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Editorial Energy Efficiency of Machine-Learning-Based Designs for Future Wireless Systems and Networks
Reference Type:
Journal Article
Hossain, Ekram, and Firas Fredj. 2021. “Editorial Energy Efficiency of Machine-Learning-Based Designs for Future Wireless Systems and Networks.” IEEE Transactions on Green Communications and Networking 5 (3): 1005–10. https://doi.org/10.1109/TGCN.2021.3099580
While 5G standards are being developed, research is moving toward designing the next generation of communications (e.g., 5.5G and 6G) which are expected to provide data rates of the order of 1 Tb/s using frequency bands in the range of 100 GHz to 3 THz. In addition to providing massive capacity and connectivity by exploiting new network architectures (e.g., cell-free massive MIMO, integrated terrestrial-aerial-underwater networks), radio transmission technologies (e.g., THz communications) and resource management techniques (e.g., end-to-end network resource slicing), future networks will support new context-aware applications and services (e.g., those based on joint communications and sensing) and provide connected intelligence in the era of Internet-of-Everything.
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