Deep Learning-Based 5G Networks and IoVs: Advances, Meta-Data Analysis, and Future Direction
Keywords:
5G, cloud-based, energy-efficient, key performance indicators, RANAbstract
The emergence of 5G wireless networks is intensifying competition and driving a growing demand for network capacity to support a multitude of devices running data-intensive applications that rely on uninterrupted connectivity. This escalating need for network capacity is crucial for handling multiple devices simultaneously, and it holds the promise of significantly benefiting evolving business models in the wireless network market, which is striving for increased accessibility. Moreover, the early stage of 5G technology has compounded these challenges, rendering the strategies employed thus far less effective in addressing these newly prominent issues.
Consequently, research endeavors have been concentrated on the utilization of deep learning algorithms to address the challenges confronting 5G networks and the Internet of Vehicles (IoVs) powered by 5G. In this paper, we delve into research exploring the use of deep learning algorithms to resolve issues that arise within 5G mobile networks and the convergence of 5G and IoV, with the aim of tackling the complexities that can arise in this technological intersection. Our survey reveals innovative developments in deploying deep learning models for problem-solving within 5G mobile networks and the 5G-powered Internet of Things. These deep learning algorithms provide solutions for a wide array of areas, encompassing security, energy management, resource allocation, 5G-enabled Internet of Things, mobile networks, and more, all within the context of 5G communication systems. Recent progress has also been made in enhancing and expanding existing taxonomies, as well as introducing new taxonomies, supported by thorough research and presentation. The previous research analyzed and deconstructed the limitations of the techniques, and this article introduces and explores a novel perspective point for addressing those concerns. The previous research looked at the problems of the techniques. We anticipated that our study would pique academics' curiosity in deep learning's real-world applications in 5G networks and point them in the right direction for developing innovative solutions.
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