Q-Learning Aided Resource Allocation and Environment Recognition in LoRaWAN With CSMA/CA
Q-Learning Aided Resource Allocation and Environment Recognition in LoRaWAN With CSMA/CA
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DOI：
10.1109/access.2019.2948111
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Fujii, Takeo
中科院分区：
文献类型：
--
作者：
Aihara, Naoki;Adachi, Koichi;Fujii, Takeo
物联网（IoT）时代由于其密集部署，无线节点之间的相互干扰是一个关键因素。由于其大的覆盖区域，无线节点可能无法检测作为低功率广域（LPWA）标准之一的远程广域网（LoRaWAN）中的其他节点的正在进行的通信。这会导致数据包冲突。LoRaWAN节点之间的数据包冲突会显著降低网络性能，例如数据包传输率（PDR）。此外，如果发生数据包冲突，LoRaWAN节点必须重新封装数据包，耗尽其有限的电池电量。因此，从网络性能和网络寿命的角度来看，LoRaWAN节点之间的相互干扰管理非常重要。然而，由于其庞大的网络规模，很难明确理解每个LoRaWAN节点周围的无线信道环境，例如其他LoRaWAN节点之间的关系。因此，在本文中，我们利用强大的机器学习技术。学习LoRaWAN节点周围的无线环境，并将这些知识用于资源分配，以提高PDR性能。在所提出的方法中，在LoRaWAN系统中采用Q学习，并且将成功接收的数据包的数量的加权和视为Q奖励。网关（GW）分配资源以最大化该Q奖励。考虑LoRaWAN的数值结果表明，与随机资源分配方案相比，所提出的方案可以将平均PDR性能提高约20。
The mutual interference among wireless nodes is a critical factor in the Internet-of-Things (IoT) era due to its dense deployment. Due to its large coverage area, wireless nodes may not be able to detect the on-going communication of other nodes in a long range wide area network (LoRaWAN), which is one of the low power wide area (LPWA) standards. This results in packet collision. The packet collision among LoRaWAN nodes significantly deteriorates network performance functions such as packet delivery rate (PDR). Furthermore, if packet collision happens, LoRaWAN nodes must retransmit packets, draining their limited battery power. Thus, mutual interference management among LoRaWAN nodes is important from the perspectives of both network performance and network lifetime. However, due to its large network size, it is difficult to explicitly comprehend the wireless channel environment around each LoRaWAN node, such as the relation among other LoRaWAN nodes. Thus, in this paper, we utilize the powerful machine learning technique. The wireless environment around LoRaWAN nodes are learned, and the knowledge is utilized for resource allocation in order to improve PDR performance. In the proposed method, Q-learning is adopted in a LoRaWAN system, and the weighted sum of the number of successfully received packets is treated as a Q-reward. The gateway (GW) allocates resources to maximize this Q-reward. The numerical results considering LoRaWAN elucidate that the proposed scheme can improve average PDR performance by about 20 compared to the random resource allocation scheme.