Mobile communication technology is evolving rapidly and becoming increasingly ubiquitous, thereby increasing the demand for uplink data-intensive applications (e.g., personal broadcasting and live augmented/virtual reality videos). Recently, to facilitate a cost-effective and smooth transition from 4G to 5G networks, most carriers leverage existing 4G infrastructures using a dual connectivity (DC) feature. DC increases uplink throughput and mobility robustness; however, it also causes unprecedented dynamic fluctuations in radio channels due to the coverage discrepancy between 4G and 5G networks. Thus, in this article, we propose a self-attention-based deep learning model to predict uplink radio resources in 5G DC. We trained the proposed model on commercial 5G DC traffic data from three major carriers in South Korea and obtained an average prediction accuracy of 95.08% under various mobility and cell-load conditions. The proposed model explains the rationale for the obtained predictions by highlighting the parts of the input time-series data that are important to realize accurate prediction. We also demonstrate the usability of the proposed model using a network emulator based on real-world 5G trace data. Extensive evaluations demonstrate that the existing congestion control algorithms can achieve excellent performance when used with the proposed model.

In Information-Centric Networking (ICN), in-network caching is one of the core functions to implement efficient content distribution. As content is served from the network cache, redundant transmission across the same link is reduced and better quality services are provided to the user. However, caching operations (locating cached content, validating/writing content to the cache) may be a large burden on routers. In this article, we substantially reduce the caching overhead of routers by introducing T-Caching, a new in-network caching model in which routers selectively cache some of the most popular content recommended by content providers. In T-Caching, tokens are used 1) to enable routers to maximize caching benefits by simply controlling the amount of content inserted into the cache; 2) to allow content providers to recommend the most popular content as much as routers would actually cache. Our simulation and empirical studies using synthetic data, as well as real-world traces, show that T-Caching allows routers to insert much less than 1 percent of the content into the cache, compared to typical caching mechanisms. Nevertheless, the cache-hit ratio is improved by up to 2~9 times depending on the cache size. We believe that T-Caching can significantly contribute to improving feasibility and performance of in-network caching in ICN.