Modeling and Analysis of MPTCP Proxy-based LTE-WLAN Path Aggregation

Long Term Evolution (LTE)-Wireless Local Area Network (WLAN) Path Aggregation (LWPA) based on Multi-path Transmission Control Protocol (MPTCP) has been under standardization procedure as a promising and cost-efficient solution to boost Downlink (DL) data rate and handle the rapidly increasing data traffic. This paper aims at providing tractable analysis for the DL performance evaluation of large-scale LWPA networks with the help of tools from stochastic geometry. We consider a simple yet practical model to determine under which conditions a native WLAN Access Point (AP) will work under LWPA mode to help increasing the received data rate. Using stochastic spatial models for the distribution of WLAN APs and LTE Base Stations (BSs), we analyze the density of active LWPA-mode WiFi APs in the considered network model, which further leads to closed-form expressions on the DL data rate and area spectral efficiency (ASE) improvement. Our numerical results illustrate the impact of different network parameters on the performance of LWPA networks, which can be useful for further performance optimization.Comment: IEEE GLOBECOM 201

Advanced Technologies Enabling Unlicensed Spectrum Utilization in Cellular Networks

As the rapid progress and pleasant experience of Internet-based services, there is an increasing demand for high data rate in wireless communications systems. Unlicensed spectrum utilization in Long Term Evolution (LTE) networks is a promising technique to meet the massive traffic demand. There are two effective methods to use unlicensed bands for delivering LTE traffic. One is offloading LTE traffic toWi-Fi. An alternative method is LTE-unlicensed (LTE-U), which aims to directly use LTE protocols and infrastructures over the unlicensed spectrum. It has also been pointed out that addressing the above two methods simultaneously could further improve the system performance. However, how to avoid severe performance degradation of the Wi-Fi network is a challenging issue of utilizing unlicensed spectrum in LTE networks. Specifically, first, the inter-system spectrum sharing, or, more specifically, the coexistence of LTE andWi-Fi in the same unlicensed spectrum is the major challenge of implementing LTE-U. Second, to use the LTE and Wi-Fi integration approach, mobile operators have to manage two disparate networks in licensed and unlicensed spectrum. Third, optimization for joint data offloading to Wi-Fi and LTE-U in multi- cell scenarios poses more challenges because inter-cell interference must be addressed. This thesis focuses on solving problems related to these challenges. First, the effect of bursty traffic in an LTE and Wi-Fi aggregation (LWA)-enabled network has been investigated. To enhance resource efficiency, the Wi-Fi access point (AP) is designed to operate in both the native mode and the LWA mode simultaneously. Specifically, the LWA-modeWi-Fi AP cooperates with the LTE base station (BS) to transmit bearers to the LWA user, which aggregates packets from both LTE and Wi-Fi. The native-mode Wi-Fi AP transmits Wi-Fi packets to those native Wi-Fi users that are not with LWA capability. This thesis proposes a priority-based Wi-Fi transmission scheme with congestion control and studied the throughput of the native Wi-Fi network, as well as the LWA user delay when the native Wi-Fi user is under heavy traffic conditions. The results provide fundamental insights in the throughput and delay behavior of the considered network. Second, the above work has been extended to larger topologies. A stochastic geometry model has been used to model and analyze the performance of an MPTCP Proxy-based LWA network with intra-tier and cross-tier dependence. Under the considered network model and the activation conditions of LWA-mode Wi-Fi, this thesis has obtained three approximations for the density of active LWA-mode Wi-Fi APs through different approaches. Tractable analysis is provided for the downlink (DL) performance evaluation of large-scale LWA networks. The impact of different parameters on the network performance have been analyzed, validating the significant gain of using LWA in terms of boosted data rate and improved spectrum reuse. Third, this thesis also takes a significant step of analyzing joint multi-cell LTE-U and Wi-Fi network, while taking into account different LTE-U and Wi-Fi inter-working schemes. In particular, two technologies enabling data offloading from LTE to Wi-Fi are considered, including LWA and Wi-Fi offloading in the context of the power gain-based user offloading scheme. The LTE cells in this work are subject to load-coupling due to inter-cell interference. New system frameworks for maximizing the demand scaling factor for all users in both Wi-Fi and multi-cell LTE networks have been proposed. The potential of networks is explored in achieving optimal capacity with arbitrary topologies, accounting for both resource limits and inter-cell interference. Theoretical analyses have been proposed for the proposed optimization problems, resulting in algorithms that achieve global optimality. Numerical results show the algorithms’ effectiveness and benefits of joint use of data offloading and the direct use of LTE over the unlicensed band. All the derived results in this thesis have been validated by Monte Carlo simulations in Matlab, and the conclusions observed from the results can provide guidelines for the future unlicensed spectrum utilization in LTE networks

Identifying protein complexes and disease genes from biomolecular networks

With advances in high-throughput measurement techniques, large-scale biological data, such as protein-protein interaction (PPI) data, gene expression data, gene-disease association data, cellular pathway data, and so on, have been and will continue to be produced. Those data contain insightful information for understanding the mechanisms of biological systems and have been proved useful for developing new methods in disease diagnosis, disease treatment and drug design. This study focuses on two main research topics: (1) identifying protein complexes and (2) identifying disease genes from biomolecular networks. Firstly, protein complexes are groups of proteins that interact with each other at the same time and place within living cells. They are molecular entities that carry out cellular processes. The identification of protein complexes plays a primary role for understanding the organization of proteins and the mechanisms of biological systems. Many previous algorithms are designed based on the assumption that protein complexes are densely connected sub-graphs in PPI networks. In this research, a dense sub-graph detection algorithm is first developed following this assumption by using clique seeds and graph entropy. Although the proposed algorithm generates a large number of reasonable predictions and its f-score is better than many previous algorithms, it still cannot identify many known protein complexes. After that, we analyze characteristics of known yeast protein complexes and find that not all of the complexes exhibit dense structures in PPI networks. Many of them have a star-like structure, which is a very special case of the core-attachment structure and it cannot be identified by many previous core-attachment-structure-based algorithms. To increase the prediction accuracy of protein complex identification, a multiple-topological-structure-based algorithm is proposed to identify protein complexes from PPI networks. Four single-topological-structure-based algorithms are first employed to detect raw predictions with clique, dense, core-attachment and star-like structures, respectively. A merging and trimming step is then adopted to generate final predictions based on topological information or GO annotations of predictions. A comprehensive review about the identification of protein complexes from static PPI networks to dynamic PPI networks is also given in this study. Secondly, genetic diseases often involve the dysfunction of multiple genes. Various types of evidence have shown that similar disease genes tend to lie close to one another in various biomolecular networks. The identification of disease genes via multiple data integration is indispensable towards the understanding of the genetic mechanisms of many genetic diseases. However, the number of known disease genes related to similar genetic diseases is often small. It is not easy to capture the intricate gene-disease associations from such a small number of known samples. Moreover, different kinds of biological data are heterogeneous and no widely acceptable criterion is available to standardize them to the same scale. In this study, a flexible and reliable multiple data integration algorithm is first proposed to identify disease genes based on the theory of Markov random fields (MRF) and the method of Bayesian analysis. A novel global-characteristic-based parameter estimation method and an improved Gibbs sampling strategy are introduced, such that the proposed algorithm has the capability to tune parameters of different data sources automatically. However, the Markovianity characteristic of the proposed algorithm means it only considers information of direct neighbors to formulate the relationship among genes, ignoring the contribution of indirect neighbors in biomolecular networks. To overcome this drawback, a kernel-based MRF algorithm is further proposed to take advantage of the global characteristics of biological data via graph kernels. The kernel-based MRF algorithm generates predictions better than many previous disease gene identification algorithms in terms of the area under the receiver operating characteristic curve (AUC score). However, it is very time-consuming, since the Gibbs sampling process of the algorithm has to maintain a long Markov chain for every single gene. Finally, to reduce the computational time of the MRF-based algorithm, a fast and high performance logistic-regression-based algorithm is developed for identifying disease genes from biomolecular networks. Numerical experiments show that the proposed algorithm outperforms many existing methods in terms of the AUC score and running time. To summarize, this study has developed several computational algorithms for identifying protein complexes and disease genes from biomolecular networks, respectively. These proposed algorithms are better than many other existing algorithms in the literature

Ab initio study of electron-phonon interaction in phosphorene

The monolayer of black phosphorous, or phosphorene, has recently emerged as a new 2D semiconductor with intriguing highly anisotropic transport properties. Existing calculations of its intrinsic phonon-limited electronic transport properties so far rely on the deformation potential approximation, which is in general not directly applicable to anisotropic materials since the deformation along one specific direction can scatter electrons traveling in all directions. We perform a first-principles calculation of the electron-phonon interaction in phosphorene based on density functional perturbation theory and Wannier interpolation. Our calculation reveals that 1) the high anisotropy provides extra phase space for electron-phonon scattering, and 2) optical phonons have appreciable contributions. Both effects cannot be captured by the deformation potential calculations.Comment: 25 pages, 15 figure

Significant reduction of lattice thermal conductivity by electron-phonon interaction in silicon with high carrier concentrations: a first-principles study

Electron-phonon interaction has been well known to create major resistance to electron transport in metals and semiconductors, whereas less studies were directed to its effect on the phonon transport, especially in semiconductors. We calculate the phonon lifetimes due to scattering with electrons (or holes), combine them with the intrinsic lifetimes due to the anharmonic phonon-phonon interaction, all from first-principles, and evaluate the effect of the electron-phonon interaction on the lattice thermal conductivity of silicon. Unexpectedly, we find a significant reduction of the lattice thermal conductivity at room temperature as the carrier concentration goes above 1e19 cm-3 (the reduction reaches up to 45% in p-type silicon at around 1e21 cm-3), a range of great technological relevance to thermoelectric materials.Comment: 19 pages, 5 figure

Limiting efficiencies of solar energy conversion and photo-detection via internal emission of hot electrons and hot holes in gold

We evaluate the limiting efficiency of full and partial solar spectrum harvesting via the process of internal photoemission in Au-semiconductor Schottky junctions. Our results based on the ab initio calculations of the electron density of states (e-DOS) reveal that the limiting efficiency of the full-spectrum Au converter based on hot electron injection is below 4%. This value is even lower than previously established limit based on the parabolic approximation of the Au electron energy bands. However, we predict limiting efficiency exceeding 10% for the hot holes collection through the Schottky junction between Au and p-type semiconductor. Furthermore, we demonstrate that such converters have more potential if used as a part of the hybrid system for harvesting high- and low-energy photons of the solar spectrum.Comment: Proc. SPIE 9608, Infrared Remote Sensing and Instrumentation XXIII, 960816 (September 1, 2015) 7 pages, 4 figure

First-principles calculations of thermal, electrical, and thermoelectric transport properties of semiconductors

The transport properties of semiconductors are key to the performance of many solid-state devices (transistors, data storage, thermoelectric cooling and power generation devices, etc). An understanding of the transport details can lead to material designs with better performances. In recent years simulation tools based on first-principles calculations have been greatly improved, being able to obtain the fundamental ground-state properties of materials (such as band structure and phonon dispersion) accurately. Accordingly, methods have been developed to calculate the transport properties based on an ab initio approach. In this review we focus on the thermal, electrical, and thermoelectric transport properties of semiconductors, which represent the basic transport characteristics of the two degrees of freedom in solids—electronic and lattice degrees of freedom. Starting from the coupled electron-phonon Boltzmann transport equations, we illustrate different scattering mechanisms that change the transport features and review the first-principles approaches that solve the transport equations. We then present the first-principles results on the thermal and electrical transport properties of semiconductors. The discussions are grouped based on different scattering mechanisms including phonon-phonon scattering, phonon scattering by equilibrium electrons, carrier scattering by equilibrium phonons, carrier scattering by polar optical phonons, scatterings due to impurities, alloying and doping, and the phonon drag effect. We show how the first-principles methods allow one to investigate transport properties with unprecedented detail and also offer new insights into the electron and phonon transport. The current status of the simulation is mentioned when appropriate and some of the future directions are also discussed

Generalized two-temperature model for coupled phonon-magnon diffusion

We generalize the two-temperature model [Sanders and Walton, Phys. Rev. B, 15, 1489 (1977)] for coupled phonon-magnon diffusion to include the effect of the concurrent magnetization flow. Working within the framework of Boltzmann transport equation, we derive the constitutive equations for coupled phonon-magnon transport driven by gradients of both temperature and external magnetic fields, and the corresponding conservation laws. Our equations reduce to the original Sanders-Walton two-temperature model under a uniform external field, but predict a new magnon cooling effect driven by a non-uniform magnetic field in a homogeneous single-domain ferromagnet. We estimate the magnitude of the cooling effect in yttrium iron garnet, and show it is within current experimental reach. With properly optimized materials, the predicted cooling effect can potentially supplement the conventional magnetocaloric effect in cryogenic applications in the future.Comment: 17 pages, 6 figure

Significant Phonon Drag Effect in Wide Bandgap GaN and AlN

A thorough understanding of electrical and thermal transport properties of group-III nitride semiconductors is essential for their electronic and thermoelectric applications. Despite extensive previous studies, these transport properties were typically calculated without considering the nonequilibrium coupling effect between electrons and phonons, which can be particularly strong in group-III nitride semiconductors due to the high electric fields and high heat currents in devices based on them. In this work, we systematically examine the phonon drag effect, namely the momentum exchange between nonequilibrium phonons and electrons, and its impact on charge mobility and Seebeck coefficient in GaN and AlN by solving the fully coupled electron and phonon Boltzmann transport equations with ab initio scattering parameters. We find that, even at room temperature, the phonon drag effect can significantly enhance mobility and Seebeck coefficient in GaN and AlN, especially at higher carrier concentrations. Furthermore, we show that the phonon drag contribution to mobility and Seebeck coefficient scale differently with the carrier concentration and we highlight a surprisingly important contribution to the mobility enhancement from the polar optical phonons. We attribute both findings to the distinct mechanisms the phonon drag affects mobility and Seebeck coefficient. Our study advances the understanding of the strong phonon drag effect on carrier transport in wide bandgap GaN and AlN and gives new insights into the nature of coupled electron-phonon transport in polar semiconductors

Additive manufacturing of flexible energy harvesting and storage device

Printing technology as an additive manufacturing method offers promising approach to deposit functional nanomaterials in a scalable fashion. Thermoelectric generators (TEGs) offer seemingly limitless, clean energy to power ever more increasing and complex wearable and pocket devices. The burgeoning field of wearable technologies, or body worn application- enabled computing devices, are capable of providing user feedback/alerts from multiple devices that continuously monitor metrics such as physical and muscular activity; cardiac and respiratory rates; as well as temperature, humidity, and light. The challenge of supplying continuous power in a non-invasive fashion to ever more complex and numerous wearable devices is a key prohibitive bottleneck to commercialization. A manufacturing process involved additive manufacturing is studied to highly-efficient thermoelectric generators to power wearable devices via body heat. Such wearable power generation would be of interest to a myriad of applications including those associated with particular fields of work/leisure efficiency (e.g., computing and communication devices that connect to the internet and supply requested information), biomedical monitoring (e.g., wearable sensors that monitor respiratory, muscular, and metabolism activity) and military applications (e.g., devices that alert the warfighter of nearby biochemical threats). On the other hand, supercapacitors have emerged as a promising energy storage device, due to their high power, long cycle life, and ability to bridge the energy and power gap between batteries and conventional dielectric capacitors. Supercapacitors are widely used in electronic systems where fast and frequent charging/discharging is required. Hybrid power sources integrating batteries and supercapacitors together provide both high energy and high power at the same time. Herein, we report for the use of DIW technology for printing fully packaged flexible supercapacitors