Deep Learning-Driven Intelligent Sensing and Resource Management for 5G Wireless Network Slicing
Abstract
The emergence of 5G is transforming wireless communications, enabling massive connections and diverse application scenarios through technologies such as network slicing. A central challenge is the dynamic and efficient management of network slice resources, particularly under variable traffic, diverse user demands, and complex wireless environments. This article presents models and algorithms utilizing deep learning (LSTM, GAN) and reinforcement learning to advance intelligent wireless network sensing, traffic prediction, channel estimation, and adaptive resource allocation. The proposed approaches are validated by simulation, demonstrating improvements in prediction accuracy, channel estimation performance, and network resource utilization compared to traditional methods. Network slicing, deep learning, 5G, MIMO channel estimation, reinforcement learning.
References
Li, W., & Luan, Q. (2020). 5G network architecture and service scenarios. Wireless Communications Today, 28(3), 45-62.
Putra, S. A., Trilaksono, B. R., Riyansyah, M., Laila, D. S., Harsoyo, A., & Kistijantoro, A. I. (2019). Emerging applications in 5G mobile networks. Journal of Network Technology, 15(2), 112-128.
Zhang, H., Han, X., Wang, R., Li, Z., & Zhou, M. (2021). Network slicing in 5G: Principles and implementation. IEEE Communications Magazine, 59(1), 88-96.
Ji, G., Xiao, G., & Zhang, Y. (2020). Reinforcement learning for dynamic network resource management. IEEE Transactions on Network and Service Management, 17(4), 2234-2246.
Ye, L. (2021). LSTM-based traffic prediction in wireless networks: Methods and applications. Journal of Wireless Communications, 32(5), 234-250.
He, G., Lan, F., Wang, L., Song, T., Pan, Y., & Chen, Z. (2022). Deep learning-based channel estimation for large-scale MIMO systems. IEEE Transactions on Signal Processing, 70(3), 445-459.
Hao, Z., Qiao, Z., Dang, X., Zhang, D., & Duan, Y. (2022). GAN-based deep reinforcement learning for network slice resource allocation. ACM Transactions on Networking, 30(2), 789-807.
Wang, G., Xia, J., Lu, E., & Huang, H. (2020). Channel state information prediction and its impact on resource allocation. IEEE Access, 8, 145678-145692.
Mukherjee, A., Panja, A. K., & Dey, N. (2020). Deep learning methods for wireless network optimization. Computational Intelligence in Telecommunications Networks, 12(4), 567-581.
Fang, W., Zhang, W., Pan, T., Gao, Z., & Ni, Y. (2020). Deep reinforcement learning for network optimization: A survey. Journal of Network Intelligence, 5(3), 201-220.
Downloads
Published
How to Cite
License
Copyright (c) 2025 Journal of Reproducible Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright of the article belongs to Journal of Reproducible Research (JRR) once the paper is ACCEPTED for publication. Author(s) agrees to this terms, during submission.
