# 5G/B5G mmWave Cellular Networks with MEC Prefetching Based on User Context Information +
###### Abstract
In recent years, machine-learning based prefetching has been applied on 5G networks, to predict segment requests, bitrate and user-BS association [19]. The literature [20] investigated the caching performance of MEC-enabled small-cell networks considering the backhaul capability. It should be noted that caching is a technique to reuse already visited data. The technical meaning differs from prefetching, which attempts to cache un-visited data before use. Cellular networks have made remarkable progresses in wireless access rates, and their true performance must now be considered in conjunction with the capacity of the backhaul network. The literature [21] analyzed the throughput performance of a capacity-constrained heterogeneous network where macro and small cells are intensively densely deployed with sharing microwave-band frequency resources. The work in [22] optimized macro/small-cell associations to minimize the packet delay. Limitations in backhaul are considered to be only the transmission latency. The impact on limited backhaul in machine-type communication scenarios was investigated in [23], where only 4G-based macro cells were supported. The authors in [24] tackled progressive resource allocation optimization on mmWave-integrated access and backhaul (IAB) scenario where wired/wireless systems co-exist. The performance of backhaul constrained cellular networks has also been investigated with the content prefetching approach. Content caching and content delivery were jointly optimized in ultra-dense small-cell networks with constrained-backhaul links [25]. This study assumed microwave-band small cells, and thus the impact of the backhaul constraint was relatively relaxed compared to high-capacity mmWave access. The work in [26] proposed a mobility-aware distributed pre-caching strategy for a content-centric vehicular network. Some chunks of the content were distributed to road-side units according to the vehicles' transition probability. The effectiveness was evaluated in terms of the delay time and required backhaul traffic. To realize dynamic content prefetching in wide cellular coverage with mobility prediction, multiple edge coordination is inevitable. Although the recent work in [27] proposed 5G network structure and sequences to support MEC application mobility, it only presented the methodology and did not reveal detailed performance analysis results. The above key contributions related to the content prefetching and cellular network under constrained backhaul are summarized in Table 1.  
This work is motivated by the above background; incorporating mmWave into MEC is expected to enhance both the throughput and latency capabilities, even with limited backhaul capacity. However, its characterization has not been fully disclosed thus far. Our conference paper [28] investigated the fundamental effectiveness of the prefetching and mmWave HetNet where 60 GHz small cells were overlaid on macro cellular systems. This paper is an extension of that study to analyze the impact of forthcoming 5G NR based MEC prefetching that uses a 28 GHz band. The 60 GHz band is expected to be additionally assigned as the B5G spectrum. This paper also discloses that the 60 GHz band is more effective in MEC prefetching in terms of the system capacity, access delay, and energy efficiency compared to the 28 GHz band. Our previous work [29; 30] experimentally demonstrated the effectiveness of MEC prefetching in a simple environment. Other works [31; 32] discussed the effectiveness of MEC from the market perspective involved with private 5G operators, under the requirement of end-to-end latency and backhaul capacity constraints. The key contributions and novelty of this work are summarized as follows:  
* MEC-assisted demand traffic prefetching is introduced to fully utilize the potential of mmWave communication under the constraint of limited backhaul. * The proposed prefetching algorithm is based on the user context information, such as the users' position information, preferences, and so on.