Najiullah HAKIM et al., International Journal of Emerging Trends in Engineering Research, 11(5), May 2023, 159 – 167 159 ABSTRACT Wireless networks of the future can take advantage of beamforming techniques in the millimeter wave (mmWave) and terahertz (THz) bands to effectively handle the immense bandwidths required. This opens up a world of possibilities for the advancement of wireless technology and the potential to create even faster and more efficient networks. To achieve directional beamforming gain, it is essential to have a reliable beam management (BM) framework that can track the best uplink and downlink beam pairs using traditional exhaustive beam scans (EBS). However, this requires extensive beam measurement, which can result in a significant overhead, especially for higher carrier frequencies and narrower beams. To tackle this issue, machine learning (ML) algorithms based on artificial intelligence (AI) are being created to detect and understand intricate mobility patterns and environmental changes. This article presents an overview of the current AI- based ML beam tracking (BT) techniques used in mmWave/THz bands for 5G and future networks, highlighting the essential features of an effective beam tracking framework. Key words: mmWaves, Artificial Intelligence, Machine learning, Beam Tracking 1. INTRODUCTION The ever-increasing demand for bandwidth-heavy applications, such as Virtual Reality (VR), Augmented Reality (AR), and Ultra-High Definition (UHD) 3D video is leading to a steady rise in wireless data traffic, doubling every year. This trend is expected to continue, indicating a massive surge in the demand for ultra-high data rates in the foreseeable future [1]. Predictions suggest that by 2030, global data traffic demand could reach up to 5 Zettabytes (ZB) each month, by 2030 with data rates expected to peak at 100 Gbps. However, due to limited availability of spectrum resources poses a significant challenge in meeting the increasing demand for high-bandwidth requirements of next generation wireless communication [2]. The challenge is to provide enough wireless bandwidth to support the growing demand for high- speed data transmission. The upcoming 6th Generation (6G) of wireless technology is expected to provide significantly higher data rates and more reliable connectivity than current legacy systems. The upcoming 6G wireless technology is expected to offer much higher peak data rates of 1 Tbps, which is approximately 50 times faster than the current 5G technology, this improved speed is predicted to enable new applications such as autonomous driving, holographic images, and telemedicine. Additionally, the latency of 6G is expected to be one-tenth of 5G which is 0.1 ms [3]. To fulfill the demanding need for enhanced transmission capacity, there exist two solutions that can meet the stringent data requirements. One solution to address the challenge of limited spectrum resources is to improve spectrum efficiency by utilizing techniques such as large-scale Multiple Input Multiple Output (MIMO) and high-order modulation. The second approach involves techniques such as dual connection, non-orthogonal multiple access (NOMA), and carrier aggregation, which can expand system bandwidth substantially and improve the data service capacity [4]. However, despite the advances made in these technologies, overcoming the bottleneck of wireless bandwidth scarcity remains a challenge. To tackle this problem, researchers are investigating the use of mmWave /THz frequency bands because they provide an abundant amount of bandwidth resources that can fulfill the need for high transmission capacity [5]. Despite its potential benefits, mmWave/THz communication presents some difficulties and obstacles. 1) Limited range of communication: This is because the high frequency of mmWave bands leads to significant attenuation in free space, which ultimately restricts the effective range of communication. Review of Artificial Intelligence Based Beam Tracking Techniques for mmWave 5G and Beyond Networks Najiullah HAKIM 1 , Adnan KAVAK 2 , Halil YIGIT 3 1 Kocaeli University, Department of Electronics and Communication Engineering, İzmit, Kocaeli, Turkey 41001, naj.hakim12@gmail.com 2 Kocaeli University, Department of Computer Engineering, İzmit, Kocaeli, Turkey 41001, akavak@kocaeli.edu.tr 3 Kocaeli University, Department of Information Systems Engineering, İzmit, Kocaeli, Turkey 41001, halilyigit@kocaeli.edu.tr Received Date: March 29, 2023 Accepted Date: April 21, 2023 Published Date : May 07, 2023 ISSN 2347 - 3983 Volume 11. No.5, May 2023 International Journal of Emerging Trends in Engineering Research Available Online at http://www.warse.org/IJETER/static/pdf/file/ijeter081152023.pdf https://doi.org/10.30534/ijeter/2023/081152023