Evaluation rapide et précise des performances d'une communication LoRa basée sur la fonction de Marcum

International audienc

Performances de LoRa sur les canaux de Rice et Nakagami

International audienceLes applications telles que les fermes connectées, les villes intelligentes ou l'industrie 4.0 utilisent de plus en plus les Low Power Wide Area Networks (LPWAN), permettant un bon compromis entre la portée, le débit et la consommation d'énergie. Cependant, seulement quelques études théoriques sont disponibles pour aider à la conception de ces réseaux avant un déploiement. Une nouvelle approche basée sur la fonction de Marcum est proposée ici pour estimer la probabilité d'erreur binaire de LoRa. La méthode proposée est appliquée avec un bruit gaussien pour les canaux de Nakagami et Rice

How robust is a LoRa communication against impulsive noise?

International audienceIn the last decade Internet of Things (IoT) grew up in an exponential behavior with applications requiring long range and low power wireless transmissions. Factory of the Future (FoF), also called Industry 4.0, aims to use IoT technologies to enhance productivity, therefore adding high reliability constraints. Several IoT standards were proposed and LoRa has emerged as a high potential candidate for a variety of applications. LoRa modulation is based on a chirp spread-spectrum technique and offers efficient transmission up to 50 kbps over several kilometers. The performance of LoRa in terms of symbol or bit error probability has been theoretically analyzed in few recent papers for a Gaussian channel. However, the industrial environment is often corrupted with impulsive non Gaussian noise generated by high-power equipment. In this paper, the impact of impulsive noise, modeled by the Middleton Class-A noise, is introduced and the robustness of a LoRa communication is studied. Compared to the Gaussian case, simulations show that impulsive noise may severely degrade system performance. This Signal-to-Noise Ratio loss can reach up to 10 dB, but increasing the spreading factor can reduce the noise impact

Fast performance evaluation of LoRa communications over Rayleigh fading channels

International audienceIn the last years, LoRa has emerged as a high potential candidate among several standards for the Internet of Things (IoT) subject to an exponential development. LoRa modulation is based on a classical chirp spread-spectrum technique and permits wireless data transmission up to 50 kbps over several kilometers with a high energy efficiency. Although a well-known principle, its performance in terms of symbol or bit error probability has been theoretically analyzed in few recent papers only. Recently, closed-form approximations of Bit Error Probability (BEP) for additive white Gaussian noise channels and Rayleigh fading channels were proposed. In this paper, we introduce a new approach using Marcum function for approximating the LoRa BEP. The latter is available for both Additive White Gaussian Noise channels and Rayleigh fading channels and the approach should deal with a variety of fadings. Simulations and comparisons with the state of the art show that the proposed approximation is almost ten times more accurate and may be considered as a numerical reference

Evaluation rapide et précise des performances d'une communication LoRa basée sur la fonction de Marcum

International audienc

Accurate LoRa Performance evaluation using Marcum function

International audienceIn the last years, Internet of Things (IoT) grew up in an exponential behavior and required long range and low power wireless transmissions. Several standards were proposed and LoRa has emerged as a high potential candidate for many IoT solutions. LoRa modulation is based on a chirp spread-spectrum technique and offers efficient transmission up to 50 kbps over several kilometers. Although the principle is known and studied for decades now, the performance in terms of symbol or bit error probability has been theoretically analyzed in few papers only. Closed-form approximations for additive white Gaussian noise and Rayleigh fading were recently proposed. In this paper, we propose a new approach based on Marcum function. Simulations and comparisons with the state of the art show that the proposed approximation of the Binary Error Probability is up to ten times more accurate for a full SNR range

A Computable Form for LoRa Performance Estimation: Application to Ricean and Nakagami Fading

International audienceEmerging applications such as connected farms, wildness monitoring, smart cities, and Factory of the Future leverage emerging Low Power Wide Area Networks (LPWAN), allowing a good trade-off between radio range, data rate, and energy consumption. However, only few theoretical studies of these recent technologies are available to help network designers to optimize real field deployments or even achieve real-time adaptation of transmission parameters. A new approach based on Marcum function is proposed in this paper to estimate-in a fast and accurate manner-the performance in terms of Bit Error Rate of LoRa, one of the most used LPWAN technologies. The method is proposed for Gaussian channels, over challenging propagation environments, i.e., Ricean and Nakagami fadings. Simulation results show that our proposed approximation reduces the approximation error about one order of magnitude compared to existing ones and can be computed by classical software. INDEX TERMS Accurate approximation, fading channels, Internet of Things, LoRa performance, low-power wide area networks, Marcum function

Performances de LoRa sur les canaux de Rice et Nakagami

International audienceLes applications telles que les fermes connectées, les villes intelligentes ou l'industrie 4.0 utilisent de plus en plus les Low Power Wide Area Networks (LPWAN), permettant un bon compromis entre la portée, le débit et la consommation d'énergie. Cependant, seulement quelques études théoriques sont disponibles pour aider à la conception de ces réseaux avant un déploiement. Une nouvelle approche basée sur la fonction de Marcum est proposée ici pour estimer la probabilité d'erreur binaire de LoRa. La méthode proposée est appliquée avec un bruit gaussien pour les canaux de Nakagami et Rice

Performances de LoRa sur les canaux de Rice et Nakagami

International audienceLes applications telles que les fermes connectées, les villes intelligentes ou l'industrie 4.0 utilisent de plus en plus les Low Power Wide Area Networks (LPWAN), permettant un bon compromis entre la portée, le débit et la consommation d'énergie. Cependant, seulement quelques études théoriques sont disponibles pour aider à la conception de ces réseaux avant un déploiement. Une nouvelle approche basée sur la fonction de Marcum est proposée ici pour estimer la probabilité d'erreur binaire de LoRa. La méthode proposée est appliquée avec un bruit gaussien pour les canaux de Nakagami et Rice

Catching the LoRa ADR Bandit with a New Sheriff: J-LoRaNeS

International audienceLow Power Wide Area Networks (LPWAN) are very promising for a variety of IoT applications, but they face two major challenges: energy consumption of the wireless nodes and congestion of the networks due to the huge number of nodes involved. LPWAN transmission parameters can be optimised, e.g. using artificial intelligence algorithms, but the performance estimation made during simulations is often higher than what it is in reality. In this paper, we propose a new LoRa network simulator, J-LoRaNeS. Based on the Julia programming language, it allows fast prototyping and is therefore suited to the study of different adaptive LoRa mechanisms. We used our novel simulator to clearly show the benefits of using multi-arm bandits for adaptation, but we also show that the benefits reported in the literature are not attainable when realistic network conditions are simulated. CCS CONCEPTS • Computing methodologies → Simulation tools; Reinforcement learning