Loughborough University is a top-ten rated university in England for research intensity (REF2014) and an outstanding 66% of the work of Loughborough’s academic staff who were eligible to be submitted to the REF was judged as ‘world-leading’ or ‘internationally excellent’, compared to a national average figure of 43%. In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College, including tailored careers advice, to help you succeed in your research and future career. Wireless networks need to support 1000 times increase in data traffic by 2020 compared to the 2010 level. To address this crisis, the ultra-dense network (UDN) has become one of the most promising solutions for their ability to provide remarkable regional capacity. However, the true potential of UDN is much more than just providing localised capacity but it offers a platform that allows massive cooperative signal and data processing to help understand the user requirements, make meaningful predictions and more importantly, take proactive actions to address the anticipated traffic fluctuations. This PhD project will focus on two complementary studies of UDNs: 1) to design optimization and signal processing techniques to enable massive signal cooperation. This requires tackling the difficulty of overhead and explores interference to advance signal cooperation. 2) to improve future wireless design by exploring large-scale data cooperation using analytic tools. Specifically, big data will provide guidelines for the design of advanced wireless technologies, such as wireless network virtualization, software defined networking, mobile edge computing, Fog-RAN, etc. The complementary studies in this PhD project will lay the theoretical foundation for delivering, processing and mining wireless big data using UDNs. The candidate is expected to develop new signal processing algorithms and predictive methods using optimization, game theory as well as machine learning and data mining tools to fully explore the massive cooperation opportunities in UDNs.