A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
Source abstract
Abstract
Fifth-Generation (5G) networks deal with dynamic fluctuations in user traffic and the demands of each connected user and application. This creates a need for optimized resource allocation to reduce network congestion in densely populated urban centers and further ensure Quality of Service (QoS) in (5G) environments. To address this issue, we present a framework for both predicting user traffic and allocating users to base stations in 5G networks using neural network architectures. This framework consists of a hybrid approach utilizing a Long Short-Term Memory (LSTM) network or a Transformer architecture for user traffic prediction in base stations, as well as a Convolutional Neural Network (CNN) to allocate users to base stations in a realistic scenario. The models show high accuracy in the tasks performed, especially in the user traffic prediction task, where the models show an accuracy of over 99%. Overall, our framework is capable of capturing long-term temporal features and spatial features from 5G user data, taking a significant step towards a holistic approach in data-driven resource allocation and traffic prediction in 5G networks.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced MIMO Systems Optimization · Image and Video Quality Assessment · Telecommunications and Broadcasting Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical and Computer Engineering, University of Patras, 26504 Rion, Greece
Department of Chemistry, University of Ioannina, 45110 Ioannina, Greece
Computer Engineering and Informatics Department, University of Patras, 26504 Rion, Greece
Funding
Hellenic Foundation for Research and Innovation (H.F.R.I.)
References (37)
Show source-provided references
1. Umar, A., Khalid, Z., Ali, M., Abazeed, M., Alqahtani, A., Ullah, R., and Safdar, H. (2023). A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions. Appl. Sci., 13.10.3390/app132212384 ↗
2. A review of machine learning techniques for enhanced energy efficient 5G and 6G communications10.1016/j.engappai.2023.106032 ↗
3. A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning
4. Kim, Y., Kim, S., and Lim, H. (2019). Reinforcement Learning Based Resource Management for Network Slicing. Appl. Sci., 9.10.3390/app9112361 ↗
5. Deep learning based user association in heterogeneous wireless networks10.1109/ACCESS.2020.3033133 ↗
6. Deep learning-based resource allocation for 5G broadband TV service10.1109/TBC.2020.2968730 ↗
7. Kumaresan, S.P., Tan, C.K., and Ng, Y.H. (2021). Deep neural network (DNN) for efficient user clustering and power allocation in downlink non-orthogonal multiple access (NOMA) 5G networks. Symmetry, 13.10.3390/sym13081507 ↗
8. Deep learning based cooperative resource allocation in 5G wireless networks10.1007/s11036-018-1178-9 ↗
9. An Optimal Resource Allocation in 5G Environment Using Novel Deep Learning Approach
10. Energy efficient resource allocation method for 5G access network based on reinforcement learning algorithm
11. Bouras, C., Caragiannis, I., Gkamas, A., Protopapas, N., Sardelis, T., and Sgarbas, K. (2023, January 11–14). State of the Art Analysis of Resource Allocation Techniques in 5G MIMO Networks. Proceedings of the 2023 International Conference on Information Networking (ICOIN), Bangkok, Thailand.10.1109/ICOIN56518.2023.10049018 ↗
12. Bouras, C., Diasakos, D., Gkamas, A., Kokkinos, V., Pouyioutas, P., and Prodromos, N. (2023, January 26–28). Evaluation of User Allocation Techniques in Massive MIMO 5G Networks. Proceedings of the 2023 10th International Conference on Wireless Networks and Mobile Communications (WINCOM), Istanbul, Turkiye.10.1109/WINCOM59760.2023.10322955 ↗
13. Joint resource allocation, user association, and power control for 5G LTE-based heterogeneous networks10.1109/ACCESS.2020.3007193 ↗
14. Barolli, L., Woungang, I., and Enokido, T. (2025, January 9–11). User Allocation in 5G Networks Using Machine Learning Methods for Clustering. Proceedings of the Advanced Information Networking and Applications, Barcelona, Spain.
15. Deep reinforcement learning based resource allocation for network slicing with massive MIMO10.1109/ACCESS.2023.3296851 ↗
16. Intelligent Resource Allocation via Hybrid Reinforcement Learning in 5G Network Slicing10.1109/ACCESS.2025.3550518 ↗
17. Selvamanju, E., and Shalini, V.B. (2021, January 2–4). Machine learning based mobile data traffic prediction in 5G cellular networks. Proceedings of the 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India.10.1109/ICECA52323.2021.9675887 ↗
18. 5G traffic prediction based on deep learning10.1155/2022/3174530 ↗
19. Intelligent traffic steering in beyond 5G open RAN based on LSTM traffic prediction10.1109/TWC.2023.3254903 ↗
20. Shrestha, A., Sharma, V., Hussein, L., Aishwarya, M., Satyanarayana, A., and Saimanohar, T. (2024, January 19–20). User Mobility Prediction in 5G Networks Using Recurrent Neural Networks. Proceedings of the 2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS), Asansol, India.10.1109/IICCCS61609.2024.10763639 ↗
21. Spatial-temporal cellular traffic prediction for 5G and beyond: A graph neural networks-based approach10.1109/TII.2022.3182768 ↗
22. A Traffic-Aware Graph Neural Network for User Association in Cellular Networks10.1109/TMC.2025.3545464 ↗
23. Joint Optimization of Base Station Activation and User Association in Ultra Dense Networks Under Traffic Uncertainty10.1109/TCOMM.2021.3090794 ↗
24. Matoussi, S., Fajjari, I., Aitsaadi, N., and Langar, R. (2020, January 16–19). Deep Learning based User Slice Allocation in 5G Radio Access Networks. Proceedings of the 2020 IEEE 45th Conference on Local Computer Networks (LCN), Sydney, NSW, Australia.10.1109/LCN48667.2020.9314857 ↗
25. ADAPTIVE6G: Adaptive resource management for network slicing architectures in current 5G and future 6G systems10.1007/s10922-022-09693-1 ↗
26. Ml-based 5g traffic generation for practical simulations using open datasets10.1109/MCOM.001.2200679 ↗
27. (2025, March 30). DeepMIMO. Available online: https://www.deepmimo.net/.
28. Pascanu, R., Gulcehre, C., Cho, K., and Bengio, Y. (2014). How to Construct Deep Recurrent Neural Networks. arXiv.
29. Kingma, D., and Ba, J. (2015, January 7–9). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA.
30. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2023). Attention Is All You Need. arXiv.
31. Bai, S., Kolter, J.Z., and Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv.
32. Konstantoulas, I., Loi, I., Sgarbas, K., Gkamas, A., and Bouras, C. (2024, January 13–15). A Deep Learning Approach to User Allocation in a 5th Generation Network. Proceedings of the PCI ’24: 28th Pan-Hellenic Conference on Progress in Computing and Informatics, Athens, Greece.10.1145/3716554.3716626 ↗
33. Mao, A., Mohri, M., and Zhong, Y. (2023). Cross-Entropy Loss Functions: Theoretical Analysis and Applications. arXiv.
34. Kaplan, J., McCandlish, S., Henighan, T., Brown, T.B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D. (2020). Scaling Laws for Neural Language Models. arXiv.
35. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
36. Informer: Beyond efficient transformer for long sequence time-series forecasting
37. Temporal fusion transformers for interpretable multi-horizon time series forecasting10.1016/j.ijforecast.2021.03.012 ↗