Showing papers by "Carleton University published in 2016"

Efficient 3-D placement of an aerial base station in next generation cellular networks

Abstract: Agility and resilience requirements of future cellular networks may not be fully satisfied by terrestrial base stations in cases of unexpected or temporary events. A promising solution is assisting the cellular network via low-altitude unmanned aerial vehicles equipped with base stations, i.e., drone-cells. Although drone-cells provide a quick deployment opportunity as aerial base stations, efficient placement becomes one of the key issues. In addition to mobility of the drone-cells in the vertical dimension as well as the horizontal dimension, the differences between the air-to-ground and terrestrial channels cause the placement of the drone-cells to diverge from placement of terrestrial base stations. In this paper, we first highlight the properties of the drone-cell placement problem, and formulate it as a 3-D placement problem with the objective of maximizing the revenue of the network. After some mathematical manipulations, we formulate an equivalent quadratically-constrained mixed integer non-linear optimization problem and propose a computationally efficient numerical solution for this problem. We verify our analytical derivations with numerical simulations and enrich them with discussions which could serve as guidelines for researchers, mobile network operators, and policy makers.

An Indigenous Feminist's Take On The Ontological Turn: ‘Ontology’ Is Just Another Word For Colonialism

Abstract: In this article, I ask how anthropology can adopt a decolonial approach that incorporates and acknowledges the critical scholarship of Indigenous thinkers whose work and labour informs many current trends in Euro-Western scholarship, activism and socio-political discourse. I also query how to address ongoing structural colonialism within the academy in order to ensure that marginalised voices are heard within academic discourses.

Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges

Abstract: Distributed denial of service (DDoS) attacks in cloud computing environments are growing due to the essential characteristics of cloud computing. With recent advances in software-defined networking (SDN), SDN-based cloud brings us new chances to defeat DDoS attacks in cloud computing environments. Nevertheless, there is a contradictory relationship between SDN and DDoS attacks. On one hand, the capabilities of SDN, including software-based traffic analysis, centralized control, global view of the network, dynamic updating of forwarding rules, make it easier to detect and react to DDoS attacks. On the other hand, the security of SDN itself remains to be addressed, and potential DDoS vulnerabilities exist across SDN platforms. In this paper, we discuss the new trends and characteristics of DDoS attacks in cloud computing, and provide a comprehensive survey of defense mechanisms against DDoS attacks using SDN. In addition, we review the studies about launching DDoS attacks on SDN, as well as the methods against DDoS attacks in SDN. To the best of our knowledge, the contradictory relationship between SDN and DDoS attacks has not been well addressed in previous works. This work can help to understand how to make full use of SDN's advantages to defeat DDoS attacks in cloud computing environments and how to prevent SDN itself from becoming a victim of DDoS attacks, which are important for the smooth evolution of SDN-based cloud without the distraction of DDoS attacks.

The New Frontier in RAN Heterogeneity: Multi-Tier Drone-Cells

Abstract: In cellular networks, the locations of the RAN elements are determined mainly based on the long-term traffic behavior. However, when the random and hard-to-predict spatio-temporal distribution of the traffic (load, demand) does not fully match the fixed locations of the RAN elements (supply), some performance degradation becomes inevitable. The concept of multi-tier cells (heterogeneous networks, HetNets) has been introduced in 4G networks to alleviate this mismatch. However, as the traffic distribution deviates more and more from the long-term average, even the HetNet architecture will have difficulty in coping with the erratic supply-demand mismatch, unless the RAN is grossly over-engineered (which is a financially non-viable solution). In this article, we study the opportunistic utilization of low-altitude unmanned aerial platforms equipped with BSs (i.e., drone-BSs) in future wireless networks. In particular, we envisage a multi-tier drone-cell network complementing the terrestrial HetNets. The variety of equipment and non-rigid placement options allow utilizing multi-tier drone-cell networks to serve diversified demands. Hence, drone-cells bring the supply to where the demand is, which sets new frontiers for the heterogeneity in 5G networks. We investigate the advancements promised by dronecells and discuss the challenges associated with their operation and management. We propose a drone-cell management framework (DMF) benefiting from the synergy among SDN, network functions virtualization, and cloud computing. We demonstrate DMF mechanisms via a case study, and numerically show that it can reduce the cost of utilizing drone-cells in multi-tenancy cellular networks.

Muon reconstruction performance of the ATLAS detector in proton–proton collision data at √ s =13 TeV

Abstract: This article documents the performance of the ATLAS muon identification and reconstruction using the first LHC dataset recorded at s√ = 13 TeV in 2015. Using a large sample of J/ψ→μμ and Z→μμ decays from 3.2 fb−1 of pp collision data, measurements of the reconstruction efficiency, as well as of the momentum scale and resolution, are presented and compared to Monte Carlo simulations. The reconstruction efficiency is measured to be close to 99% over most of the covered phase space (|η| 2.2, the pT resolution for muons from Z→μμ decays is 2.9% while the precision of the momentum scale for low-pT muons from J/ψ→μμ decays is about 0.2%.

On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks

Abstract: Using drone base stations (drone-BSs) in wireless networks has started attracting attention. Drone-BSs can assist the ground BSs in both capacity and coverage enhancement. One of the important problems about integrating drone-BSs to cellular networks is the management of their placement to satisfy the dynamic system requirements. In this paper, we propose a method to find the positions of drone-BSs in an area with different user densities using a heuristic algorithm. The goal is to find the minimum number of drone-BSs and their 3D placement so that all the users are served. Our simulation results show that the proposed approach can satisfy the quality-of-service requirements of the network.

A moving target—incorporating knowledge of the spatial ecology of fish into the assessment and management of freshwater fish populations

Abstract: Freshwater fish move vertically and horizontally through the aquatic landscape for a variety of reasons, such as to find and exploit patchy resources or to locate essential habitats (e.g., for spawning). Inherent challenges exist with the assessment of fish populations because they are moving targets. We submit that quantifying and describing the spatial ecology of fish and their habitat is an important component of freshwater fishery assessment and management. With a growing number of tools available for studying the spatial ecology of fishes (e.g., telemetry, population genetics, hydroacoustics, otolith microchemistry, stable isotope analysis), new knowledge can now be generated and incorporated into biological assessment and fishery management. For example, knowing when, where, and how to deploy assessment gears is essential to inform, refine, or calibrate assessment protocols. Such information is also useful for quantifying or avoiding bycatch of imperiled species. Knowledge of habitat connectivity and usage can identify critically important migration corridors and habitats and can be used to improve our understanding of variables that influence spatial structuring of fish populations. Similarly, demographic processes are partly driven by the behavior of fish and mediated by environmental drivers. Information on these processes is critical to the development and application of realistic population dynamics models. Collectively, biological assessment, when informed by knowledge of spatial ecology, can provide managers with the ability to understand how and when fish and their habitats may be exposed to different threats. Naturally, this knowledge helps to better evaluate or develop strategies to protect the long-term viability of fishery production. Failure to understand the spatial ecology of fishes and to incorporate spatiotemporal data can bias population assessments and forecasts and potentially lead to ineffective or counterproductive management actions.

Performance of $b$-Jet Identification in the ATLAS Experiment

Abstract: The identification of jets containing b hadrons is important for the physics programme of the ATLAS experiment at the Large Hadron Collider. Several algorithms to identify jets containing b hadrons are described, ranging from those based on the reconstruction of an inclusive secondary vertex or the presence of tracks with large impact parameters to combined tagging algorithms making use of multi-variate discriminants. An independent b-tagging algorithm based on the reconstruction of muons inside jets as well as the b-tagging algorithm used in the online trigger are also presented. The b-jet tagging efficiency, the c-jet tagging efficiency and the mistag rate for light flavour jets in data have been measured with a number of complementary methods. The calibration results are presented as scale factors defined as the ratio of the efficiency (or mistag rate) in data to that in simulation. In the case of b jets, where more than one calibration method exists, the results from the various analyses have been combined taking into account the statistical correlation as well as the correlation of the sources of systematic uncertainty.

Performance of pile-up mitigation techniques for jets in pp collisions at √s = 8 TeV using the ATLAS detector

Abstract: The large rate of multiple simultaneous protonproton interactions, or pile-up, generated by the Large Hadron Collider in Run 1 required the development of many new techniques to mitigate the advers ...

Luminosity determination in pp collisions at √s = 8 TeV using the ATLAS detector at the LHC

Abstract: The luminosity determination for the ATLAS detector at the LHC during pp collisions at s√= 8 TeV in 2012 is presented. The evaluation of the luminosity scale is performed using several luminometers ...

When big data meets software-defined networking: SDN for big data and big data for SDN

Abstract: Both big data and software-defined networking (SDN) have attracted great interests from both academia and industry. These two important areas have traditionally been addressed separately in the most of previous works. However, on the one hand, the good features of SDN can greatly facilitate big data acquisition, transmission, storage, and processing. On the other hand, big data will have profound impacts on the design and operation of SDN. In this paper, we present the good features of SDN in solving several issues prevailing with big data applications, including big data processing in cloud data centers, data delivery, joint optimization, scientific big data architectures and scheduling issues. We show that SDN can manage the network efficiently for improving the performance of big data applications. In addition, we show that big data can benefit SDN as well, including traffic engineering, cross-layer design, defeating security attacks, and SDN-based intra and inter data center networks. Moreover, we discuss a number of open issues that need to be addressed to jointly consider big data and SDN in future research.

The social, economic, and environmental importance of inland fish and fisheries

Abstract: Though reported capture fisheries are dominated by marine production, inland fish and fisheries make substantial contributions to meeting the challenges faced by individuals, society, and the envir...

Small-Molecule Binding Aptamers: Selection Strategies, Characterization, and Applications

Abstract: Aptamers are single-stranded, synthetic oligonucleotides that fold into 3-dimensional shapes capable of binding non-covalently with high affinity and specificity to a target molecule. They are generated via an in vitro process known as the Systematic Evolution of Ligands by EXponential enrichment, from which candidates are screened and characterized, and then used in various applications. These applications range from therapeutic uses to biosensors for target detection. Aptamers for small molecule targets such as toxins, antibiotics, molecular markers, drugs, and heavy metals will be the focus of this review. Their accurate detection is needed for the protection and wellbeing of humans and animals. However, the small molecular weights of these targets, including the drastic size difference between the target and the oligonucleotides, make it challenging to select, characterize, and apply aptamers for their detection. Thus, recent (since 2012) notable advances in small molecule aptamers, which have overcome some of these challenges, are presented here, while defining challenges that still exist are discussed.

Observation of Long-Range Elliptic Azimuthal Anisotropies in s =13 and 2.76 TeV pp Collisions with the ATLAS Detector

Abstract: ATLAS has measured two-particle correlations as a function of relative azimuthal-angle, $\Delta \phi$, and pseudorapidity, $\Delta \eta$, in $\sqrt{s}$=13 and 2.76 TeV $pp$ collisions at the LHC using charged particles measured in the pseudorapidity interval $|\eta|$<2.5. The correlation functions evaluated in different intervals of measured charged-particle multiplicity show a multiplicity-dependent enhancement at $\Delta \phi \sim 0$ that extends over a wide range of $\Delta\eta$, which has been referred to as the "ridge". Per-trigger-particle yields, $Y(\Delta \phi)$, are measured over 2<$|\Delta\eta|$<5. For both collision energies, the $Y(\Delta \phi)$ distribution in all multiplicity intervals is found to be consistent with a linear combination of the per-trigger-particle yields measured in collisions with less than 20 reconstructed tracks, and a constant combinatoric contribution modulated by $\cos{(2\Delta \phi)}$. The fitted Fourier coefficient, $v_{2,2}$, exhibits factorization, suggesting that the ridge results from per-event $\cos{(2\phi)}$ modulation of the single-particle distribution with Fourier coefficients $v_2$. The $v_2$ values are presented as a function of multiplicity and transverse momentum. They are found to be approximately constant as a function of multiplicity and to have a $p_{\mathrm{T}}$ dependence similar to that measured in $p$+Pb and Pb+Pb collisions. The $v_2$ values in the 13 and 2.76 TeV data are consistent within uncertainties. These results suggest that the ridge in $pp$ collisions arises from the same or similar underlying physics as observed in $p$+Pb collisions, and that the dynamics responsible for the ridge has no strong $\sqrt{s}$ dependence.

Search for an additional, heavy Higgs boson in the H → ZZ decay channel at √s = 8 TeV in pp collision data with the ATLAS detector

Abstract: A search is presented for a high-mass Higgs boson in the , , , and decay modes using the ATLAS detector at the CERN Large Hadron Collider. The search uses proton-proton collision data at a centre-of-mass energy of 8 TeV corresponding to an integrated luminosity of 20.3 fb. The results of the search are interpreted in the scenario of a heavy Higgs boson with a width that is small compared with the experimental mass resolution. The Higgs boson mass range considered extends up to for all four decay modes and down to as low as 140 , depending on the decay mode. No significant excess of events over the Standard Model prediction is found. A simultaneous fit to the four decay modes yields upper limits on the production cross-section of a heavy Higgs boson times the branching ratio to boson pairs. 95 % confidence level upper limits range from 0.53 pb at GeV to 0.008 pb at GeV for the gluon-fusion production mode and from 0.31 pb at GeV to 0.009 pb at GeV for the vector-boson-fusion production mode. The results are also interpreted in the context of Type-I and Type-II two-Higgs-doublet models.

Big Data in food and agriculture

Abstract: Farming is undergoing a digital revolution. Our existing review of current Big Data applications in the agri-food sector has revealed several collection and analytics tools that may have implications for relationships of power between players in the food system (e.g. between farmers and large corporations). For example, Who retains ownership of the data generated by applications like Monsanto Corproation's Weed I.D. “app”? Are there privacy implications with the data gathered by John Deere's precision agricultural equipment? Systematically tracing the digital revolution in agriculture, and charting the affordances as well as the limitations of Big Data applied to food and agriculture, should be a broad research goal for Big Data scholarship. Such a goal brings data scholarship into conversation with food studies and it allows for a focus on the material consequences of big data in society.

Nanotechnologies for increasing the crop use efficiency of fertilizer-micronutrients

Abstract: Billions of people and many soils across the planet suffer from micronutrient (MN) deficiencies impairing human health. In general, fertilization of deficient soils, according to soil test, with MNs alone and in combination with nitrogen, phosphorous, and potassium (NPK) baseline treatment increases crop yield. The soil applied fertilizer-MN use efficiency (MUE) by crops is <5 % due to a lack of synchronization between the fertilizer-MN release and their crop demand during growth. Nanotechnology and biotechnology have the potential to play a prominent place in transforming agricultural systems and food production worldwide in the coming years. MNs added in microcapsules and nanocapsules, nanomaterials (NMs), and nanoparticles (NPs) are taken up and translocated within plants when grown to maturity, increasing crop yield and MN concentration in plants. Noteworthy, many of the effects of NPs and NMs on crop yield and quality, human health, and associated environmental risks remain to be explored. Increasing MUE requires synchronizing the release of nutrients from fertilizers with crop demand during the growing season. Development of intelligent MN fertilizer delivery platforms (IMNDP) may be possible on the basis of elucidating communication signals between plant roots and soil microorganisms. Important benefits from the development and farm adoption of intelligent MN delivery platforms include increased MUE, reduced fertilizer use, and minimal toxicity and environmental impacts. This article proposes for the first time a novel model for IMNDP to enhance MUE and food quality by enabling the synchronization of MN release from fertilizers according to crop demand.

Long-lived connection between southern Siberia and northern Laurentia in the Proterozoic

Abstract: Precambrian supercontinents Nuna-Columbia (1.7 to 1.3 billion years ago) and Rodinia (1.1 to 0.7 billion years ago) have been proposed. However, the arrangements of crustal blocks within these supercontinents are poorly known. Huge, dominantly basaltic magmatic outpourings and intrusions, covering up to millions of square kilometres, termed Large Igneous Provinces, typically accompany (super) continent breakup, or attempted breakup and offer an important tool for reconstructing supercontinents. Here we focus on the Large Igneous Province record for Siberia and Laurentia, whose relative position in Nuna-Columbia and Rodinia reconstructions is highly controversial. We present precise geochronology—nine U–Pb and six Ar–Ar ages—on dolerite dykes and sills, along with existing dates from the literature, that constrain the timing of emplacement of Large Igneous Province magmatism in southern Siberia and northern Laurentia between 1,900 and 720 million years ago. We identify four robust age matches between the continents 1,870, 1,750, 1,350 and 720 million years ago, as well as several additional approximate age correlations that indicate southern Siberia and northern Laurentia were probably near neighbours for this 1.2-billion-year interval. Our reconstructions provide a framework for evaluating the shared geological, tectonic and metallogenic histories of these continental blocks. The configurations of the ancient supercontinents are poorly known. Analysis of the ages of giant magma intrusions that affected both Siberia and Laurentia shows that the two continents were connected, possibly for as long as 1.2 billion years.

Persevering with Positivity and Purpose: An Examination of Purpose Commitment and Positive Affect as Predictors of Grit

Abstract: Grit, defined as a passion and perseverance for one’s goals, has been consistently demonstrated as an adaptive resource across multiple domains. Less explored, however, are the correlates of and sources from which grit is derived. The current studies examined two plausible candidates for promoting grit, positive affect and commitment to a purpose, using college student samples from Canada and the United States. Study 1 confirmed our predictions that grittier students tended to report greater positive affect and purpose commitment, and demonstrated that these variables appear to be unique and independent predictors of grit. Study 2 examined these claims using two-wave data collected across a semester, and found that while both purpose and positive affect were initially correlated with grit, only initial levels of purpose predicted grit at wave two. In other words, having a life direction may help more than positive affect when predicting who is likely to become grittier over a college semester. Implications of these findings are discussed.

[INVITED] Tilted fiber grating mechanical and biochemical sensors ☆

Abstract: The tilted fiber Bragg grating (TFBG) is a new kind of fiber-optic sensor that possesses all the advantages of well-established Bragg grating technology in addition to being able to excite cladding modes resonantly. This device opens up a multitude of opportunities for single-point sensing in hard-to-reach spaces with very controllable cross-sensitivities, absolute and relative measurements of various parameters, and an extreme sensitivity to materials external to the fiber without requiring the fiber to be etched or tapered. Over the past five years, our research group has been developing multimodal fiber-optic sensors based on TFBG in various shapes and forms, always keeping the device itself simple to fabricate and compatible with low-cost manufacturing. This paper presents a brief review of the principle, fabrication, characterization, and implementation of TFBGs, followed by our progress in TFBG sensors for mechanical and biochemical applications, including one-dimensional TFBG vibroscopes, accelerometers and micro-displacement sensors; two-dimensional TFBG vector vibroscopes and vector rotation sensors; reflective TFBG refractometers with in-fiber and fiber-to-fiber configurations; polarimetric and plasmonic TFBG biochemical sensors for in-situ detection of cell, protein and glucose.

Functional Adult Outcomes 16 Years After Childhood Diagnosis of Attention-Deficit/Hyperactivity Disorder: MTA Results.

Abstract: Objective To compare educational, occupational, legal, emotional, substance use disorder, and sexual behavior outcomes in young adults with persistent and desistent attention-deficit/hyperactivity disorder (ADHD) symptoms and a local normative comparison group (LNCG) in the Multimodal Treatment Study of Children with ADHD (MTA). Method Data were collected 12, 14, and 16 years postbaseline (mean age 24.7 years at 16 years postbaseline) from 476 participants with ADHD diagnosed at age 7 to 9 years, and 241 age- and sex-matched classmates. Probands were subgrouped on persistence versus desistence of DSM-5 symptom count. Orthogonal comparisons contrasted ADHD versus LNCG and symptom-persistent (50%) versus symptom-desistent (50%) subgroups. Functional outcomes were measured with standardized and demographic instruments. Results Three patterns of functional outcomes emerged. Post−secondary education, times fired/quit a job, current income, receiving public assistance, and risky sexual behavior showed the most common pattern: the LNCG group fared best, symptom-persistent ADHD group worst, and symptom-desistent ADHD group between, with the largest effect sizes between LNCG and symptom-persistent ADHD. In the second pattern, seen with emotional outcomes (emotional lability, neuroticism, anxiety disorder, mood disorder) and substance use outcomes, the LNCG and symptom-desistent ADHD group did not differ, but both fared better than the symptom-persistent ADHD group. In the third pattern, noted with jail time (rare), alcohol use disorder (common), and number of jobs held, group differences were not significant. The ADHD group had 10 deaths compared to one death in the LNCG. Conclusion Adult functioning after childhood ADHD varies by domain and is generally worse when ADHD symptoms persist. It is important to identify factors and interventions that promote better functional outcomes.

Adaptive Power Allocation Schemes for Spectrum Sharing in Interference-Alignment-Based Cognitive Radio Networks

Abstract: Interference alignment (IA) is a promising technique for interference management and can be applied to spectrum sharing in cognitive radio (CR) networks. However, the sum rate may fall short of the theoretical maximum, particularly at low signal-to-noise ratio (SNR), and the quality of service (QoS) of the primary user (PU) may not be guaranteed. In addition, power allocation (PA) in IA-based CR networks is largely ignored, which can further improve its performance. Thus, in this paper, PA in IA-based CR networks is studied. To guarantee the QoS requirement of the PU, its minimal transmitted power is derived. Then, we propose three PA algorithms to maximize the throughput of secondary users (SUs), the energy efficiency (EE) of the network, and the requirements of SUs, respectively, while guaranteeing the QoS of the PU. To reduce the complexity, the closed-form solutions of these algorithms are further studied in detail. The outage probability of the PU according to its rate threshold is also derived to analyze the performance of these algorithms. Moreover, we propose a transmission-mode adaptation scheme to further improve the PU's performance when its QoS requirement cannot be guaranteed at low SNR, and it can be easily combined with the proposed PA algorithms. Simulation results are presented to show the effectiveness of the proposed adaptive PA algorithms for IA-based CR networks.

How Effective Is Road Mitigation at Reducing Road-Kill? A Meta-Analysis.

Abstract: Road traffic kills hundreds of millions of animals every year, posing a critical threat to the populations of many species. To address this problem there are more than forty types of road mitigation measures available that aim to reduce wildlife mortality on roads (road-kill). For road planners, deciding on what mitigation method to use has been problematic because there is little good information about the relative effectiveness of these measures in reducing road-kill, and the costs of these measures vary greatly. We conducted a meta-analysis using data from 50 studies that quantified the relationship between road-kill and a mitigation measure designed to reduce road-kill. Overall, mitigation measures reduce road-kill by 40% compared to controls. Fences, with or without crossing structures, reduce road-kill by 54%. We found no detectable effect on road-kill of crossing structures without fencing. We found that comparatively expensive mitigation measures reduce large mammal road-kill much more than inexpensive measures. For example, the combination of fencing and crossing structures led to an 83% reduction in road-kill of large mammals, compared to a 57% reduction for animal detection systems, and only a 1% for wildlife reflectors. We suggest that inexpensive measures such as reflectors should not be used until and unless their effectiveness is tested using a high-quality experimental approach. Our meta-analysis also highlights the fact that there are insufficient data to answer many of the most pressing questions that road planners ask about the effectiveness of road mitigation measures, such as whether other less common mitigation measures (e.g., measures to reduce traffic volume and/or speed) reduce road mortality, or to what extent the attributes of crossing structures and fences influence their effectiveness. To improve evaluations of mitigation effectiveness, studies should incorporate data collection before the mitigation is applied, and we recommend a minimum study duration of four years for Before-After, and a minimum of either four years or four sites for Before-After-Control-Impact designs.

What determines the spatial extent of landscape effects on species

Abstract: Landscape ecologists are often interested in measuring the effects of an environmental variable on a biological response; however, the strength and direction of effect depend on the size of the area within which the environmental variable is measured. Thus a central objective is to identify the optimal spatial extent within which to measure the environmental variable, i.e. the “scale of effect”. Our objectives are (1) to provide a comprehensive summary of the hypotheses concerning what determines the scale of effect, (2) to provide predictions that can be tested in empirical studies, and (3) to show, with a review of the literature, that most of these predictions have so far been inadequately tested. We propose 14 predictions derived from five hypotheses explaining what determines the scale of effect, and review the literature (if any) supporting each prediction. These predictions involve five types of factors: (A) species traits, (B) landscape variables, (C) biological responses (e.g. abundance vs. occurrence), (D) indirect influences, and (E) regional context of the study. We identify methodological issues that hinder estimation of the scale of effect. Of the 14 predictions, only nine have been tested empirically and only five have received some empirical support. Most support is from simulation studies. Empirical evidence usually does not support predictions. The study of the spatial scale at which landscape variables influence biological outcomes is in its infancy. We provide directions for future research by clarifying predictions concerning the determinants of the scale of effect.

Measurement of the inelastic proton-proton cross section at √s=13 TeV with the ATLAS detector at the LHC

Abstract: This Letter presents a measurement of the inelastic proton-proton cross section using 60 μb^{-1} of pp collisions at a center-of-mass energy sqrt[s] of 13 TeV with the ATLAS detector at the LHC. Inelastic interactions are selected using rings of plastic scintillators in the forward region (2.07 10^{-6}, where M_{X} is the larger invariant mass of the two hadronic systems separated by the largest rapidity gap in the event. In this ξ range the scintillators are highly efficient. For diffractive events this corresponds to cases where at least one proton dissociates to a system with M_{X}>13 GeV. The measured cross section is compared with a range of theoretical predictions. When extrapolated to the full phase space, a cross section of 78.1±2.9 mb is measured, consistent with the inelastic cross section increasing with center-of-mass energy.

Interference Alignment and Its Applications: A Survey, Research Issues, and Challenges

Abstract: The capacity of interference network is a fundamental issue that eludes the researchers for decades. Interference alignment (IA) is an emerging interference management technique that is degrees of freedom (DoFs) optimal, which means it can approach the capacity of interference network at very high signal-to-noise ratio (SNR). In IA networks, the signals are constrained into the same subspaces at the unintended receivers through cooperative precoding, and the desired signal can be recovered at each receiver by eliminating the aligned interferences using decoding matrix. Due to its promising performance in interference management, IA has successfully been applied to many kinds of multiuser wireless networks with excellent performance. Nevertheless, there are still some challenges for the practical utilization of IA, e.g., the overhead of channel state information (CSI) feedback, performance degradation at low and moderate SNRs, etc. In this review, we provide a survey on IA and its applications and discuss some research issues and challenges. The dimensions, networks topologies, and applications of IA are first introduced. Then some fundamental aspects of IA are discussed, including feasibility condition, performance metrics, iterative algorithms, and CSI. We also present some recent research issues of opportunistic IA, spectrum sharing, green IA, topology management, and physical layer security. Finally, some research challenges of IA are identified.