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

Apprentissage avec des bandits multi-bras pour des réseaux IoT (Internet des Objets)

International audienc

Une démonstration d'apprentissage avec des bandits multi-bras pour des réseaux IoT (MALIN)

International audienceWith the advent of the Internet of Things (IoT), unlicensed band are going to be shared by a large number of devices with dissimilar caracteristics. In such context, solutions are required to allow the coexistence of devices and to avoid performance drop due to interference. In this demonstration, we show that reinforcement learning algorithms and in particular Multi-Armed Bandit algorithms can be used as a means of improving the performance of IoT communications.Avec l'arrivée de l'Internet des objets (IoT), le spectre sans licence va être partagée par un grand nombre d'appareils aux caractéristiques différentes. Dans un tel contexte, des solutions sont nécessaires pour permettre la coexistence des appareils et éviter les pertes de performance dues aux interférences. Dans cette démonstration, nous montrons que les algorithmes d'apprentissage de renforcement et en particulier les algorithmes de bandits multi-bras peuvent être utilisés comme un moyen simple d'améliorer les performances des communications IoT

New Expressions for Ergodic Capacities of Optical Fibers and Wireless MIMO Channels

Multimode/multicore fibers are expected to provide an attractive solution to overcome the capacity limit of current optical communication system. In presence of high crosstalk between modes/cores, the squared singular values of the input/output transfer matrix follow the law of the Jacobi ensemble of random matrices. Assuming that the channel state information is only available at the receiver, we derive in this paper a new expression for the ergodic capacity of the Jacobi MIMO channel. This expression involves double integrals which can be evaluated easily and efficiently. Moreover, the method used in deriving this expression does not appeal to the classical one-point correlation function of the random matrix model. Using a limiting transition between Jacobi and Laguerre polynomials, we derive a similar formula for the ergodic capacity of the Gaussian MIMO channel. The analytical results are compared with Monte Carlo simulations and related results available in the literature. A perfect agreement is obtained

New Expressions for Ergodic Capacities of Optical Fibers and Wireless MIMO Channels

Multimode/multicore fibers are expected to provide an attractive solution to overcome the capacity limit of current optical communication system. In presence of high crosstalk between modes/cores, the squared singular values of the input/output transfer matrix follow the law of the Jacobi ensemble of random matrices. Assuming that the channel state information is only available at the receiver, we derive in this paper a new expression for the ergodic capacity of the Jacobi MIMO channel. This expression involves double integrals which can be evaluated easily and efficiently. Moreover, the method used in deriving this expression does not appeal to the classical one-point correlation function of the random matrix model. Using a limiting transition between Jacobi and Laguerre polynomials, we derive a similar formula for the ergodic capacity of the Gaussian MIMO channel. The analytical results are compared with Monte Carlo simulations and related results available in the literature. A perfect agreement is obtained

Implémentation GNU Radio de MALIN: "stratégies d'apprentissage de bandits multi-bras pour des réseaux de l'Internet des Objets" ("Multi-Armed bandits Learning for Internet-of-things Networks")

International audienceWe implement an IoT network in the following way: one gateway, one or several intelligent (i.e., learning) objects, embedding the proposed solution, and a traffic generator that emulates radio interferences from many other objects. Intelligent objects communicate with the gateway with a wireless ALOHA-based protocol, which does not require any specific overhead for the learning. We model the network access as a discrete sequential decision making problem, and using the framework and algorithms from Multi-Armed Bandit (MAB) learning, we show that intelligent objects can improve their access to the network by using low complexity and decentralized algorithms, such as UCB1 and Thompson Sampling. This solution could be added in a straightforward and costless manner in LoRaWAN networks, just by adding this feature in some or all the devices, without any modification on the network side.Nous implémentons un réseau IoT de la manière suivante : une station de base, un ou plusieurs objets intelligents (c'est-à-dire dotés de capacité d'apprentissage), intégrant la solution proposée, et un générateur de trafic qui émule les interférences radio de nombreux autres objets. Les objets intelligents communiquent avec la station de base à l'aide d'un protocole sans fil basé sur ALOHA, qui ne nécessite pas de surcharge spécifique pour l'apprentissage. Nous modélisons l'accès au réseau comme un problème de prise de décision séquentielle discrète, et en utilisant le cadre et les algorithmes de l'apprentissage des bandits multi-bras (MAB, Multi-Armed Bandit), nous montrons que les objets intelligents peuvent améliorer leur accès au réseau en utilisant des algorithmes peu complexes et décentralisés, tels que UCB1 et Thompson Sampling. Cette solution pourrait être ajoutée de manière simple et gratuite dans les réseaux LoRaWAN, simplement en ajoutant cette fonctionnalité dans certains ou tous les appareils, sans aucune modification côté réseau

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