## 5 Key 6G Technologies
### Other Perspectives  
#### 5.3.1 AI Integration in 6G Networks  
Mobile networks are becoming increasingly intricate and heterogeneous, rendering optimization a formidable challenge. This is where AI steps in as a transformative force, brimming with potential to streamline 6G. Think of it as a versatile toolbox equipped with diverse learning approaches--supervised, unsupervised, and reinforcement learning--ready to tackle tasks spanning the entire network, from fundamental radio control to sophisticated network management [173; 174]. Deep learning, inspired by the intricate workings of the brain, shines in all these learning paradigms [175]. It tackles critical wireless hurdles like dynamically adjusting transmission in massive MIMO systems, precisely predicting and forecasting fading channels, optimizing RF design for efficiency, and implementing intelligent network management strategies [176; 177; 178; 179]. Its reach extends even further, orchestrating mobile edge computing, network slicing, and the adept management of virtual resources [180]. Beyond the prowess of deep learning, other state-of-the-art AI methods, such as transfer learning and federated learning, are showing tremendous promise [173]. Federated learning safeguards data privacy by processing it locally on individual devices and sharing only masked results, enabling model training without compromising sensitive information [173]. While it crafts a universal model, transfer learning specializes in fine-tuning models for specific scenarios with minimal data requirements, enhancing their adaptability [173]. However, AI's contribution to 6G transcends mere network operation. It unlocks the door to providing AI-as-a-service directly to users [181]. Imagine powerful edge resources empowering AI-driven applications like computer vision, speech recognition, or motion control on devices like robots, smart cars, and VR glasses--even with their limited onboard computing capabilities [181]. In essence, AI is poised to revolutionize 6G, not only optimizing its inner workings but also paving the way for a future where potent AI services are readily available at the network edge, transforming user experiences across the board. 3.2 Integration of Perception and Communication Networks in 6G: The Role of Integrated Sensing and Communication (ISAC)  
As one of 6G's key visions, \"full application\" highlights the fusion of perceptual and communication networks. A comprehensive system that can perceive attributes and states across different services, networks, users, terminals, and environmental objects is what is meant by the perceptive network. ISAC stands out as a crucial technology supporting the realization of a unified 6G network. It facilitates the efficient utilization of wireless and hardware resources, creating a symbiotic relationship between perception and communication systems to enhance hardware, spectrum, time, and energy efficiency [182]. Historically, communication and perceptive technologies developed independently. However, the evolution of communication technologies has led to a more interconnected relationship between the two systems. Both systems now share similar high-frequency antennas and large-aperture designs, utilizing comparable data and signal processing techniques [183]. The idea for ISAC originated in the 1960s when information was transmitted from ground-based radars to spacecraft using coded pulses [184]. Recent technological advancements have revived interest in ISAC, prompting theoretical designs and system implementations by scholars worldwide. Researchers have delved into waveform design, signal processing, and the first practical ISAC system utilizing OFDM waveforms [185]. Information-theoretical analyses, applications in vehicle communication scenarios, and surveys of progress and challenges in ISAC design have also been conducted [186; 187; 188; 182].