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Deep learning for Enhancing Smart Wireless Networks: A Review | ||
| Al-Noor Journal for Information Technology and Cybersecurity | ||
| Article 7, Volume 3, Issue 1, June 2026, Pages 75-92 PDF (437.83 K) | ||
| Document Type: Review Article | ||
| DOI: 10.69513/jncs.v3.i1.a7 | ||
| Authors | ||
| Sara Raed* 1; Ahmed Ahmed2; Mohammad Haqqi1 | ||
| 1Computer and Information Engineering Department, Electronics Engineering College, Ninevah University, Mosul, Iraq | ||
| 2Department of Computer Networks and Internet, College of Information Technology, Ninevah University, Mosul, Iraq | ||
| Abstract | ||
| Deep learning has proven to be effective tool for improving the performance of wireless networks, for addressing many networks important problems including routing, security, spectrum sensing, resource allocation, and localization. Furthermore, integrating deep learning tools into networks can greatly increase the network's capacity to adjust to changing into smart environments and improve the overall stability and robustness of the system. As the demand for large network nodes and huge amounts of information, it can be used to determine and identify the network characteristics (such as best path, hotspots node, interference distribution, congestion points, traffic bottlenecks, spectrum availability, etc.) by analyzing a large number of network parameters (such as energy consumption, delay, lifetime, loss rate, packet overhead, etc.). This paper presents a review on recent published research that used different deep learning models to enhance performance of wireless network. In addition, the review focused primarily on the following topics in this field: user localization, routing, security, big data, mobility, network control, and other application. This article aims to help the readers awareness of the most recent proposed models and algorithms in deep learning-driven wireless network, as well as to identify relevant unresolved challenges that could be addressed in future study. | ||
| Keywords | ||
| Artificial intelligence; Deep learning; Wireless Network; Network Management; Neural networks | ||
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