# Experimental Evaluation of the Packet Reception Performance of LoRa
## 1 Introduction  
In order to make our way of life more efficient, the industrialized world is becoming increasingly automated. Various innovative technologies are constantly being proposed. The Internet of Things (IoT) is currently one of the most popular innovative technologies. The technologies related to the IoT are continuing to develop, and the number of IoT devices is also rapidly increasing. The Ericsson Mobility Report pointed out that IoT connected devices will grow from 7 billion in 2017 to 20 billion in 2023 [1]. At present, the practical application of the IoT has achieved remarkable results in many fields, such as manufacturing, agriculture [2], home furnishing [3], transportation, car networking, and medical treatment [4]. However, in order to connect IoT devices, wireless networks are required to provide reliable communication and a higher energy efficiency coverage [5]. The above requirements have led to a simpler technical solution called the \"Low Power Wide Area Network (LPWAN)\", which can provide low-energy communication over a distance of several kilometers [6, 7]. Therefore, for long-distance applications such as sensor networks, LPWAN technology occupies an absolute dominant position. LoRa [8; 9] is a kind of LPWAN communication technology and is a long-distance wireless transmission solution based on spread spectrum transmission techniques and chirp spread spectrum (CSS) modulation. LoRa's low power consumption, long transmission distance, flexible networking capability, and many other features fit perfectly with the needs of IoT fragmentation, providing a low cost and many connections [10; 11; 12]. Therefore, LoRa is widely deployed in multiple scenarios, such as campuses [13; 14], buildings [15], and forest farms [16], and has broad prospects for future development [17]. Meanwhile, this also results in higher requirements for the performance of LoRa (such as reliability, delay, and energy consumption). In order to improve the communication capability between LoRa nodes and meet the requirements of a wide variety of scenarios, developers need to know the important details of the relationship between physical layer parameters and the packet reception performance of LoRa. LoRa is a chirp-modulated high-order M-ary FSK system. The modulation and demodulation process is described by rigorous mathematical signal processing in [18]. Comparing the results of FSK modulation and LoRa modulation in the additive white Gaussian noise channel shows that the performances of the two are similar. Furthermore, the results of [PERSON] and [PERSON] [19], who compared the performance of LoRa modulation and BPSK modulation in the additive white Gaussian noise channel, demonstrate that LoRa modulation has better interference immunity, because LoRa modulation occupies more bandwidth. However, the authors [18; 19] did not consider the influence of channel coding and the model proposed by the authors [19] only considers the influence of Spreading Factor (SF) parameters. In [20], the authors consider more factors that affect the packet reception performance of LoRa, including the Bandwidth (BW) and Coding Rate (CR). Their results indicated that LoRa technology can provide very robust communication, even when there is a high noise level. However, the authors did not consider a wide enough Signal to Noise Ratio (SNR) range, and the experimental result of the relationship between physical layer parameters and the packet reception performance of LoRa was not sufficiently detailed. As a result, the important details of the relationship between physical layer parameters and the packet reception performance of LoRa are still unclear. Furthermore, LoRa devices have been deployed in various scenarios, and different scenarios will have different requirements for the performance of LoRa. Some scenarios require a higher communication reliability for LoRa devices, such as smart water metering, and others require LoRa devices to use less energy to complete communication, such as smart forest farms.