GNU无线电实现MALIN：“多武装匪徒学习物联网网络”
GNU Radio Implementation of MALIN: "Multi-Armed bandits Learning for Internet-of-things Networks"
摘要：
我们通过以下方式实现物联网网络：一个网关，一个或多个智能（即，学习）对象，嵌入所提出的解决方案，以及仿真来自许多其他对象的无线电干扰的业务生成器。智能对象使用基于无线ALOHA的协议与网关通信，该协议不需要任何特定的学习开销。我们将网络访问建模为一个离散的顺序决策问题，并使用多臂强盗（MAB）学习的框架和算法，我们表明智能对象可以通过使用低复杂度和分散的算法（如UCB 1和Thompson Sampling）来改善其对网络的访问。该解决方案可以以简单且无成本的方式添加到LoRaWAN网络中，只需在部分或所有设备中添加此功能，而无需对网络侧进行任何修改。
英文摘要：
We implement an IoT network 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
