Net5.5G时代 IP自动驾驶网络白皮书2024

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The industry is experiencing a significant digital transformation, with over 170 countries having released national digital strategies. The digitalization process has accelerated globally, particularly in the Asia-Pacific region, which is ahead by 10 years. The speed of digital transformation in operator and enterprise businesses has increased by 20-25 times compared to previous expectations [14] - By 2030, the total number of global connections is expected to reach 200 billion, transitioning from connecting billions of people to connecting trillions of devices. New business demands such as AR, VR, industrial IoT, and AI model training are driving the need for advanced network connectivity [14] - The 5GtoB market is projected to reach $602 billion by 2025, with higher requirements for network performance metrics such as bandwidth, latency, and reliability. The industry must adapt to meet these demands through enhanced network capabilities [16] - The report emphasizes the importance of automation and intelligence in network operations, with 91% of operators initiating digital automation strategies to enhance efficiency and reduce costs [22] Summary by Sections Section 3: IP Autonomous Driving Network Vision - The vision for the IP Autonomous Driving Network is to create a self-optimizing, self-healing, and self-configuring network that enhances customer experience by minimizing wait times and ensuring zero faults [37] - The network aims to address challenges such as configuration complexity, congestion, and long mean time to repair (MTTR) through automation and intelligent operations [37] Section 4: IP Autonomous Driving Network Architecture - The architecture consists of intelligent network elements, high-definition digital maps, and network operation intelligence, which collectively enable real-time monitoring and management of network resources [52] Section 5: IP Autonomous Driving Network Applications - The network supports various applications, including network construction, maintenance, and optimization, with a focus on providing automated solutions to enhance operational efficiency [9] Section 6: Successful Practices of IP Autonomous Driving Network - Successful implementations include the Guangdong Mobile Net Master FME Copilot application and the simulation verification of digital map configurations by Guangdong Telecom, showcasing practical applications of the autonomous network concepts [10] Section 24: Challenges and Key Technologies - The industry faces challenges such as slow innovation cycles and the complexity of multi-vendor networks, which hinder visibility and operational efficiency. The report highlights the need for advanced technologies like SRv6 and network slicing to address these issues [24][30] Section 32: AI and Big Data - AI technologies are increasingly integrated into telecommunications networks, enhancing operational efficiency and enabling predictive maintenance and intelligent decision-making [32] Section 34: Communication Large Models - The development of communication large models is transforming network operations by improving task efficiency and enabling adaptive learning based on network conditions [34]