# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 2 Related Work  
Few studies look at the caching problem in F-RAN for mmWave operations. The work in [2] proposes a low complexity subchannel assignment and power control mechanism for mmWave F-RAN that includes caching, user experience constraints, interference suppression, and energy efficiency. The power problem is formulated as an optimization model, and the alternative direction of the multipliers method is leveraged. However, the work here investigates the energy efficiency only and lacks analysis of network delay and cost. Further, the work in [3] accounts for user mobility in transmission scheduling for caching at the fog nodes. The optimal scheduling problem is formulated as a stochastic nonlinear mixed-integer programming, and multi-hop relaying caching is proposed. The work leverages _device-to-device_ (D2D) communication in the hopping process to enable simultaneous transmission in the scheduling of caching at the edge nodes. The analysis is limited to the amount of cached data under different hop numbers, locations, time slots, and transmission power. Another set of caching schemes is proposed for F-RAN, albeit not in the context of mmWave NR operations. For example, the authors in [4] propose a cooperative coded caching method by deploying deep reinforcement learning to search for the optimal content coded caching approach. The search here is applied to every request by the controller in a high-power node in terms of the deep Q network model, i.e., to enhance the probability of successful transmission. Furthermore, in [5], an online caching approach is introduced that considers the time-varying nature of the popular content with a focus on long-term normalized delivery delay, i.e., based on the temporal dependency of the coding times aggregated during multiple time slots in the high signal-to-noise (SNR) ratio regime. The authors in [6] deploy a _cooperative multi-point_ (CoMP) transmission approach at fog nodes with cache modules that serve clustered users. They aim to optimize the minimum weighted _signal-to-interference noise ratio_ (SINR) among all clusters while considering cluster fairness and load balancing in the backhaul. Namely, the work aims to jointly optimize the clustering formulation for increased fairness and multicast beamforming, with consideration for power consumption. The authors in [7] assume that fog access points for a distributed cache cluster that cooperatively serve MS requests in efforts to reduce fronthaul traffic. Here, cache placement is optimized by concatenating an MDS code with a repetition code, i.e., to save energy and bandwidth. Namely, repeating the same packet of some F-APs allows for multicasting over the fronthaul link, which saves energy and bandwidth. In [8], fog computing is used as an intermediate communication interface between the underlying and global tiers of Information-centric Networks (ICN), where content is processed and stored at the fog nodes. The goal here is to reduce the total cached content using content labeling and sharing. The goal here is to reduce the content request delay and enhance the cache hit rate. Another cooperative caching approach in [9] applies graph theory to maximize the offloaded traffic. Specifically, the work formulates the clustering problem while considering cooperative caching and local content popularity. Thereafter, a graph-based approach is proposed to solve the problem. Reference [10] also aims to enhance the cache hit rate in F-RAN by again formulating the edge caching problem as an optimization model to determine the optimal policy for a content popularity prediction algorithm. The algorithm considers the content features and user preferences, as well as an offline user preference learning algorithm based on the online gradient descent and follows the regularized leader method. The goal is to estimate upcoming content popularity at reduced complexity and track the content with spatial and temporal dynamics in time.