Distributed Intelligence in Wireless Networks
Distributed Intelligence in Wireless Networks
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DOI：
10.1109/ojcoms.2023.3265425
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发表时间：
2022-08
影响因子：
7.9
通讯作者：
Xiaolan Liu;Jiadong Yu;Yuanwei Liu;Yue Gao;Toktam Mahmoodi;S. Lambotharan;D. Tsang
中科院分区：
文献类型：
--
作者：
Xiaolan Liu;Jiadong Yu;Yuanwei Liu;Yue Gao;Toktam Mahmoodi;S. Lambotharan;D. Tsang
基于云的解决方案由于相当大的时间延迟、高功耗以及数十亿连接的无线设备和它们在网络边缘产生的通常数亿字节的数据所引起的安全和隐私问题而变得低效。边缘计算和人工智能（AI）技术的混合可以最佳地将资源丰富的计算服务器转移到更靠近网络边缘的地方，这为高级AI应用提供了支持（例如，视频/音频监控和个人推荐系统），通过在需要时在数据生成时对计算进行智能决策，以及分布式机器学习（ML），其有可能避免大数据集的传输和可能存在于基于云的集中式学习中的隐私妥协。此外，部署人工智能技术来重新设计端到端通信正在引起人们的关注，以提高通信性能。因此，人工智能和无线通信的交互产生了一个新的概念，称为原生人工智能无线网络。在本文中，我们全面概述了在原生AI无线网络的保护伞下无线网络中分布式智能的最新进展，重点关注异构网络的分布式学习架构的设计，支持AI的边缘计算，支持分布式学习的通信高效技术，以及AI授权的端到端通信。我们强调了混合分布式学习架构的优势相比，国家的最先进的分布式学习技术。我们总结了现有的研究成果在无线网络中的分布式智能的挑战，并确定潜在的未来机会。
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.