无线网络中的分布式智能
Distributed Intelligence in Wireless Networks
摘要：
由于数十亿连接的无线设备以及它们在网络边缘产生的通常数不胜数的数据所引起的相当大的时间延迟、高功耗、安全和隐私问题，基于云的解决方案变得低效。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。因此，人工智能被设想在未来的通信和网络系统中变得原生和无处不在。在本文中，我们全面概述了在原生人工智能无线网络的保护伞下无线网络中分布式智能的最新进展，重点关注原生人工智能无线网络的基本概念，支持人工智能的边缘计算，异构网络的分布式学习架构的设计，支持分布式学习的通信高效技术，以及人工智能支持的端到端通信。我们强调了混合分布式学习架构的优势相比，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。
英文摘要：
The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, security and privacy concerns caused by billions of connected wireless devices and typically zillions 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 large dataset and possible compromise of privacy that may exist in cloud-based centralized learning. Therefore, AI is envisioned to become native and ubiquitous in future communication and networking systems. 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 basic concepts of native-AI wireless networks, on the AI-enabled edge computing, on the design of distributed learning architectures for heterogeneous networks, 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 the state-of-art distributed learning techniques. We summarize the challenges of existing research contributions in distributed intelligence in wireless networks and identify the potential future opportunities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
