## 1 Introduction  
Fiber-optic technology is an essential part of the Internet and is an area of ongoing interest and development [1]. The rollout of 5G and the upcoming 6G systems, focused on the Internet of Everything (IoE) and related applications, are significant trends in telecommunications [2, 3]. These advancements signal a gradual move away from 4G technology. While 5G and 6G can offer faster internet speeds, they are expected to have higher costs and shorter ranges than 4G. Therefore, finding ways to reduce costs and improve coverage to make 5G and 6G more effective and encourage widespread adoption is crucial. Recently, researchers have made important progress in developing systems for 5G new radio (NR). This includes technologies like power-over-fiber (PoF) [4], radio-over-fiber (RoF) [5], wired-wireless convergence [6], investigations on optimization of energy-efficient configurations [3], and optical wireless communication [2, 4]. These advancements will help make the rollout of 5G NR and future technologies more cost-effective, efficient, and manageable. In 4G applications, beamforming can improve performance and signal quality by directing signals to specific devices. However, many see it as an added feature rather than a necessary part of the setup. Without advanced beamforming techniques, the characteristics of low frequencies can still provide good coverage and performance. In contrast, beamforming becomes crucial in mmW communications, which operate at higher frequencies (over 24 GHz). The shorter wavelengths at these frequencies lead to greater signal loss, especially when obstacles or atmospheric conditions are present. Beamforming helps direct signals, improving coverage and reliability for mmW applications like 5G. Machine learning, as a component of artificial intelligence, presents a viable approach to enhancing network performance and optimizing resource allocation [7, 8, 9, 10]. Specifically, RL techniques can be applied to improve mmW beamforming by concentrating on user-specific needs to achieve superior communication outcomes [11]. However, as the steering angle increases, aligning the beam with the target receiver becomes less efficient [12, 13]. If the receiver moves or changes its distance from the antenna, the ability to maintain good alignment decreases, which is often unrealistic in real-world situations. It has been found that the EVM rises with larger steering angles, leading to weaker network performance [12, 13]. The network may not perform well at the edges of coverage areas served by mmW beamforming. While adding more beamforming could improve coverage, it is not a practical solution. The high costs for the necessary components, along with interference issues from antennas using the same frequency and overlapping coverage areas, make this option impractical. A better solution is to use frequency diversity [14] to place additional antennas around the outer edges of the mmW beamforming area, where coverage gaps are noticeable. O-RAN is a cost-effective choice for 5G NR applications. It allows network operators to connect hardware and software from different manufacturers easily and flexibly. This flexibility comes from O-RAN's use of standardized interfaces and protocols, like open application programming interfaces, which help devices and software work together smoothly [15]. O-RAN helps lower costs and speeds up deployment by using readily available standard equipment that works with commercial off-the-shelf (COTS) technologies. Traditional RANs often have high power use, high operating costs, and poor sharing of processing power because the baseband unit (BBU) and remote RU are located together at the base station and link to the core network through backhaul. O-RAN addresses this issue by separating the base station into two parts: the centralized unit (CU) and the distributed unit (DU) at the center and the RU at the far end, which is linked by a fronthaul [3].