5G Cloud RAN: Edge Computing, Network Slicing, and AI-Based Optimization

Abstract

The fifth-generation (5G) mobile network revolutionizes connectivity through ultra-high data rates, massive device interconnectivity, and low latency. However, its implementation introduces challenges, especially increased energy consumption due to denser base station deployments and higher processing requirements. To address these concerns, this review explores the integration of advanced technologies, including network slicing, Cloud radio access networks (C-RAN), edge computing, and artificial intelligence (AI)-based optimization in 5G systems. C-RAN enhances network scalability and resource efficiency by centralizing baseband processing, while Edge computing lowers latency and improved real-time responsiveness for applications like as immersive media as well as remote medical care by bringing computation closer to end users. Network slicing supports several applications for 5G, such as improved mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), and massive machine-type communications (MMTC), by enabling customized virtual networks over common physical infrastructure. Furthermore, AI techniques empower intelligent resource management and predictive analytics for efficient network operation. This review highlights current architectures, key components, implementation challenges, and practical applications, offering a thorough comprehension of how These technological converge to optimize 5G Cloud RAN deployments.