# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 2 Related Work
In [11], online caching based on time-varying content is analyzed to characterize the long-term normalized delivery time, which captures the temporal dependence of the coding latencies accrued across multiple time slots in the SNR regime. Further, the work studies online caching and delivery schemes for serial and pipelined transmission modes across fronthaul and edge network entities. In [12], the cooperative caching problem is formulated as a probability-triggered combinatorial multi-armed bandit problem. Then, an enhanced multi-agent reinforcement learning algorithm is developed to solve the problem. The solution combines user preference and content popularity prediction based on a real dataset to enhance the cache hit rate. Federated learning is also applied in [13] to develop a content popularity prediction framework for D2D communication in F-RAN in an effort to maximize the cache hit ratio. Note that the work considers individual privacy in the content popularity prediction model. Further note that it only utilizes the user's local model in the training process. Furthermore, the work in [14] presents an F-RAN model based on a femto-base station cluster (FBSC) structure to coordinately serve users on the mmWave bands. However, it still relies on microwave bands between fog stations and central units. Overall, the work focuses on resource allocation problem for caching placement and lacks analysis of the caching mechanism including caching delay, network cost, and energy consumption. It rather optimizes the hybrid precoder to reduce the transmission delay as a function of the BS transmit power for different metrics such as the volume of cached content. A joint caching and recommendation policy is proposed in [15] for F-RAN by considering a dynamic request model of various times. The caching problem is formulated as an optimization model and reinforcement learning is leveraged to maximize the net profit of each F-AP. Furthermore, a double deep Q-network is used to allocate optimal caching policy with a content recommendation at reduced complexity. Again, the work here lacks the context of mmWave systems, i.e., incorporating beamforming and channel models. Finally, a content caching strategy for F-RAN is developed in [16] using a federated deep reinforcement learning algorithm to enhance the caching performance in terms of content request delay and cache hit rate. Here, the model learns content popularity at multiple cooperative F-APs, and then a deep Q-network is applied to learn the request content data in each F-AP. However, this work again lacks the context of mmWave operations and the analysis on network cost and energy consumption. Various outcomes have been proposed from the 5G MiEdge project in [1]. First, stochastic optimization and matching theory are leveraged in [17] to propose an online computation offloading method in MEC, while considering offloading requests, channel conditions, user mobility, and computation queues. However, the work lacks the integration of mmWave system and its requirements, and it lacks network function virtualization and content popularity analysis. Service migration is investigated due to user mobility in [18] that considers latency penalty. It proposes a method to resume the established service at a new mobile edge host to maintain service continuity. It aims to overcome service disruption and resource consumption in the backhaul links. However, it suffers from increased signaling when notifying the MS terminal about a new optimal edge host during path configuration. This adds a complexity at the MS to transmit service parameters. Proactive computation caching methods are presented in Reference [19], which considers task popularity, size, and cached resources that can vary from the incoming resource demands. However, the methods lack service delay analysis and implementation of network function virtualization.