gly, efficient and universal security and privacy preservation frameworks for C-RAN are challenging issue. In addition, the high data traffic M. Alfaqawi et al. generated from the massive number of end-user devices might overwhelm the fronthaul link between the RRH and the BBU in C-RAN. Therefore, H-CRAN was proposed to manage the traffic between SCs or LPN and macro-cell or HPN. However, H-CRAN faces several challenging issues in terms of optimal planning to avoid interference, energy consumption, and demanding low latency and high capacity fronthaul and backhaul links. V-CRAN was proposed to virtualize the E2E resources of 5G networks and thereby provides cost-effective solutions. However, V-CRAN faces several challenging issues in terms of latency and heterogeneity of fronthaul links, unified protocol, security and privacy, resources management and allocation, VMs migration, placement, and management of SDN controllers and network isolation. Trust mechanisms are mandated for V-CRAN to provide security for multiple operators sharing the same resources on the BBU pool. Another challenging issue of the various C-RAN architectures is regarding the high round-trip delay to process the end-user device data by the cloud/BBU pool. This limitation of high delay of C-RAN can be tackled by adopting edge computing paradigms within the C-RAN architecture where part of the data can be processed or stored at the edge servers. Consequently, O-RAN and 5GPPP have adopted edge computing within NG-RAN. 2. Edge RAN: O-RAN and 5GPPP proposed different RAN architectures than the C-RAN provided by IBM. O-RAN proposed six deployment scenarios for the installation of RAN and core functionalities at the edge or regional cloud. Meanwhile, 5GPPP proposed NG-RAN with MEC and several UCs. In addition, in O-RAN and 5GPPP architectures, the BBU is separated into DU and CU. Furthermore, edge computing has been considered in order to provide ultralow latency and real-time processing. We found that the 5G architectural frameworks of O-RAN and 5G