AI in 6G: Energy-Efficient Distributed Machine Learning for Multilayer Heterogeneous Networks
This work addresses energy efficiency for 6G network operators and users, but it appears incremental as it builds on existing HetNet and collaborative learning concepts.
The paper tackles the challenge of high computational and energy costs of machine learning tasks in 6G heterogeneous networks by proposing a novel layer-based HetNet architecture that distributes ML tasks across network layers and entities, using multiple access schemes and D2D communications to enhance energy efficiency.
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.