分布式计算与边缘智能时代的多址接入
Multiple Access in the Era of Distributed Computing and Edge Intelligence
AI总结：
本文综述了6G网络中下一代多址接入技术及其与边缘计算、空中计算、语义通信、机器学习和数字孪生的共存，旨在提升网络效率与可靠性。
摘要：
本文聚焦于下一代多址接入（NGMA）技术的基础性最新研究与创新，以及其与第六代（6G）无线网络其他关键技术共存的问题。更具体地，我们首先考察了多接入边缘计算（MEC），这对于满足网络边缘日益增长的数据处理与计算能力需求至关重要，同时涉及网络切片。随后，我们探讨了空中（OTA）计算，该方法被认为能够快速高效地计算各种函数。我们还研究了语义通信，其被认定为一种通过关注有意义信息交换来提升通信系统的有效途径，从而最小化不必要的数据并提高效率。本文还回顾了机器学习（ML）与多址接入技术之间的相互关系，重点涉及联邦学习、联邦蒸馏、分割学习、强化学习以及基于ML的多址接入协议的开发。最后，讨论了数字孪生的概念及其在网络管理中的作用，强调物理网络的虚拟复制如何能够带来网络效率与可靠性的提升。
英文摘要：
This paper focuses on the latest research and innovations in fundamental next-generation multiple access (NGMA) techniques and the coexistence with other key technologies for the sixth generation (6G) of wireless networks. In more detail, we first examine multi-access edge computing (MEC), which is critical to meeting the growing demand for data processing and computational capacity at the edge of the network, as well as network slicing. We then explore over-the-air (OTA) computing, which is considered to be an approach that provides fast and efficient computation of various functions. We also explore semantic communications, identified as an effective way to improve communication systems by focusing on the exchange of meaningful information, thus minimizing unnecessary data and increasing efficiency. The interrelationship between machine learning (ML) and multiple access technologies is also reviewed, with an emphasis on federated learning, federated distillation, split learning, reinforcement learning, and the development of ML-based multiple access protocols. Finally, the concept of digital twinning and its role in network management is discussed, highlighting how virtual replication of physical networks can lead to improvements in network efficiency and reliability.
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。
