# Spectrum Demand Forecasting for IoT Services
## 2 Related Work
The methodology is applicable to packet switch-based traffic, it can accommodate multiple services and it is mainly oriented to digital cellular mobile systems, IMT-2000 systems, IMT-Advanced systems, radio LANs, and Digital mobile broadcasting systems. This methodology has often been applied for spectrum requirement estimation of mobile cellular systems for radio spectrum planning purposes and policy-making processes. For instance, the work in [30] adopted the ITU spectrum forecasting method for spectrum allocation in the Chinese railway mobile communication system, but this work is not related to IoT spectrum. Only a few works have been reported in the literature regarding the application of the methodology for IoT services. In [31], authors aim to estimate spectrum requirements for the deployment of IoT services in Jakarta Smart City by proposing two general scenarios according to the service data rate (50/100 kbps) and frequency channel separation (200/400 kHz). Spectrum estimation of their method predicted for 2021 a required spectrum of 18.67 MHz and 19.91 MHz, respectively, for each scenario. Similarly, the work in [32] has applied the methodology for spectrum demand calculation for smart transportation services powered by Long Term Evolution (LTE) mobile technology, in this work, the authors defined two scenarios according to the services provided in the transportation system, one for the closed-circuit television (CCTV) system and another for the global positioning system (GPS). Results show that by 2025, the required spectrum for each scenario will be 0.014 kHz and 84.290 kHz, respectively. In [33], spectrum needs are assessed for mobile telemedicine services in Indonesia supported by LTE technology, the prediction obtained is 165.47 kHz by the end of 2025 on the frequency band of 1800 MHz. The work in [34] adapted the ITU-R M.2290 spectrum requirement estimation model [35] for proposing a 5G spectrum estimation framework based on a heuristic game theory called a coalition formation algorithm, applied in Taiwan. In particular, the study is centered to address the minimum spectrum requirements for individual telecommunications operators considering the sub-6 GHz frequency bands and the 4G/5G network technologies. In this work, the spectrum requirement estimation for standalone 5G services is 800 MHz considering all the national operators. The work in [36] presents a study for analyzing the conditions of bandwidth demand for certain IoT applications in Mexico. In this work, the calculation of the bandwidth needs is computed using population densities in different regions of Mexico in order to obtain the density of IoT end-devices, which combined with the communication range of IoT networks, for urban, semi-urban and rural areas, allows to estimate a traffic demand and finally the required spectrum bandwidth. Spectrum requirements results are presented for the different regions by the end of 2023, where highly dense urban areas as is the case of Mexico City presents an estimated spectrum demand of 13.64 MHz considering a radio communication coverage cell of 2 km, this result can eventually change by increasing the cell coverage area. According to the previously cited works, two main hints should be highlighted: First, to the best knowledge of the authors, the proposed methods do not include a clear definition of spectrum contention scenarios and IoT services, the calculation of an IoT market penetration, a wider identification of communication IoT technologies, a clear distinction of the different frequency bands where IoT services could be deployed and calculation adjustments for future technologies that may increase the spectrum efficiency. The lack of these aspects makes these methodologies non-general and difficult to replicate in other countries. Finally, due to the different assumptions and procedures used for spectrum calculation, none of these works can really be used as a reference framework for spectrum demand comparison with this research.