  Adept network management is key for supporting extremely heterogeneous
applications with stringent quality of service (QoS) requirements; this is more
so when envisioning the complex and ultra-dense 6G mobile heterogeneous network
(HetNet). From both the environmental and economical perspectives,
non-homogeneous QoS demands obstruct the minimization of the energy footprints
and operational costs of the envisioned robust networks. As such, network
intelligentization is expected to play an essential role in the realization of
such sophisticated aims. The fusion of artificial intelligence (AI) and mobile
networks will allow for the dynamic and automatic configuration of network
functionalities. Machine learning (ML), one of the backbones of AI, will be
instrumental in forecasting changes in network loads and resource utilization,
estimating channel conditions, optimizing network slicing, and enhancing
security and encryption. However, it is well known that ML tasks themselves
incur massive computational burdens and energy costs. To overcome such
obstacles, we propose a novel layer-based HetNet architecture which optimally
distributes tasks associated with different ML approaches across network layers
and entities; such a HetNet boasts multiple access schemes as well as
device-to-device (D2D) communications to enhance energy efficiency via
collaborative learning and communications.
