Testbed Design and Performance Emulation in Fog Radio Access Networks

Abstract

F-RAN, as a promising technology to achieve low latency and high spectral and energy efficiency, has attracted significant attention. Although performance analysis and resource allocation for F-RANs have been well researched, performance emulation based on a testbed is still challenging. In this article, a testbed for F-RANs has been designed and implemented based on OAI. In order to present the prototype implementation and emulation results, a high-definition video acquisition demonstration based on F-RANs has been built. Given that AI-enabled techniques can cope with dynamic network environments and intractable performance evaluation with low complexity, both an edge caching driven redirection mechanism and transmission mode selection have been evaluated, and the corresponding AI-enabled video quality assessment has been analyzed. The emulation results show that HDV acquisition in F-RANs can reduce both end-to-end latency and frame loss rate while maintaining high video quality.