## AI-Enabled 6G Network Challenges
### Network Energy Optimization  
Energy efficiency is critical for the advancement of 6G networks. AI integration is expected to be a crucial technology for increasing network speed, user experience, and resource management. AI-based solutions have the potential to significantly cut energy usage while also improving the overall performance of 6G networks. Furthermore, the integration of wireless communication systems, communications, and the Internet of Things (IoT) will be a critical component of the architecture of 6G networks. Energy efficiency is a major focus of 6G, which also increases security and allows communications to be sent through satellites. Through the combination of cryptographic technologies, satellite communication, and simpler networking for smart cities, the 6G standard provides the groundwork for a future in which wireless communication becomes increasingly secure, sustainable, and energy-efficient [36].  
The transition to AI-enabled 6G networks presents several complex challenges that necessitate innovative solutions. Channel estimation faces difficulties due to THz communication's extreme noise and environmental sensitivity, requiring new methods that balance efficiency and accuracy. The rapid growth of mobile traffic and the increased device density demand sophisticated traffic prediction and classification techniques. High user density and diverse applications necessitate advanced intelligent routing algorithms, while evolving applications require dynamic radio resource management to optimize performance. Distributed AI must address reliability concerns in the decentralized network structure, and new mobility management techniques are essential to handle moving network nodes and satellites. Enhanced cybersecurity measures, including post-quantum encryption and explainable machine-learning models, are vital for detecting botnets and other threats. Finally, AI-driven energy optimization is crucial for enhancing network performance while reducing consumption, and supporting the development of secure, sustainable 6G networks.  
The development of 6G networks brings forward several research gaps that need to be addressed to fully leverage AI-enabled technologies. First, channel estimation faces significant hurdles, due to extreme noise and complex environmental changes, particularly in THz communications and large MIMO systems. There is a critical need for new methods that balance spectrum efficiency and computational simplicity while maintaining accuracy. Further, the rapid increase in mobile traffic and device density requires advanced techniques for network traffic prediction and classification. Intelligent routing poses another challenge, necessitating sophisticated algorithms to manage high user density and sup port ultra-low-latency applications. Radio resource management must evolve to meet the dynamic demands of 6G applications, such as unmanned aerial vehicles and smart cities. The dispersed nature of 6G networks raises reliability concerns, making effective network fault-management essential. Mobility management will also be complex, due to the movement of network nodes and satellites, requiring new strategies for seamless handovers. Furthermore, enhancing network security through advanced botnet-detection methods and AI-driven approaches is vital. Lastly, optimizing energy management using AI techniques is crucial for ensuring the sustainability and efficiency of 6G networks, particularly given the integration of IoT and satellite communications. Addressing these gaps will be pivotal in realizing the full potential of 6G technology.