We propose a caching policy that uses a feedforward neural network (FNN) to predict content popularity. Our scheme outperforms popular eviction policies like LRU or ARC, but also a new policy relying on the more complex recurrent neural networks. At the same time, replacing the FNN predictor with a naive linear estimator does not degrade caching performance significantly, questioning then the role of neural networks for these applications.
The authors of @cite_15 study caching at the base stations of a cellular network. In their case the LSTM-NN is used to perform a sentiment analysis of mobile users' traffic to detect their interests and prefetch contents they may like.
@cite_15
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Mobile cellular networks are becoming increasingly complex to manage while classical deployment optimization techniques and current solutions (i.e., cell densification, acquiring more spec­trum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage memory, context-awareness, edge cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: Velocity, voracity, volume, and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul of-floadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users' mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platform and the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10 of content ratings and 15.4 Gbyte of storage size (87 of total catalog size), proactive caching achieves 100 of request satisfaction and offloads 98 of the backhaul when considering 16 base stations. As the rise of the portable devices, people usually access the social media such as Twitter and Facebook through wireless networks. Therefore, data transmission rates significant important to the end users. In this work, we discuss the problem of context-aware data caching in the heterogeneous small cell networks to reduce the service delay and how the device-to-device (D2D) and device-to-infrastructure (D2I) improve the system social welfare. In the data-caching model, we explore three types of cache entities, macro cell base stations, small cell base stations, and end user devices. We propose a long short-term memory (LSTM) deep learning model to perform data analysis and extract information content from the data. By knowing the interest of the data to the cache entities, we can cache the data that will most likely to be requested by the end users to reduce service latency. In simulation, we show our proposed algorithm can efficiently reduce the service latency during 2016 U.S. presidential election where mobile user were urgent to request the election information through wireless networks. Comparing with other mechanisms such as using one-to-many matching algorithm or without D2D communication technology, our proposed algorithm improves significantly on the devices performance and system social welfare. Today’s Internet has witnessed an increase in the popularity of mobile video streaming, which is expected to exceed 3 4 of the global mobile data traffic by 2019. To satisfy the considerable amount of mobile video requests, video service providers have been pushing their content delivery infrastructure to edge networks—from regional content delivery network (CDN) servers to peer CDN servers (e.g., smartrouters in users’ homes)—to cache content and serve users with storage and network resources nearby. Among the edge network content caching paradigms, Wi-Fi access point caching and cellular base station caching have become two mainstream solutions. Thus, understanding the effectiveness and performance of these solutions for large-scale mobile video delivery is important. However, the characteristics and request patterns of mobile video streaming are unclear in practical wireless network. In this paper, we use real-world data sets containing 50 million trace items of nearly 2 million users viewing more than 0.3 million unique videos using mobile devices in a metropolis in China over two weeks, not only to understand the request patterns and user behaviors in mobile video streaming, but also to evaluate the effectiveness of Wi-Fi and cellular-based edge content caching solutions. To understand the performance of edge content caching for mobile video streaming, we first present temporal and spatial video request patterns, and we analyze their impacts on caching performance using frequency-domain and entropy analysis approaches. We then study the behaviors of mobile video users, including their mobility and geographical migration behaviors, which determine the request patterns. Using trace-driven experiments, we compare strategies for edge content caching, including least recently used (LRU) and least frequently used (LFU), in terms of supporting mobile video requests. We reveal that content, location, and mobility factors all affect edge content caching performance. Moreover, we design an efficient caching strategy based on the measurement insights and experimentally evaluate its performance. The results show that our design significantly improves the cache hit rate by up to 30 compared with LRU LFU. We consider the problem of caching in next generation mobile cellular networks where small base stations (SBSs) are able to store their users' content and serve them accordingly. The SBSs are stochastically distributed over the plane and serve their users either from the local cache or internet via limited backhaul, depending on the availability of requested content. We model and characterize the outage probability and average content delivery rate as a function of the signal-to-interference-ratio (SINR), base station intensity, target file bitrate, storage size and file popularity. Our results provide key insights into the problem of cache-enabled small cell networks.