Edge caching framework in fog based radio access networks through AI in quantum regime

Fog Computing based Radio Access Networks are a promising paradigm for 5th Generation wireless communication technology (5G) having edge devices endowed with some caching and storage capacity, as a key component for reducing caching burden on the cloud server and providing fast access and retrieval at F-UEs in a scenario where IoT based devices requiring ultra-low latency will be used extensively. The amount of static as well as dynamic data requests generated by these real-time applications will be unpredictable and unmanageable shortly causing fronthaul congestion. In order to avoid performance degradation of F-RANs in near future, cache resource allocation strategies to increase cache hit ratio, must be redefined in a further better way. Quantum computing, on the other hand, seems to be the future for every kind of classical computing problem having non-linearity and exponential growth of computation and memory with a linear increase in Quantum bits due to its parallelism. In this paper, AI has been engaged in an attempt to enhance the caching capability in F-APs by updating caching content intelligently in quantum regime, accelerating computational speed and facilitating limited storage concerns. To validate our proposed framework, certain simulations are carried out in MATLAB. The results show an inevitable outcomes for F-RANs performance up gradation.