# Deep Learning Models Applied to Prediction of 5G Technology Adoption
## 1 Introduction
This paves the way for improved and more efficient performance predictions of the adoption of any technology, including 5G, using models generated by artificial intelligence techniques. This opens the door for improved and more efficient performance prediction with the adoption of any technology. Although TAM has been the most successful theory for modelling the adoption of technology by consumers, it is based on subjective judgments and assigns little weight to the particulars of the technology in question [9]. The most common application of deep learning is the problem solving of issues whose potential solutions can be derived from massive amounts of data. The extensive application of deep learning for prediction has contributed to the development of a well-deserved reputation for the quality of the models it generates. The three most commonly used deep learning models are DR, LSTM, and CNN [10]. They are becoming increasingly popular as a result of the flaws that are present in all of the machine learning models that have been developed up until this point [11]. These machine learning models are defined by their weakness as more data is added, which causes their accuracy to degrade, since they have a tendency to have difficulties with overfitting [12]. On the other hand, the circumstances in which deep learning methods are most successful are those in which the dataset is quite extensive [13]. This is another justification for why this current study adopted the use of deep learning for the prediction of 5G technology adoption. * The most important contributions of this work include the application of deep learning to define what the deployment of 5G will require, how simple it is to make predictions about the adoption of 5G, what role each component will play in those forecasts, etc. Consequently, by highlighting deep learning's potential to predict the adoption of 5G, the components for predicting the adoption of 5G, the contribution that each component makes in predicting the adoption of 5G, and the implementation of 5G, this study contributes to the body of work on the potential applications of deep learning by exposing it to the area of 5G adoption. Another significant component of the contribution consists of the logical inferences that are obtained from the prediction models. These inferences show that the DR model and the CNN model can be applied to the problem of predicting the rate at which 5G will be adopted, and that they can do so with a high degree of accuracy if they are used in practice. Additionally, these inferences show that the DR model and the CNN model can be applied to the problem of predicting the rate at which 5G will be adopted. This suggests that organizations that plan to invest in 5G or are considering doing so may find it easier to make predictions now that this research has provided them with the variables and model. It is possible that this will be advantageous to both companies. * The final contribution focuses on the fact that it is now possible to implement a novel method of deep learning that places an emphasis on using channel metrics, context metrics, cell metrics, and throughput data in order to predict the development of 5G networks themselves as well as the generation of the elements that determine the adoption of 5G. This indicates that the network capability of 5G is of the utmost importance, and that it should be the primary focus of efforts focused toward the adoption of the technology. The remainder of the paper consists of the following: the second section presents the related work, the third section presents the research methodology, the fourth section presents the results and discussion, and the fifth section concludes the work.