Positioning by multicell fingerprinting in urban NB-IoT networks

Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments

Understanding of sub-band gap absorption of femtosecond-laser sulfur hyperdoped silicon using synchrotron-based techniques

[[abstract]]The correlation between sub-band gap absorption and the chemical states and electronic and atomic structures of S-hyperdoped Si have been extensively studied, using synchrotron-based x-ray photoelectron spectroscopy (XPS), x-ray absorption near-edge spectroscopy (XANES), extended x-ray absorption fine structure (EXAFS), valence-band photoemission spectroscopy (VB-PES) and first-principles calculation. S 2p XPS spectra reveal that the S-hyperdoped Si with the greatest (~87%) sub-band gap absorption contains the highest concentration of S2− (monosulfide) species. Annealing S-hyperdoped Si reduces the sub-band gap absorptance and the concentration of S2− species, but significantly increases the concentration of larger S clusters [polysulfides (Sn2−, n > 2)]. The Si K-edge XANES spectra show that S hyperdoping in Si increases (decreased) the occupied (unoccupied) electronic density of states at/above the conduction-band-minimum. VB-PES spectra evidently reveal that the S-dopants not only form an impurity band deep within the band gap, giving rise to the sub-band gap absorption, but also cause the insulator-to-metal transition in S-hyperdoped Si samples. Based on the experimental results and the calculations by density functional theory, the chemical state of the S species and the formation of the S-dopant states in the band gap of Si are critical in determining the sub-band gap absorptance of hyperdoped Si samples.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]電子版[[countrycodes]]GB

Transmission of correlated Gaussian sources with opportunistic interference

Interfering transmissions of independent sources is shown to provide benefits at a cost of complex receiver design. In this paper, we study the optimum transmission of correlated Gaussian sources by allowing opportunistic interference in order to minimize the expected distortion. We assume a successive interference cancellation decoder at the destination and consider a Wyner-Ziv type setup to transmit the correlated sources along with interference. We show how interference can be utilized to improve the end-to-end distortion for correlated sources. © 2014 IEEE

Transmission of correlated Gaussian sources with opportunistic interference

Interfering transmissions of independent sources is shown to provide benefits at a cost of complex receiver design. In this paper, we study the optimum transmission of correlated Gaussian sources by allowing opportunistic interference in order to minimize the expected distortion. We assume a successive interference cancellation decoder at the destination and consider a Wyner-Ziv type setup to transmit the correlated sources along with interference. We show how interference can be utilized to improve the end-to-end distortion for correlated sources. © 2014 IEEE

Ion-beam induced oxidation of GaAs and AlGaAs

The oxidation of GaAs and AlxGa1−xAs targets by oxygen irradiation has been studied in detail. It was found that the oxidation process is characterized by the strong preferential oxidation of Al as compared to Ga, and of Ga as compared to As. This experimental observation, which has been accurately quantified by using x‐ray photoelectron spectroscopy, is connected to the different heats of formation of the corresponding oxides. The oxide grown by ion beam oxidation shows a strong depletion in As and relatively low oxidation of As as well. The depletion can be associated with the preferential sputtering of the As oxide in respect to other compounds whereas the low oxidation is due to the low heat of formation. In contrast Al is rapidly and fully oxidized, turning the outermost layer of the altered layer to a single Al2O3 overlayer, as observed by transmission electron microscopy. The radiation enhanced diffusion of oxygen and aluminum in the altered layer explains the large thickness of these altered layers and the formation of Al oxides on top of the layers. For the case of ion‐beam oxidation of GaAs a simulation program has been developed which describes adequately the various growth mechanisms experimentally observe

Lossy transmission of correlated sources in a multiple access quasi-static fading channel

In this paper, we investigate low-complexity, receiver-driven strategies for the transmission of two correlated sources in a multiple access quasi-static fading channel. We consider two different transmission modes at the physical (PHY) layer depending on the average signal to noise ratio (SNR) of the involved Rayleigh fading channels. The two modes that are allowed are orthogonal transmissions and interfering transmissions with successive interference cancellation (SIC) decoding. Further, we consider the sources are compressed independently while they are jointly decoded with an adaptive linear minimum mean square error (MMSE) algorithm. Hence, the receiver selects the combination of a PHY transmission mode that should be used at the sources, together with the adaptive MMSE-based estimation of each source. We show that in different SNR regimes, a different transmission strategy is optimum, that is in the low SNR regime interfering transmission together with MMSE decoding is better, while in the high SNR regime orthogonal transmission is superior. Both schemes outperform distributed source coding (DSC) based approaches regardless of the degree of the correlation of the two sources. © 2016 IEEE

Cooperative live video multicast for small cell base stations with overlapping coverage

In this paper, we present a cooperative video multicast protocol for densely deployed small cells. We assume that the small cell Base Stations (BS) have overlapping coverages and they are allowed to have interfering transmissions. The proposed cooperative multicast protocol realizes a two-hop transmission system where each hop has its own time slot. In the first hop, the small cell base stations simultaneously transmit the packets to the relays who are also the users of multicast transmission. The relays then follow the optimal strategy under interference conditions and apply successive interference cancellation (SIC) decoding. In the second hop, the relays apply a Distributed Space Time Code (DSTC) that dynamically adapts itself based on the result of SIC and broadcast the result. This cooperative transmission scheme is complemented with application layer Forward Error Correction (FEC) in order to handle the remaining packet level losses. Through extensive simulations, we investigate the performance of the proposed scheme and show that the proposed protocol can efficiently handle inter-user interference, leading to superior performance over the state-of-The-Art purely orthogonal cooperative transmission schemes. © 2016 IEEE

Preventing Medication Errors Using Lean and Six Sigma

Medical errors are the third leading cause of deaths in the United States. One category of medical errors is medication errors, which are failures in the medication delivery process that results in or has the potential to lead to harm to patient. Medication errors are associated with significant financial and medical consequences, thus receiving attention of healthcare managers. The cost of medication errors to the U.S. healthcare industry is $42 billion annually. Medication errors could happen at any stage of the process including prescribing, transcribing, dispensing, administering and monitoring. Lean and Six Sigma are two proven process improvement methodologies which have been adopted by the healthcare industry. Their application areas in healthcare are increasing continuously. Literature provides examples of Lean and Six Sigma implementation to reduce medication errors, which consist of mostly single case reports and literature review articles. These studies support that Lean and Six Sigma are applicable in reducing medication errors. However, an understanding of the nature of the medication errors and any contributing factors to guide Lean and Six Sigma efforts can further increase the effectiveness of their implementation. This paper presents a study of Lean and Six Sigma implementation based on the classification of medication errors, whether errors occur in planning of an action or in its execution

Estimating downlink throughput from end-user measurements in mobile broadband networks

In recent years, Downlink (DL)throughput estimation in Mobile Broadband (MBB)networks has gained immense popularity and it is expected to become a vital component of the upcoming fifth generation (5G)systems. Plentiful adaptive video streaming algorithms greatly rely on accurate DL throughput predictions to adapt their mechanisms and ensure high Quality of Service (QoS)to the end-users. Thus far, conventional DL throughput estimation approaches, also known as speed tests, require an extensive exchange of TCP traffic over the network for an allocated time duration. While such tools appear to deliver trustworthy results, they turn out to be inefficient when mobile subscriptions with limited data plans are engaged. In this paper, we propose a supervised Machine Learning (ML)solution for DL throughput estimation that aims at delivering highly accurate predictions while significantly limiting the over-the-air data consumption. We capture the network performance metrics by exploring both crowdsourced and controlled testing methodologies. We leverage RTR-NetTest, a platform of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), and MONROE-NetTest, its counterpart wrapper built as an Experiment as a Service (EaaS)on top of Measuring Mobile Broadband Networks in Europe (MONROE). Results reveal that our solution can achieve a 39.7% reduction in terms of data consumption while delivering a Median Absolute Percentage Error (MdAPE)of 5.55%. We further show that accuracy can be traded-off, for example, a significant data consumption reduction of 95.15 % can be achieved for a MdAPE of 20%. © 2019 IEEE

The same, only different: Contrasting mobile operator behavior from crowdsourced dataset

Crowdsourcing mobile network performance evaluation is rapidly gaining popularity, with new applications aiming to deliver more accurate and reliable results every day. From the perspective of end-users, these utilities help them estimate the performance of their service provider in terms of throughput, latency and other key performance indicators of the network. In this paper, we build ORCA: Operator Classifier, a Machine Learning (ML) based framework to define and determine the behavior of Mobile Network Operators (MNOs) from crowdsourced datasets. We investigate whether one can differentiate MNOs by using crowdsourced end-to-end network measurements. We consider different performance metrics (e.g. Download (DL)/Upload (UL) data rate, latency, signal strength) and study the impact of them individually but also collectively on differentiating MNOs. We use RTR Open Data, an open dataset of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), to characterize the three major mobile native operators and two virtual operators in Austria. Our results show that ORCA can be used to identify patterns between various mobile systems and disclose their differences from the end-user perspective. © 2017 IEEE