강충구 연구실

We are expecting the commercial 5G service available in 2019. It means that our research community already has been one step ahead, now start talking about 6G. According to recent research, it is forecast that at least a 100x network capacity increase will be required to meet the mobile traffic demands in 2020. Moreover, mobile video streaming will account for 72% of the overall mobile data traffic. In order to meet the mobile traffic demands in future, it is essential to change current wireless communication paradigm which is heavily relying on bandwidth to meet capacity requirement. MoCANet (Wireless Caching Access Network) is a new wireless communication network which can increase the capacity of network by minimizing unnecessary wireless transmissions by using storage of terminal without relying on increase of wireless bandwidth. The UEs efficiently process arbitrary signals received in the periphery to selectively store necessary contents with a mutually cooperative and distributed storage technique for sharing a large amount of contents with a limited storage capacity of UEs. Our projects aims at design of MoCANet architecture and system modeling, development of key networking technologies, global standard development to be evolved into beyond 5G & 6G and implementation of a prototype system. Following the success of Deep Learning (DL) in other domains, efforts to incorporate DL techniques to physical layer communication, in particular, are being carried out recently. In this regard, the research interests can roughly be categorized into two, i.e. End-to-End Design approach and Augmenting the existing algorithms with DL. Our lab aims to study how DL techniques can be incorporated to system design and optimization for 5G & Beyond 5G technologies. Motivated by the D