Joint Spreading Factor and Coding Rate Assignment in LoRaWAN Networks

El-Aasser, Minar; Elshabrawy, Tallal; Ashour, Mohamed

doi:10.1109/gciot.2018.8620147

Cited by 28 publications

(18 citation statements)

References 11 publications

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“…There are three main approaches to improve the transmission performance of LoRaWAN: (i) Improving the interference resiliency [6]- [8], (ii) reducing the number of packet collisions through resource management [9]- [13], and (iii) increasing the number of bits conveyed by a CSS symbol [14]. In [6], an SF and coding rate allocation algorithm have been proposed for maximizing the throughput of each SF tier comprising nodes using the same SF. [7] optimizes SF allocation in order to maximize the packet success probability (PSP) and achieve maximum connectivity.…”

Section: Related Work a Lorawan Networkmentioning

confidence: 99%

Packet-Level Index Modulation for LoRaWAN

et al. 2021

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The long range wide area network (LoRaWAN) is one of the enabling technologies for low power wide area (LPWA) networks. In LoRaWAN, a node transmits its packet to a gateway (GW) in an autonomous and decentralized manner. The quantity of data that can be transmitted by each node is limited by the duty cycle (DC). Furthermore, it is not easy for the nodes to perform sophisticated techniques for increasing the data quantity due to their limited functionality, especially when the nodes are battery-powered. If the network size increases, packet collision may occur more frequently. Since the packet transmission drains the LoRaWAN nodes' battery, the packet collision results in a waste of limited power. Thus, it is necessary to develop a simple but effective transmission strategy that efficiently utilizes limited battery at each LoRaWAN node. This study proposes packet-level index modulation (PLIM), which is suitable for such LPWA networks. PLIM takes advantage of the sparse data packet transmission in time and the selection of a frequency channel among multiple ones by each node. A time slot and frequency channel combination is selected, i.e., the index, to increase the data quantity. For long-range communication, it is necessary to use a higher spreading factor (SF) in LoRaWAN, which results in a lower data rate due to the DC. The proposed PLIM can compensate for such data rate loss by taking advantage of the sparse transmission in time. Numerical evaluation elucidates that the proposed PLIM can increase the data quantity of LoRaWAN system without requiring any modification in the specification. When the SF is 10, the proposed PLIM can increase the data quantity up to 32.5%, compared to the conventional LoRaWAN system. INDEX TERMS LPWA, LoRaWAN, index modulation.

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Section: Related Work a Lorawan Networkmentioning

confidence: 99%

Packet-Level Index Modulation for LoRaWAN

et al. 2021

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“…Through this, the throughput was improved to a value close to the theoretical maximum value. El-Aasser et al [9] proposes the algorithms of sensitivi-tySF and assignmentSF, which are smarter SF setting techniques than the existing ADR, to increase both the throughput between the nodes of the same tier and the overall throughput, and achieve higher throughput than the existing ADR. For fair SF allocation, Cuomo et al [10] proposed ordered water-filling-based EXPLoRa-KM (K-means) and EXPLoRa-TS (time symbol) techniques.…”

Section: Performance Issues Of Lora and Adrmentioning

confidence: 99%

Network resource optimization with reinforcement learning for low power wide area networks

Park¹,

Lee²,

Joe³

2020

J Wireless Com Network

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As the 4th industrial revolution using information becomes an issue, wireless communication technologies such as the Internet of Things have been spotlighted. Therefore, much research is needed to satisfy the technological demands for the future society. A LPWA (low power wide area) in the wireless communication environment enables low-power, long-distance communication to meet various application requirements that conventional wireless communications have been difficult to meet. We propose a method to consume the minimum transmission power relative to the maximum data rate with the target of LoRaWAN among LPWA networks. Reinforcement learning is adopted to find the appropriate parameter values for the minimum transmission power. With deep reinforcement learning, we address the LoRaWAN problem with the goal of optimizing the distribution of network resources such as spreading factor, transmission power, and channel. By creating a number of deep reinforcement learning agents that match the terminal nodes in the network server, the optimal transmission parameters are provided to the terminal nodes. The simulation results show that the proposed method is about 15% better than the existing ADR (adaptive data rate) MAX of LoRaWAN in terms of throughput relative to energy transmission.

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“…Desta forma, um modelo matemático desenvolvido através de PLIMé de grande importância para formular e apresentar resultados da soluçãoótima para maximizar o desempenho, poiś e possível obter uma melhor configuração dos parâmetros em dispositivos IoT com baixo poder computacional. No estado-da-arte, alguns trabalhos focaram na alocação de recurso do LoRaWAN através de modelos de otimização usando alocação de SF [El-Aasser et al 2018, Caillouet et al 2019, Amichi et al 2019, alocação de SF juntamenteà Taxa de codificação (CR, de Code Rate) [Sandoval et al 2019a] e maximizar a vazão [Sandoval et al 2019b]. Contudo, definir um modelo de otimização dos parâmetros de CF e SF que maximize a entrega de dados aindaé um desafio em aberto [Ertürk et al 2019].…”

Section: Introductionunclassified

Modelo de Otimização de Alocação de Recursos em LoRaWAN para Aplicações de Internet das Coisas

Matni¹,

Moraes²,

Riker³

et al. 2020

Anais Do XXV Workshop De Gerência E Operação De Redes E Serviços (WGRS 2020)

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O LoRaWAN é a tecnologia sem fio de longo alcance mais utilizada para aplicações de Internet das Coisas (IoT) que trabalham com alta densidade, pois é capaz de conectar dispositivos que requerem serviços de comunicação de longo alcance, baixo custo e menor consumo de energia. Contudo, a densificação do uso de LoRaWAN em serviços IoT traz uma série de desafios devido a interferência por transmissão simultânea no mesmo canal e/ou maior consumo de energia pelos dispositivos. Nesse contexto, é crucial entender os conceitos de alocação de recursos do LoRaWAN para otimizar a configuração de parâmetros específicos do rádio, ie, Fator de Espalhamento (SF) e Frequência de portadora (CF), em que a otimização dos parâmetros de transmissão via modelos de otimização é um desafio em aberto. Este artigo apresenta o MARCO, um modelo de otimização de alocação de recursos para minimizar a Qualidade de Serviço (QoS) do LoRaWAN para aplicações de IoT, além de contribuir para a melhora da eficiência enérgica dos dispositivos. O MARCO considera uma programação linear inteira mista para definir as configurações ideais dos parâmetros SF e CF, bem como especificações de trafego da rede como um todo. Resultados de simulação demonstram a eficiência em termos de taxa de extração de dados, número de colisões e consumo de energia do MARCO em comparação com as heurísticas de alocação de recursos para LoRaWAN existentes.

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Joint Spreading Factor and Coding Rate Assignment in LoRaWAN Networks

Cited by 28 publications

References 11 publications

Packet-Level Index Modulation for LoRaWAN

Packet-Level Index Modulation for LoRaWAN

Network resource optimization with reinforcement learning for low power wide area networks

Modelo de Otimização de Alocação de Recursos em LoRaWAN para Aplicações de Internet das Coisas

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