# Cooperation Based Proactive Caching in Multi-Tier Cellular Networks
## 1 Introduction and Background  
Recently, smart phone usage has enormously increased which results in exponential growth of mobile data traffic. This mobile data traffic poses an unbelievable challenge in the radio resource demand [1, 2]. CISCO predicts that mobile traffic will increase eight times from 2015-2020 [3] and would reach 30.6 Exabyte per month by the year 2020 [4]. It is observed that a big chunk of increased data traffic is due to duplicate downloads of famous videos files from the remote server [5]. In the recent era, mobile media users are extensively sharing information and their thoughts via cross net. The mobile social media services such as Facebook and Twitter have also increased the data traffic exponentially [6]. This unbelievable mobile data traffic has brought a burden on the communication networks as these are limited in transmission rate and hardware capabilities. Also the rapid growth of the content on the Web, for example cossetting articles, games or other type of entertaining contents such as videos, audios, etc. have now started problems regarding quality and down loadings for theusers [7, 8]. Thus, proactive caching performs a pivotal role in the upcoming 5G technology. Caching the popular files on a nearby base station and then serving the users from the local cache are now becoming advantageous due to reducing latency and the data traffic load, especially on the back haul. On the other hand, machine learning tools bring a new research area for the modeling and prediction of the user behavior for proactive caching decisions [9]. Recently, the stupendous success of deep learning methods in pattern recognition, images, and natural language have attracted the operators to use them for network optimization [10, 11]. Different machine learning tool can extract information in the data traffic and cache the contents at the local base station [10]. For example, extreme learning machine predicts the popularity of the contents based on the different meta features of the contents [12]. Unsupervised learning such as K-mean clustering and collaborative filtering methods are employed to cluster the users with similar crosseset for the contents and then cache the popular contents accordingly [11, 13]. Similarly adaptive caching scheme optimizes the network performance thereby decreasing the delay and back haul load [12, 13]. In [14], the author suggested the multi-armed bandit algorithm to estimate content popularity and the authors in [15] proposed an extreme-learning machine algorithm for estimation of the future content popularity. Similarly, reinforcement learning methods are employed in cache enabled networks [16, 17, 18]. For example, in [16], a multi-step return actor-critic architecture which is reinforcement learning agent is used for optimizing the cache hit ratio. The information content can be extracted from the data using a (LSTM) deep learning [19]. The contents are cached if the crosseset of the data is known. This decreases the service latency. These contents are most likely requested by the users. Deep CachNet is a proactive caching framework in cellular network using deep learning algorithm [20]. In this caching scheme, a huge amount of data is collected from the end mobile devices of users connected to SBSs. In [21], the author solved the problem of context-aware data caching in the heterogeneous small cell networks using deep learning algorithm. The author in [22], dealt with the problem of minimization average energy cost in cache enabled networks, especially in limited cache memory nodes, using reinforced learning algorithm. Similarly multi-agent multi-armed bandit can be applied to the caching problems regarding the device to device communication. Such design applied Q-learning in order to coordinate the caching decisions [23]. The author of paper [24], strived to jointly minimize the average transmission delay in cache enabled networks by jointly considering scheduling and the caching problem using reinforced leaning and deep learning.