  Ultra-dense heterogeneous networks (Ud-HetNets) have been put forward to
improve the network capacity for next-generation wireless networks. However,
counter to the 5G vision, ultra-dense deployment of networks would
significantly increase energy consumption and thus decrease network energy
efficiency suffering from the conventional worst-case network design
philosophy. This problem becomes particularly severe when Ud-HetNets meet big
data because of the traditional reactive request-transmit service mode. In view
of these, this article first develops a big-data-aware artificial intelligent
based framework for energy-efficient operations of Ud-HetNets. Based on the
framework, we then identify four promising techniques, namely big data
analysis, adaptive base station operation, proactive caching, and
interference-aware resource allocation, to reduce energy cost on both large and
small scales. We further develop a load-aware stochastic optimization approach
to show the potential of our proposed framework and techniques in energy
conservation. In a nutshell, we devote to constructing green Ud-HetNets of big
data with the abilities of learning and inferring by improving the flexibility
of control from worst-case to adaptive design and shifting the manner of
services from reactive to proactive modes.
