基于匹配理论的LoRa参数双重匹配优化

Double matching optimization of LoRa parameters based on matching theory

将LoRaWAN中的资源分配设定为扩频因子分配和信道分配的优化问题,特别是在LoRaWAN中有大量连接设备的情况下,以保证有限频谱资源的LoRa用户之间的吞吐量公平性。首先,引入匹配理论,将LoRa用户与信道和LoRa用户与扩频因子视为匹配双方,为了最大化它们的效用,提出了一种基于匹配的信道与扩频因子分配算法MSFCAA。然后,以匹配理论为基础,以最大化效用为目标,以最优化网络信道与扩频因子分配为结果,最大限度地提高LoRaWAN中实现的最小信道容量。同时,还提出一种公平传输时间初始化算法,以保证每组参数的吞吐量公平性。仿真结果表明,公平传输时间初始化算法能获得优于其他分配方案的初始分配结果,基于匹配的信道与扩频因子分配算法能显著提升LoRa网络数据提取率并极大降低网络能耗。

Resource allocation in LoRaWAN is expressed as an optimization problem of spreading factor allocation and channel allocation,especially when there are a large number of connected devices in LoRaWAN,to ensure the fairness of throughput among LoRa users with limited spectrum resources.Firstly,the matching theory is introduced.LoRa users and channels,and LoRa users and spreading factors are used as matching parties to maximize their utility.Therefore,a matching-based channel and spreading factor assignment algorithm is proposed.Based on the matching theory,with the goal of maximizing utility,by optimizing the results of network channel and spreading factor allocation,the minimum channel capacity achieved in LoRaWAN is maximized.A fair airtime initialization algorithm is proposed to ensure the fairness of the throughput of each group of parameters.The simulation results show that the fair airtime initialization algorithm can obtain better initial allocation results than other allocation schemes.The matching-based channels and spreading factors assignment algorithm can significantly increase the LoRa network data extraction rate and greatly reduce network energy consumption.