection limit criterion (25-30dB), implying that CP-QFSK modulation out-performs the conventional GMSK as it causes significantly less ACI, thus it is more spectrally efficient in a multi-channel system. However, measured system performance in terms of BER indicates that this system does not coexist well with other interferers as at delay spreads between 100ns to 200ns, which are commonly encountered in such indoor environment, a severe degradation in system performance apparently caused by multi-path fading has been noticed, and there exists a noise floor of about 40 dB, i.e. high irreducible error rate of less than 5.10-3. Implementing MRC diversity combiner and BCH codec has brought in a good gain.Higher Education Ministr