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

An inventory of supraglacial lakes and channels across the West Antarctic Ice Sheet

Quantifying the extent and distribution of supraglacial hydrology, i.e. lakes and streams, is important for understanding the mass balance of the Antarctic ice sheet and its consequent contribution to global sea-level rise. The existence of meltwater on the ice surface has the potential to affect ice shelf stability and grounded ice flow through hydrofracturing and the associated delivery of meltwater to the bed. In this study, we systematically map all observable supraglacial lakes and streams in West Antarctica by applying a semi-automated Dual-NDWI (normalised difference water index) approach to >2000 images acquired by the Sentinel-2 and Landsat-8 satellites during January 2017. We use a K-means clustering method to partition water into lakes and streams, which is important for understanding the dynamics and inter-connectivity of the hydrological system. When compared to a manually delineated reference dataset on three Antarctic test sites, our approach achieves average values for sensitivity (85.3 % and 77.6 %), specificity (99.1 % and 99.7 %) and accuracy (98.7 % and 98.3 %) for Sentinel-2 and Landsat-8 acquisitions, respectively. We identified 10 478 supraglacial features (10 223 lakes and 255 channels) on the West Antarctic Ice Sheet (WAIS) and Antarctic Peninsula (AP), with a combined area of 119.4 km2 (114.7 km2 lakes, 4.7 km2 channels). We found 27.3 % of feature area on grounded ice and 54.9 % on floating ice shelves. In total, 17.8 % of feature area crossed the grounding line. A recent expansion in satellite data provision made new continental-scale inventories such as these, the first produced for WAIS and AP, possible. The inventories provide a baseline for future studies and a benchmark to monitor the development of Antarctica's surface hydrology in a warming world and thus enhance our capability to predict the collapse of ice shelves in the future. The dataset is available at https://doi.org/10.5281/zenodo.5642755 (Corr et al., 2021)

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

Understanding the impact of legislation on 'reduction of disease risk' claims on food and drinks: The REDICLAIM project

The Nutrition and Health Claims Regulation (EC No. 1924/2006) has established a common framework for the regulation of nutrition and health claims used on foods across the European Union. This regulation aims to provide the European food industry opportunities for product innovation whilst protecting consumer interests with respect to controlling misleading advertising and promoting public health. However, in order to satisfy the approval of new health claims procedure particularly for new 'reduction of disease risk' claims [Article 14(1)(a) claims], significant research activity is required by industry to scientifically substantiate the claims they wish to make. There is a need to establish whether the implementation of this legislation is in fact driving product innovation and the development of healthy foods or whether it forms a barrier to such developments. The EU-funded REDICLAIM project is currently considering these issues. This article describes the project's preliminary results and outlines the further programme of work

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