# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 2 Related Work  
_Data-Driven User Resource Allocation and Traffic Prediction in 5G Networks_  
In recent years, data-driven methods for user resource allocation in 5G networks [4, 5, 6, 7, 8, 9, 10] have started to appear more often in research than mathematical approaches [11, 12, 13], with a variety of ML architectures being employed for this task. Deep neural networks have been utilized in [7, 9] for user allocation in Non-Orthogonal Multiple Access (NOMA) 5G networks and to minimize system delay in 5G networks, respectively. In contrast, traditional ML techniques based on decision trees and K-means clustering show promising results over 5G resource allocation in [14]. CNN-based architectures have also been employed to optimize user allocation [5, 8]. In [5], the problem of resource allocation in small- and large-scale base stations comes down to an image segmentation task, whereas in [8], small-scale channel information, such as the status of the channel, is exploited to reduce time consumption. Furthermore, Recurrent Neural Networks (RNNs) demonstrate significant efficacy in facilitating 5G user allocation tasks. For example, in [6], a Long Short-Term Memory (LSTM) network, along with a Deep Reinforcement Learning (DRL) model combined with a convex optimization algorithm, was utilized for dynamically allocating user and power resources in 5G TV broadcasting services. Similarly, in [10], a DRL algorithm was introduced to perform energy-efficient user allocation in edge computing and the Industrial Internet of Things in 5G networks, while an RL-based method for dynamic resource allocation to improve QoS of end-users, was proposed in [4]. Other works that utilize DRL for 5G user resource allocation for network slicing are [15, 16]. Akin to user resource allocation works, approaches to optimize 5G user traffic prediction use deep and ML methods to tackle the increasing demand for wireless access. Therefore, approaches span from traditional ML approaches [17] and DL approaches such as RNNs (e.g., LSTM [18, 19, 20]) to leverage the temporal dependencies in user traffic data to state-of-the-art Graph Neural Networks (GNNs) [21, 22], which exploit spatiotemporal features (i.e., spatial data refer to base station topology) to achieve accurate predictions. More specifically, in [18], a smoothed LSTM model trained on 5G data pre-processed by the auto correlation function was compared against other deep learning models, such as a CNN and Gated Recurrent Unit (GRU), showing promising results for traffic prediction. Similar results are observed in [20], where a hybrid RNN-CNN model exploiting geolocation user data performed better over traditional ML and other RNN methods. In [19], a LSTM-based framework is used, where the resource optimization problem is tackled by either a short-term or a long-term approach. Traffic prediction can also be utilized to facilitate user resource allocation [6, 9]. For instance, in [6], an LSTM network performed traffic prediction on multicast services and was utilized for pre-resource allocation. In [23] a framework to jointly optimize base station activation and user association under traffic uncertainty conditions in ultra-dense networks is presented. Moreover, the authors of the work in [24] propose a DL methodology to enable user-centric end-to-end Radio Access Network slicing. Finally, in [25], an adaptive learning framework, i.e., a transfer learning method, to tackle user traffic prediction problems and enhance the distribution of network resources is developed. In this study, we detail a framework for user traffic prediction that entails resource allocation by employing a fully data-driven approach based on sophisticated ML models. A problem underexplored in the aforementioned works, our framework explores the prediction of long-term trends in user data while employing computationally inexpensive ML models. Moreover, our framework can also be utilized for dynamic network slicing, considering the impact of environmental factors and user behavior during the learning process of our models.