Linear Combining in Dependent α-Stable Interference

International audienc

On the Validity of Isotropic Complex α-Stable Interference Models for Interference in the IoT

International audienc

Stochastic Resource Optimization of Random Access for Transmitters with Correlated Activation

For a range of scenarios arising in sensor networks, control and edge computing, communication is event-triggered; that is, in response to the environment of the communicating devices. A key feature of device activity in this setting is correlation, which is particularly relevant for sensing of physical phenomena such as earthquakes or flooding. Such correlation introduces a new challenge in the design of resource allocation and scheduling for random access that aim to maximize throughput or expected sum-rate, which do not admit a closed-form expression. In this paper, we develop stochastic resource optimization algorithms to design a random access scheme that provably converge with probability one to locally optimal solutions of the throughput and the sum-rate. A key feature of the stochastic optimization algorithm is that the number of parameters that need to be estimated grows at most linearly in the number of devices. We show via simulations that our algorithms can outperform existing approaches by up to 30% for a moderate number of available time slots in realistic networks

Impulsive Multivariate Interference Models for IoT Networks

Device density in wireless internet of things (IoT) networks is now rapidly increasing and is expected to continue in the coming years. As a consequence, interference is a crucial limiting factor on network performance. This is true for all protocols operating on ISM bands (such as SigFox and LoRa) and licensed bands (such as NB-IoT). In this paper, with the aim of improving system design, we study the statistics of the interference due to devices in IoT networks; particularly those exploiting NB-IoT. Existing theoretical and experimental works have suggested that interference on each subband is well-modeled by impulsive noise, such as α-stable noise. If these devices operate on multiple partially overlapping resource blocks-which is an option standardized in NB-IoT-complex statistical dependence between interference on each subband is introduced. To characterize the multivariate statistics of interference on multiple subbands, we develop a new model based on copula theory and demonstrate that it effectively captures both the marginal α-stable model and the dependence structure induced by overlapping resource blocks. We also develop a low complexity estimation procedure tailored to our interference model, which means that the copula model can often be expressed in terms of standard network parameters without significant delays for calibration. We then apply our interference model in order to optimize receiver design, which provides a tractable means of outperforming existing methods for a wide range of network parameters

Copula-Based Interference Models for IoT Wireless Networks

International audienceAs the Internet of Things (IoT) is largely supported by wireless communication networks in unlicensed bands, there has been a proliferation of technologies that use a large variety of protocols. An ongoing challenge is how these networks can coexist given that they have different power levels, symbol periods, and access protocols. In this paper, we study the statistics of interference due to IoT networks that transmit small amounts of data. A key observation is that sets of active devices change rapidly, which leads to impulsive noise channels. Moreover, these devices operate on multiple partially overlapping resource blocks. As such, we characterize the joint distribution and propose a tractable model based on copulas. Using our copula model, we derive closed-form achievable rates. This provides a basis for resource allocation and network design for coexisting IoT networks. I. INTRODUCTION With the increasing scale of wireless network deployments for the Internet of Things (IoT), an ongoing challenge is to ensure that these networks can coexist. A key issue is that interference from a large number of devices, even if they operate at low power levels, can degrade the performance of other communication networks. This means that the interference statistics are difficult to characterize and has lead to a number of experimental studies on the interference in various contexts [1]-[4]. One feature observed in IoT networks is the presence of impulsive interference, where high amplitude interference is significantly more likely than in Gaussian models. This behavior has been observed both in experimental studies [4] and also in theoretical analysis [5], [6]. As a consequence, Gaussian models are often not appropriate and the interference statistics lie in a more general class of models. Introducing non-Gaussian interference models implies that the interference statistics are not simply characterized by their mean and variance. This issue is amplified in settings where a number of frequency bands are used for transmissions. In these cases, the covariance matrix is not sufficient in order to characterize the joint interference statistics over multiple frequency bands. This is particularly evident when the frequency bands used by different users only partially overlap [7], such as in non-orthogonal multiple access (NOMA) schemes [8] including sparse code multiple access (SCMA) [9]. Due to the rich dependence structure possible for the joint distribution of non-Gaussian random vectors, a key question is how it should be modeled. For the purposes of analysis

On the Validity of Isotropic Complex α-Stable Interference Models for Interference in the IoT

International audienc

Dynamic Interference for Uplink SCMA in Large-Scale Wireless Networks without Coordination

International audienceFast varying active transmitter sets are a key feature of wireless communication networks with very short length transmissions arising in communications for the Internet of Things. As a consequence, the interference is dynamic, leading to non-Gaussian statistics. At the same time, the very high density of devices is motivating non-orthogonal multiple access (NOMA) techniques, such as sparse code multiple access (SCMA). In this paper, we study the statistics of the dynamic interference from devices using SCMA. In particular, we show that the interference is α-stable with non-trivial dependence structure for large scale networks modeled via Poisson point processes. Moreover, the interference on each frequency band is shown to be sub-Gaussian α-stable in the special case of disjoint SCMA codebooks. We investigate the impact of the α-stable interference on achievable rates and on the optimal density of devices. Our analysis suggests that ultra dense networks are desirable even with α-stable interference

Triangle Counting in Dynamic Graph Streams

Estimating the number of triangles in graph streams using a limited amount of memory has become a popular topic in the last decade. Different variations of the problem have been studied, depending on whether the graph edges are provided in an arbitrary order or as incidence lists. However, with a few exceptions, the algorithms have considered {\em insert-only} streams. We present a new algorithm estimating the number of triangles in {\em dynamic} graph streams where edges can be both inserted and deleted. We show that our algorithm achieves better time and space complexity than previous solutions for various graph classes, for example sparse graphs with a relatively small number of triangles. Also, for graphs with constant transitivity coefficient, a common situation in real graphs, this is the first algorithm achieving constant processing time per edge. The result is achieved by a novel approach combining sampling of vertex triples and sparsification of the input graph. In the course of the analysis of the algorithm we present a lower bound on the number of pairwise independent 2-paths in general graphs which might be of independent interest. At the end of the paper we discuss lower bounds on the space complexity of triangle counting algorithms that make no assumptions on the structure of the graph.Comment: New version of a SWAT 2014 paper with improved result

Microarray Method for the Rapid Detection of Glycosaminoglycan–Protein Interactions

Glycosaminoglycans (GAGs) perform numerous vital functions within the body. As major components of the extracellular matrix, these polysaccharides participate in a diverse array of cell-signaling events. We have developed a simple microarray assay for the evaluation of protein binding to various GAG subclasses. In a single experiment, the binding to all members of the GAG family can be rapidly determined, giving insight into the relative specificity of the interactions and the importance of specific sulfation motifs. The arrays are facile to prepare from commercially available materials

Characterization of NF-κB reporter U937 cells and their application for the detection of inflammatory immune-complexes

Our study tested the hypothesis that immunoglobulins differ in their ability to activate the nuclear factor-κB pathway mediated cellular responses. These responses are modulated by several properties of the immune complex, including the ratio of antibody isotypes binding to antigen. Immunoassays allow the measurement of antigen specific antibodies belonging to distinct immunoglobulin classes and subclasses but not the net biological effect of the combination of these antibodies. We set out to develop a biosensor that is suitable for the detection and characterization of antigen specific serum antibodies. We genetically modified the monocytoid U937 cell line carrying Fc receptors with a plasmid encoding NF-κB promoter-driven GFP. This clone, U937-NF-κB, was characterized with respect to FcR expression and response to solid-phase immunoglobulins. Human IgG3, IgG4 and IgG1 induced GFP production in a time- and dose-dependent manner, in this order of efficacy, while IgG2 triggered no activation at the concentrations tested. IgA elicited no response alone but showed significant synergism with IgG3 and IgG4. We confirmed the importance of activation via FcγRI by direct stimulation with monoclonal antibody and by competition assays. We used citrullinated peptides and serum from rheumatoid arthritis patients to generate immune complexes and to study the activation of U937-NF-κB, observing again a synergistic effect between IgG and IgA. Our results show that immunoglobulins have distinct pro-inflammatory potential, and that U937-NF-κB is suitable for the estimation of biological effects of immune-complexes, offering insight into monocyte activation and pathogenesis of antibody mediated diseases