# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 1 Introduction  
Millimeter wave (mmWave) bands support wide bandwidth transmission without the need for sophisticated channelization techniques such as multi-carrier and career aggregation. This is attributed to the wide contiguous bandwidth chunks at these bands. This has allowed for mmWave to be considered in the _New Radio_ (NR) standard of 5G standard, in the _Frequency Range_ (FR) 2. A key limitation in mmWave bands here is the high path and penetration losses, along with atmospheric attenuation, oxygen absorption, and sensitivity to blockage. This makes mmWave links susceptible to significant degradation in signal quality. The long separation distances between _mobile station_ (MS) terminals and access points (radio remote heads) add complexity to the design of beamforming and access networks. For beamforming, new designs are required that deem low power consumption and multi-analog beamformers at the MS terminals and digital or hybrid beamformers at the BS for spatial multiplexing and multi-user connectivity. For access networks, it is essential to reduce fronthaul traffic to minimize end-to-end delays for the geographically distributed terminals. Solutions here include _cloud-radio access networks_ (C-RANs) that allow for a centralized _baseband unit_ (BBU) pool that processes various _network functions_ (NFs). These NFs are migrated from the _radio remote heads_ (RRHs) that feature limited resources to theC-RAN through fronthaul links, i.e., to benefit from the abundant resources at the cloud core. The C-RAN (BBU) here facilitates centralized scheduling and allocation at the RRHs. One limitation to this centralization structure is the need to collect information from the distributed RRHs, which increases signaling over the fronthaul links. This in turn prolongs the propagation delay for service requests, in addition to an increase in power consumption and cost for the network. An alternative structure is the deployment of F-RAN at the edge of the network, which is more suitable for mmWave operations, as it reduces the separation distances between MS terminals and RRHs. The closer proximity to the MS terminals also reduces power consumption, which promotes the demands power efficiency for mmWave communications. Therefore, it is vital to reduce the fronthaul traffic and deploy NFs at the edge to provide low latency services. One solution here is caching the popular NFs from incoming traffic at fog nodes that are collocated with the RRH. This forms an F-RAN that offers existing copies of the NFs at less processing and storage demands. This eliminates online offloading between the cloud and fog, and instead allows for offline prefetching at the caching content with the popular NFs. As a result, network operators benefit from a reduction in the transmitted power and the fronthaul cost. Furthermore, caching at the fog nodes promotes network densification of mmWave small cells, given the efficient resource utilization that allows for higher network capacity. Overall, the goal is to enable fog nodes with new functionalities, such as virtualization and efficient caching for robust mmWave backhaul networks, that allow for flexible and agile operations. There are only a few studies on caching in mmWave F-RAN that focus on optimization and federated and reinforcement learning to predict content popularity in a time-varying fashion. These studies lack a comprehensive analysis in terms of the network operation, such as cost and energy consumption. Further, there is a pressing need to consider the limited resources offered at the fog node for users that demand ultra-low latency levels. Furthermore, the 5G MiEdge project in [1] addresses the need of combining mobile edge computing (MEC) and mmWave systems, thus facilitating a system termed as mmWave edge cloud (MiEdge). The project aims to develop a 5G ecosystem that combines MiEdge, liquid RAN C-plane, and user/application-centric orchestration.