# A Survey of Deep Learning for Data Caching in Edge Network
## 2 Data Caching Review
### Layer 2 Caching
The former is based on a large number of users' behaviors, but the individual may prefer specific content categories. For small cells, the preference estimation requires more accurate information, like historical data [28]. In order to capture the temporal and spatial dynamics of user preference, many different deep learning based algorithms are proposed, which will be illustrated in Section 4. Cache dimensioning in Layer 2 Caching has more complicated factors need to be considered, not only including the network topology and content popularity as Layer 1 Caching, but also containing backhaul transmission status and wireless channel features. The proper cache size assignment is studied in the scenario of backhaul limited cellular network [37]. It also provides the closed-form boundary of minimum cache size in one cell case. In the case of dense wireless network, the work in Reference [38] quantifies the minimum required cache to achieve the linear capacity scaling of network throughput. The authors of Reference [39] also consider the scenario of dense networks. They derive the closed-form of the optimal memory size which can reduce the consumption of backhaul capacity, as well as guarantee wireless QoS. According to the number of transmitters and receivers, we divide the content delivery in Layer 2 caching into three categories: one candidate serves one end user, such as unicast and D2D transmission; one candidate serves multiple users, like multicast; coordinated delivery including multiple transmitters serve one or more receivers, like coordinated multi-point joint transmission (CoMP-JT). Once the requested content is cached locally, BS can serve the end user via unicast or the adjacent device shares the contents by implementing D2D transmission. The concurrent transmission has the risk of co-channel interference in densely deployed networks. In the D2D network, link scheduling is introduced to select subsets of links to transmit simultaneously [28]. In order to improve spectral efficiency, multicast is applied in content delivery when serving multiple requests simultaneously with the same content. Therefore, there is a trade-off between spectral efficiency and service delay. For the aim of serving more users in one transmission, as well as higher spectral efficiency, the BS will wait to collect enough requirement for the same content, which makes the first request a long waiting time. An optimal dynamic multicast scheduling is proposed in Reference [40] to balance these two factors. Multicast can also serve multiple requests with different contents. In Reference [41], the authors provide a coded caching scheme which requires the communication link is error-free and each user caches a part of its own content and partial for other users. Then, BS multicasts the coded data to all users. Each user can decode his own requested content by XOR operation between the received data and the precached other users' file. However, the coding complexity increases exponentially as the number of end users grows. The CoMP-JT can improve the spectral efficiency, as well via sharing channel state information (CSI) and contents among BSs, but it also needs high-capacity backhaul consumption for exchanging data. In C-RAN, the BBUs are centralized in the BBU pool, which makes communication among BSs very efficiency. Reference [42] designs CoMP-JT in C-RAN for the purpose of minimizing power consumption with limitations of transmission energy, link capacity and requested QoS.