In wireless communication, Fifth Generation (5G) Technology is a recent generation of mobile networks. In this paper, evaluations in the field of mobile communication technology are presented. In each evolution, multiple challenges were faced that were captured with the help of next-generation mobile networks. Among all the previously existing mobile networks, 5G provides a high-speed internet facility, anytime, anywhere, for everyone. 5G is slightly different due to its novel features such as interconnecting people, controlling devices, objects, and machines. 5G mobile system will bring diverse levels of performance and capability, which will serve as new user experiences and connect new enterprises. Therefore, it is essential to know where the enterprise can utilize the benefits of 5G. In this research article, it was observed that extensive research and analysis unfolds different aspects, namely, millimeter wave (mmWave), massive multiple-input and multiple-output (Massive-MIMO), small cell, mobile edge computing (MEC), beamforming, different antenna technology, etc. This article’s main aim is to highlight some of the most recent enhancements made towards the 5G mobile system and discuss its future research objectives.

Fifth-generation networks efficiently support and fulfill the demands of mobile broadband and communication services. There has been a continuing advancement from 4G to 5G networks, with 5G mainly providing the three services of enhanced mobile broadband (eMBB), massive machine type communication (eMTC), and ultra-reliable low-latency services (URLLC). Since it is difficult to provide all of these services on a physical network, the 5G network is partitioned into multiple virtual networks called “slices”. These slices customize these unique services and enable the network to be reliable and fulfill the needs of its users. This phenomenon is called network slicing. Security is a critical concern in network slicing as adversaries have evolved to become more competent and often employ new attack strategies. This study focused on the security issues that arise during the network slice lifecycle. Machine learning and deep learning algorithm solutions were applied in the planning and design, construction and deployment, monitoring, fault detection, and security phases of the slices. This paper outlines the 5G network slicing concept, its layers and architectural framework, and the prevention of attacks, threats, and issues that represent how network slicing influences the 5G network. This paper also provides a comparison of existing surveys and maps out taxonomies to illustrate various machine learning solutions for different application parameters and network functions, along with significant contributions to the field.

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5G is planned to link not just traditional devices such as tablets and smartphones, but also smart devices, smart homes, autonomous vehicles, and industry 4.0 which significantly increases the amount of traffic over the network. Network function virtualization and software defined networks will be used heavily to create scalably and on-demand 5G architecture using virtual network functions. In this article, we proposed a unique approach to scaling 5G core network resources by predicting traffic load fluctuations using a hybrid model. Most researchers have presented deep learning models to anticipate regular traffic to improve services, however, these recommended models have failed to estimate traffic load during festivals to unexpected changes in traffic conditions. To solve this issue, we introduced CNN+LSTM, a hybrid model that combines CNN, and LSTM to forecast cumulative network traffic across particular intervals to scale up and properly estimate the availability of 5G network resources by leveraging traffic load variations. The suggested model surpasses the other tested deep learning models and existing techniques that forecast the output in both normal and abnormal traffic conditions, according to a comparison of the produced output with existing techniques.

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