Distributed intelligence in wireless networks
The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, and security and privacy concerns caused by billions of connected wireless devices and typically zillions of bytes of data they produce at the network edge. A blend of edge computing and Artificial Intelligence (AI) techniques could optimally shift the resourceful computation servers closer to the network edge, which provides the support for advanced AI applications (e.g., video/audio surveillance and personal recommendation system) by enabling intelligent decision making on computing at the point of data generation as and when it is needed, and distributed Machine Learning (ML) with its potential to avoid the transmission of the large dataset and possible compromise of privacy that may exist in cloud-based centralized learning. Besides, the deployment of AI techniques to redesign end-to-end communication is attracting attention to improve communication performance. Therefore, the interaction of AI and wireless communications generates a new concept, named native AI wireless networks. In this paper, we conduct a comprehensive overview of recent advances in distributed intelligence in wireless networks under the umbrella of native AI wireless networks, with a focus on the design of distributed learning architectures for heterogeneous networks, on AI-enabled edge computing, on the communication-efficient technologies to support distributed learning, and on the AI-empowered end-to-end communications. We highlight the advantages of hybrid distributed learning architectures compared to state-of-the-art distributed learning techniques. We summarize the challenges of existing research contributions in distributed intelligence in wireless networks and identify potential future opportunities.
Funding
Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)
Engineering and Physical Sciences Research Council
Find out more...Pervasive Wireless Intelligence Beyond the Generations (PerCom)
Engineering and Physical Sciences Research Council
Find out more...GBSense: GHz Bandwidth Sensing from Smart Antennas to Sub-Nyquist Signal Processing
Engineering and Physical Sciences Research Council
Find out more...Guangzhou Municipal Science and Technology Project under Grant 2023A03J0011
Guangdong Provincial Key Laboratory of Integrated Communications, Sensing and Computation for Ubiquitous Internet of Things
Royal Society under grant IEC01112
U.K. Government Funded Project under the Future Open Networks Research Challenge sponsored by the Department of Science Innovation and Technology
History
School
- Loughborough University London
Published in
IEEE Open Journal of the Communications SocietyVolume
4Pages
1001 - 1039Publisher
Institute of Electrical and Electronics Engineers (IEEE)Version
- VoR (Version of Record)
Rights holder
© The AuthorsPublisher statement
This is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/Acceptance date
2023-03-30Publication date
2023-04-12Copyright date
2023eISSN
2644-125XPublisher version
Language
- en