Upper-Confidence Bound for Channel Selection in LPWA Networks with Retransmissions

In this paper, we propose and evaluate different learning strategies based on Multi-Arm Bandit (MAB) algorithms. They allow Internet of Things (IoT) devices to improve their access to the network and their autonomy, while taking into account the impact of encountered radio collisions. For that end, several heuristics employing Upper-Confident Bound (UCB) algorithms are examined, to explore the contextual information provided by the number of retransmissions. Our results show that approaches based on UCB obtain a significant improvement in terms of successful transmission probabilities. Furthermore, it also reveals that a pure UCB channel access is as efficient as more sophisticated learning strategies.Comment: The source code (MATLAB or Octave) used for the simula-tions and the figures is open-sourced under the MIT License, atBitbucket.org/scee\_ietr/ucb\_smart\_retran

GNU Radio implementation for Multiuser Multi-Armed Bandit learning algorithms in IoT networks

International audienceNovel access schemes based on multi-armed bandit (MAB) learning approaches has been proposed to support the increasing number of devices in IoT networks in unlicensed bands. In the present work, a GNU radio framework is implemented to recreate an IoT network where IoT devices embedding MAB algorithms are able to learn the availability of the channels for their packet transmissions to the gateway. It allows to incorporate several IoT users recognized by an identifier (ID), and provides a gateway to handle a large number of IDs as well as the packet collisions among IoT devices. The experimental results show that the introduction of decentralized learning mechanism in access schemes can improve the performance of the IoT devices, both in terms of energy consumption and spectrum overload, thanks to radio collision mitigation

Tailoring Index-Modulation for uplink IoT and M2M Networks

International audienceThe low complexity, low cost of implementation, as well as the spectral and energy efficiency, are key features for the development of the internet of things (IoT) networks. In this context, the introduction of index modulation on single carrier-frequency division multiple access (SC-FDMA-IM) has reported significant gains in terms of energy efficiency. In this paper, we evaluate an SC-FDMA-IM scheme tailored for IoT devices taking into account its complexity and performance. For that end, computational simulations and a complexity analysis for different detectors are carried out. Our results show that a significant bit error rate gain is obtained in comparison to a conventional SC-FDMA scheme, while maintaining a reduced computational complexity and power consumption. Index Terms-Index modulation, SC-FDMA, narrow band internet of things (NB-IoT), M2M communications

Stratégies de Bornes Supérieures de Confiances pour la Sélection de Canaux dans des Réseaux LPWA avec Retransmissions

The source code (MATLAB or Octave) used for the simula-tions and the figures is open-sourced under the MIT License, atBitbucket.org/scee_ietr/ucb_smart_retransInternational audienceIn this paper, we propose and evaluate different learning strategies based on Multi-Arm Bandit (MAB) algorithms. They allow Internet of Things (IoT) devices to improve their access to the network and their autonomy, while taking into account the impact of encountered radio collisions. For that end, several heuristics employing Upper-Confident Bound (UCB) algorithms are examined, to explore the contextual information provided by the number of retransmissions. Our results show that approaches based on UCB obtain a significant improvement in terms of successful transmission probabilities. Furthermore, it also reveals that a pure UCB channel access is as efficient as more sophisticated learning strategies.Dans cet article, nous proposons et évaluons différentes stratégies d'apprentissage basées sur les algorithmes MAB (bandit multi-bras). Ils permettent aux appareils des futurs réseaux de l'Internet des Objets (IoT) d'améliorer leur accès au réseau et leur autonomie, tout en tenant compte de l'impact des collisions radio rencontrées. Pour ce faire, plusieurs heuristiques utilisant des algorithmes des Bornes Supérieures de Confiance (UCB) sont examinées, afin d'explorer les informations contextuelles fournies par le nombre de retransmissions. Nos résultats montrent que les approches basées sur UCB obtiennent une amélioration significative en termes de probabilités de transmission réussie. En outre, elle révèle également qu'un accès aux canaux basé sur la stratégie UCB la plus simple est aussi efficace que des stratégies d'apprentissage plus sophistiquées

Low-Complexity Iterative Receiver for Orthogonal Chirp Division Multiplexing

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Spectrum Sensing Using Software Defined Radio for Cognitive Radio Networks: A Survey

International audienceCognitive radio (CR) network has emerged as a potential solution to the under-utilization problem of the allocated radio spectrum, where spectrum sensing (SS) plays a key role to enable the coexistence between primary and secondary users. It has attracted research interests, and several works have been reported in the literature. Nevertheless, the assumptions and simplifications introduced during the modeling of the communication system often yield misleading conclusions each time relevant aspects of their implementation on a testbed are omitted. Hence, prototypes are built to study their behaviour under real-world conditions, therefore software defined radio (SDR) has emerged as an ideal vehicle to allow researchers to experiment with prototypes of these CR approaches. In this survey, we provide an overview of the latest works in CR networks related to the spectrum awareness approaches and taking into account their implementation on testbeds. These approaches are classified from a practical point of view, where a detailed review of the existing works for each category is provided. A review of the existing SDR platforms is also exposed highlighting the main components and features of current architectures employed for experimental evaluation of CR approaches. Next, the challenges to implement current spectrum awareness approaches on SDR platforms are detailed. Finally, at the light of these reviews, research challenges and open issues are identified for future research directions

Beyond LoRa and NB-IoT: Proposals for Future LPWA Systems

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