【作者】 李超；

【导师】 许荣涛；

【作者基本信息】 北京交通大学 ， 通信与信息系统， 2018， 硕士

【摘要】 随着智能设备快速普及,网络需要承载的终端设备数越来越多,许多新兴的物联网技术方案逐步出现。其中低功耗广域网(Low-Power Wide-Area Networks,LPWAN)凭借超低的设备工作功耗和较低的组网成本,实现超远距离传输,因此受到了广泛关注。而作为众多LPWAN技术中的一员,LoRaTM(LongRange)已成为当前最为普遍应用的物联网专用网络通信技术,发展形势如火如荼。因此本文从LoRa的MAC层协议LoRaWAN本身出发,通过分析LoRaWAN目前存在的痛点,针对当前协议速率自适应算法方面的不合理因素和信道选择技术的缺失,提出更加有效的资源分配策略,以保证海量设备情况下设备接入网络的成功率。本文将从以下两个方面展开具体的研究。第一,研究了 LoRaWAN网络中扩频因子的分配问题。基于LoRaWAN协议中的ACK确认机制,对每个扩频因子上数据包的碰撞概率进行推导,建立碰撞概率的最大值最小化问题,公平性优化不同扩频因子上的碰撞概率。基于此,设计一个基于贪婪算法核心的启发式算法获得每个扩频因子上最佳的设备分配数,根据每个设备的RSSI值(Received Signal Strength Indicator)进行扩频因子的分配。目的是减少整个网络的碰撞率,以支持更多的设备进行通信,提高吞吐量。第二,研究了未知网络环境下多用户信道选择问题。基于LoRaWAN实际信道构建多臂老虎机(Multi-ArmedBandit,MAB)模型,研究了多用户多信道情况下,在信道统计特性完全不知道的情况下达到信道选择方案最优化。文中分别针对基于ε-Greedy算法、上层置信区间和汤普森采样的策略进行研究和分析,并针对汤普森采样策略提出改进算法,通过权衡探索和开发过程(Exploration and Exploitation),对信道选择策略不断进行迭代更新,使得LoRa节点通过一定时长学习过程能够选择最优信道。最后仿真说明,提供的信道接入方案能够在海量接入情况下有效选择数据传输信道,使得数据传输时延能够接近最优解。更多还原

【Abstract】 With the rapid popularization of smart devices,network needs to carry more and more end devices.In order to meet this situation,many Internet of Things technology solutions are gradually emerging.Among all of these solutions,Low-Power Wide-Area Networks(LPWAN),whose end devices can achieve ultra long transmission distance with ultra-low power consumption and low network organizing costs,has received extensive attention.As a member of LPWAN solutions,LoRaTM(Long Range)has become the most commonly deployed IoT private network solution and the development situation is in full swing.Therefore,this paper will focus on the LoRa WAN,which is the MAC layer protocol for LoRa,and analyzes the existing pain points of it.In order to ensure the success of device accessing network under massive access situation,this paper will propose a more effective resource allocation strategy due to unreasonable allocation of spreading factors and channel selection strategies in current protocol.The reset of the article will carry out specific research from the following two aspects.Firstly,the problem of spreading factors allocation in LoRa WAN networks is studied.Based on the ACK acknowledgement mechanism in LoRa WAN protocol,the collision probability of data packets on each spreading factor is deduced.Then,the problem of minimizing the maximum collision probability is established for the fairness to optimize the collision probability on different spreading factors.In this thesis,a heuristic algorithm based on greedy algorithm is designed to obtain the optimal devices allocation number for each spreading factor,and the spreading factor is allocated according to the RSSI(Received Signal Strength Indicator)of each device.All of the effort is to reduce the collision probability of the entire network to allow more devices to communicate and increase the throughput.Secondly,the channel selection for multi users in unknown channel condition will be studied.With the Multi-Armed Bandit model build on the LoRa WAN specification,end device can learn to choose best channel to transmit packets without any information of the channel.The paper focuses on the strategies based on ε-Greedy algorithm and Upper Confidence Bound and Thompson Sampling and will proposed an improved algorithm based on Thompson Sampling.All of the channel selection strategies will be continuously iterated and updated with balancing the exploration and exploitation.As a result,end devices work with these strategies can find the best channel during the process of learning.Finally,the simulation shows that the channel access scheme provided can effectively select the data transmission channel in the case of massive access and the data transmission delay can be close to the optimal solution.更多还原