# 5G/B5G mmWave Cellular Networks with MEC Prefetching Based on User Context Information +
###### Abstract  
To deal with recent increasing mobile traffic, ultra-broadband communication with millimeter-wave (mmWave) has been regarded as a key technology for 5G cellular networks. In a previous study, a mmWave heterogeneous network was composed of several mmWave small cells overlaid on the coverage of a macro cell. However, as seen from the optical fiber penetration rate worldwide, it is difficult to say that backhaul with Gbps order is available everywhere. In the case of using mmWave access under a limited backhaul capacity, it becomes a bottleneck at the backhaul; thus, mmWave access cannot fully demonstrate its potential. On the other hand, the concept of multi-access edge computing (MEC) has been proposed to decrease the response latency compared to cloud computing by deploying storage and computation resources to the user side of mobile networks. This paper introduces MEC into mmWave heterogeneous networks and proposes a content prefetching algorithm to resolve such backhaul issues. Context information, such as the destination, mobility, and traffic tendency, is shared through the macro cell to the prefetch application and data that the users request. Prefetched data is stored in the MEC and then transmitted via mmWave without a backhaul bottleneck. The effectiveness is verified through computer simulations where we implement realistic user mobility as well as traffic and backhauling models. The results show that the proposed framework achieved 95% system capacity even under the constraint of a 1 Gbps backhaul link. 20  
In addition, C/U splitting, in which the macro cell BS is always in charge of the control plane (C-plane), has been proposed to prevent instantaneous disconnections due to frequent handovers. This network architecture enhances the system capacity 1000 times [5]. One of the critical challenges for realizing mmWave HetNet is a limitation of the backhaul capacity. Although the mmWave access can provide excellent throughput of several Gbps, a large capacity and expensive backhaul link, such as 10 Gbps, is required to demonstrate its capability in all small-cell sites. According to the worldwide optical fiber line penetration rate [7], there are few countries where sufficient backhaul lines are established other than Japan and South Korea. In the case of a mmWave system being operated under such a low capacity backhaul line, it becomes a bottleneck, and it is impossible to expect a high system rate as an end-to-end system. From the above background, this paper proposes multi-access edge computing (MEC)-enabled content prefetching based on the user context information to fully leverage the mmWave capability. MEC technologies [8; 9; 10] have been investigated in Beyond 5G (B5G) networks that distribute computation and storage capability to the edges of network. This enables offloading computational tasks produced on UE to the MEC, executing heavy processes (e.g., machine learning and games), and saving power consumption on user equipment (UE). Additionally, by installing a virtual machine/container instance of a server in the MEC in advance, users can access the application and obtain data with low latency compared to the cloud when requesting it. Prefetching has also long been studied for the purpose of speeding up web access. Its major challenge is prediction. It has been studied from a variety of approaches, e.g., content-based [11; 12], spatio-temporal locality [13], user behavior [14], wireless perspective [15], and so on. Prefetching is especially effective in extreme networks, such as satellite communication where the transmission latency is significant [16]. As mobile data communications have become more mainstream, such as 5G, their target has shifted to real-time content, such as streaming video [17]. In addition, the emergence of MEC with the 5G mobile network is the best match for those prefetching [18], which is why this research area is still growing.