# Spectrum Demand Forecasting for IoT Services
## 2 Related Work  
In the literature, spectrum and traffic demand prediction is determined with different approaches. On one hand, artificial intelligence methods (AI) in network traffic prediction for proactive network analysis have encountered a wide set of application areas including network monitoring, resource management, planning tasks, and threat detection. In this regard, various techniques have been proposed and studied including neural network-based techniques, data mining, deep learning, and machine learning techniques. In addition, several linear, non-linear and combined models have also been presented. A very comprehensive and descriptive survey on various traffic prediction techniques is presented in [20]. Linear models such as ARMA/ARIMA have been commonly used for network traffic forecasting [21; 22]. Among the non-linear mechanisms, neural networks are the most commonly used and they are claimed as the most precise [23]. Recurrent Neural Networks (RNN) have gained attention for network traffic prediction, and the work in [24] explores different RNN architectures to solve the traffic prediction problem. More recently, combined non-linear and linear methods promise to reach outperforming standard statistical metrics, and in [25], a network traffic predictor based on SA (Simulated Annealing) optimized ARIMA (Autoregressive Integrated Moving Average model)-BPNN (Back Propagation Neural Network) is proposed. The network traffic prediction methods have also become an indispensable part of the cellular resource management system, in particular for the massive deployment of 5G IoT cellular network systems. For instance, authors in [26] have proposed a data-augmentation-based cellular traffic prediction model (ctGAN-S2S), which achieves up to 48.49% improvement of the prediction accuracy compared with reference models on a real-world city-scale cellular traffic dataset. Other interesting work in [27] has proposed a reinforcement learning-based mechanism on a Markov decision process and Monte Carlo Q-learning technique for modeling the network traffic in the implementation of network security and management functions in industrial IoT applications. Additional applications can be also found in the literature regarding novel radio access mechanisms combined with deep reinforcement learning techniques and the RNN traffic prediction model, which tackle the problem of collisions with massive IoT networks [28]. When using AI-based traffic prediction models, the main purpose is to obtain information about the network traffic in short periods of time for network management functions. Most of the cited methods evaluate their predictions over very short time frames because most of the techniques have been applied in traditional networks such as IP backbone and cellular backhaul where the aggregated traffic obeys regular and periodic patterns. On the contrary, IoT traffic flows have a very different nature, as they are produced mainly by machine-type communications. Additionally, the short-term traffic behavior is not relevant for spectrum forecasting, where the main concern is to carefully estimate the amount of constant traffic produced by a peak case, in terms of the number of devices and its data rate. Short-term traffic predictions are out of the scope of this work. On the other hand, long-term spectrum requirement prediction techniques rely on the application of the methodology for calculation of spectrum requirements for the terrestrial component of IMT wireless networks of the ITU [29]. This recommendation provides a systematic approach that incorporates service categories, service environments, radio environments, market data analysis for traffic estimation by using the defined categories and environments, traffic distribution among radio access technology groups (RATGs), required system capacity calculation, and resultant spectrum requirement determination.