Long-range sub-GHz technologies such as LoRaWAN, SigFox, IEEE 802.15.4, and DASH7 are increasingly popular for academic research and daily life applications. However, especially in the European Union (EU), the use of their corresponding frequency bands are tightly regulated, since they must confirm to the short-range device (SRD) regulations. Regulations and standards for SRDs exist on various levels, from global to national, but are often a source of confusion. Not only are multiple institutes responsible for drafting legislation and regulations, depending on the type of document can these rules be informational or mandatory. Regulations also vary from region to region; for example, regulations in the United States of America (USA) rely on electrical field strength and harmonic strength, while EU regulations are based on duty cycle and maximum transmission power. A common misconception is the presence of a common 1% duty cycle, while in fact the duty cycle is frequency band-specific and can be loosened under certain circumstances. This paper clarifies the various regulations for the European region, the parties involved in drafting and enforcing regulation, and the impact on recent technologies such as SigFox, LoRaWAN, and DASH7. Furthermore, an overview is given of potential mitigation approaches to cope with the duty cycle constraints, as well as future research directions. more and more relevant. For example, LoRa duty cycle limitations already impacts, among others, the throughput of the downlink communication, the (un)availability of acknowledgements, the feasibility of over the air firmware upgrades, geolocation inaccuracies, and scalability [1][2][3][4]. Several models predict that the probability of duty cycle violations during downlink communication will further increase, up to 20% for SigFox and 15% for LoRaWAN [5]. Similar impacts are expected for other technologies operating in sub-GHz radio frequency bands.However, despite the large impact of these regulations, many researchers are unaware of the exact limits and are not aware of mitigation techniques they can apply. Various institutes have each implemented regulations on the availability of radio spectrum for SRDs and their usage restrictions. This fragmentation causes confusion and misconceptions for researchers and manufacturers alike.
Sub-one GHz technologies are getting utilized for different applications in home automation and smart cities, due to their long coverage range and support for large number of end devices. Due to different technologies used by different applications and device vendors, the impact of each technology on each other needs to be assessed in environments where multiple technology are used and most of the time they are coexisting with each other. In this paper, we show an inter-technology interference measurement setup for LoRa, Sigfox, Z-Wave and IO Home Control. In the setup, the packet transmissions are controllable in time and frequency, that makes it possible to test and evaluate various interference scenarios. From the designed and implemented setup, we evaluate the impact of the sub-GHz technologies (Sigfox, Z-wave, and IO Home Control) on LoRa. The results show that there is a significant loss (up to 20%) and a relatively lower loss (up to 12-15%) under Sigfox and Z-Wave or IO Home Control, respectively, when the interferer starts during the preamble and header time. Losses are practically zero if the interferer starts during the payload time.
This paper presents a Convolutional Neural Network (CNN) approach for classification of low power wide area network (LPWAN) technologies such as Sigfox, LoRA and IEEE 802.15.4g. Since the technologies operate in unlicensed sub-GHz bands, their transmissions can interfere with each other and significantly degrade their performance. This situation further intensifies when the network density increases which will be the case of future LPWANs. In this regard, it becomes essential to classify coexisting technologies so that the impact of interference can be minimized by making optimal spectrum decisions. State-of-the-art technology classification approaches use signal processing approaches for solving the task. However, such techniques are not scalable and require domain-expertise knowledge for developing new rules for each new technology. On the contrary, we present a CNN approach for classification which requires limited domain-expertise knowledge, and it can be scalable to any number of wireless technologies. We present and compare two CNN based classifiers named CNN based on in-phase and quadrature (IQ) and CNN based on Fast Fourier Transform (FFT). The results illustrate that CNN based on IQ achieves classification accuracy close to 97% similar to CNN based on FFT and thus, avoiding the need for performing FFT.
This paper demonstrates a Convolutional Neural Network (CNN) based mechanism for identification of low power wide area network (LPWAN) technologies such as LoRA, Sigfox, and IEEE 802.15.4g. Since the technologies operate in unlicensed bands and can interfere with each other, it becomes essential to identify technologies (or interference in general) so that the impact of interference can be minimized by better managing the spectrum. Contrary to the traditional rule-based identification mechanisms, we use Convolutional Neural Networks (CNNs) for identification, which do not require any domain expertise. We demonstrate two types of CNN based classifiers: (i) CNN based on raw IQ samples, and (ii) CNN based on Fast Fourier Transform (FFT), which give classification accuracies close to 95% and 98%, respectively. In addition, an online video is created for demonstrating the process [1].
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