# Deep Learning Models Applied to Prediction of 5G Technology Adoption
## 1 Introduction  
5G is a name that is used to refer to the fifth-generation technological standard for broadband associated with various categories of networks. It comes with speeds that are up to one hundred times faster than those of 4G, which enable it to offer opportunities for businesses and organisations that have never been available before [1]. South Korea became the first country in the world to offer mobile 5G services for commercial use in 2019 [2]. Many industry stakeholders as well as analysts feel that the transition from 4G to 5G networks will constitute a significant technological advancement that will result in a shift in the market for broadband services. This belief is supported by the fact that this transition is currently underway [3]. It is difficult to predict what the future holds for 5G service because the technology has not yet matured to its full potential [4]. Businesses need to wait for research findings into the emerging 5G mobile industry, and then use the findings to inform the development of long-term strategic plans so that they can make the most of the enormous opportunity that presents itself. It is critical to conduct extensive research into the general demand for 5G services, as well as customer preferences and the impact of using 5G technology. \"What is already known in the open literature\" [4; 5; 6; 7; 8; 9] is that as 4G networks continue to expand, they will eventually be unable to meet the ever-increasing demand for high-speed broadband-loving service. Active network investment and high service uptake for4G services are widespread, confirming the prior prediction result obtained by using a multi-generational diffusion model [5]. As a result, it is reasonable to believe that a network administrator should not place an excessive amount of value in adoption predictions. The inability of the telecommunications industry oversight to properly evaluate the implementation on a ground-share basis, as well as an overabundance of competitors and inadequate network service allocation, have all contributed to the plethora of problems that have recently surfaced [6]. Inadequate network service allocation, an excessive number of rivals, and insufficient network management are among the issues [7]. What the literature lacks is the ability to predict the adoption of 5G technology in advance of its eventual implementation for 5G mobile. The utilization of a deep learning model as a prediction model for the rate at which 5G technology will be adopted is required because the world is moving closer to its deployment for 5G mobile. This needs to be carried out as soon as possible. The manner in which this will be accomplished can be determined by reviewing past examples of how different parts of the world have dealt with the rollout of 4G. In order for the transition from 4G to 5G to be successful and for the 5G implementation to have any kind of influence over user behaviour, it is essential to be more efficient in the prediction evaluations, which is why deep learning techniques are adopted for this study. The Ultra-Broadband Forum 2022 is currently planning to release five heavyweight industry white papers that provide insight into the future of ultra-broadband over the next five years, in order to promote and drive the evolution of the ultra-broadband industry's adoption of 5G and to discuss the strategic direction, requirements, and challenges of the ultra-broadband industry [8]. Insight about ultra-trajectory broadband over the next five years can be gleaned from these papers. This factor is useful for making educated guesses regarding the rate of 5G adoption. The study takes into consideration deep learning algorithms as a key prediction paradigm, since they can be used to construct a model that reacts to changes with regards to any amount of data. This study was also motivated by an ever-increasing amount of data as time passes.