Optimized D-RAN Aware Data Retrieval for 5G Information Centric Networks

The evolution of wireless network services has enabled consumers and intelligent devices to freely exchange information with each other. Mobile users frequently exchange popular contents, resulting in massive increase in the mobile traffic. The redundant mobile traffic can be reduced by archiving the frequently accessed data within a 5G core network or radio access network, and demands for the same content can be readily met without relying on remote servers. In this paper, we propose an eNB/gNB aware data retrieval algorithm along with Liveliness and Size based data Replacement algorithm to refine, rank, and cache the data items efficiently. Data items are selected based on their popularity and cached in D-RAN for efficient data replacement. We have also included a cost-optimized Radar-Based data Retrieval algorithm that helps to find the data nearness in the neighbouring eNBs. In our proposed technique, unique contents are maintained at each end of the cluster to aid in extending content diversity within the cluster. The experimental analysis shows that the proposed model achieves lower latency, lower congestion, and higher cache hit ratio in 5G networks.