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

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Linear Combining in Dependent α-Stable Interference

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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

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

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On Capacity Sensitivity in Additive Vector Symmetric α-Stable Noise Channels

International audienceDue to massive numbers of uncoordinated devices present in wireless networks for the Internet of Things (IoT), interference is a key challenge. There is evidence both from experiments and analysis of statistical models that the uncoordi-nated nature of channel access leads to non-Gaussian statistics for the interference. A particularly attractive model in this scenario is the additive vector α-stable noise channel. In this paper, we study the capacity of this channel with fractional moment constraints. In particular, we establish well-posedness of the optimization problem for the capacity. We also study convergence of the capacity loss due to an additional constraint where input probability measures are concentrated on spherical shells, in addition to the fractional moment constraints

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

Multivariate alphaalpha-Stable Models in OFDM-Based IoT Networks with Interference From a Poisson Spatial Field of Interferers

International audienceThe uncoordinated nature of IoT networks makes interference management a challenging problem. Motivated by NB-IoT and SCMA protocols, we study the interference statistics of a Poisson spatial field of IoT interferers exploiting OFDM. We show for a sufficiently large number of subcarriers that the interference statistics are well-approximated by a sub-Gaussian α-stable random vector with a non-isotropic underlying Gaussian random vector. This result forms a basis to improve detection and decoding algorithms at the receiver