The future 6G network is envisioned to be AI-native, and as such, ML models
will be pervasive in support of optimizing performance, reducing energy
consumption, and in coping with increasing complexity and heterogeneity. A key
challenge is automating the process of finding optimal model architectures
satisfying stringent requirements stemming from varying tasks, dynamicity and
available resources in the infrastructure and deployment positions. In this
paper, we describe and review the state-of-the-art in Neural Architecture
Search and Transfer Learning and their applicability in networking. Further, we
identify open research challenges and set directions with a specific focus on
three main requirements with elements unique to the future network, namely
combining NAS and TL, multi-objective search, and tabular data. Finally, we
outline and discuss both near-term and long-term work ahead.

未来 6G 网络是 AI 原生的，并且机器学习模型将广泛应用于优化性能、降低能耗以及应对不断增加的复杂性和多样性。本文描述和回顾了神经架构搜索和迁移学习在网络领域的最新技术，并针对未来网络领域的三个主要需求，即融合 NAS 和 TL、多目标搜索以及表格数据，识别了开放性的研究挑战和发展方向。最后，我们概述和讨论了近期和长期的工作展望。

面向传输学习的 6G 网络神经架构搜索

Towards Neural Architecture Search for Transfer Learning in 6G Networks

A smart city solution toward future 6G network deployment allows small and
medium sized enterprises (SMEs), industry, and government entities to connect
with the infrastructures and play a crucial role in enhancing emergency
preparedness with advanced sensors. The objective of this work is to propose a
set of coordinated technological solutions to transform an existing emergency
response system into an intelligent interactive system, thereby improving the
public services and the quality of life for residents at home, on road, in
hospitals, transport hubs, etc. In this context, we consider a city wide view
from three different application scenes that are closely related to peoples
daily life, to optimize the actions taken at relevant departments. Therefore,
using artificial intelligence (AI) and machine learning (ML) techniques to
enable the next generation connected vehicle experiences, we specifically focus
on accidents happening in indoor households, urban roads, and at large public
facilities. This smart interactive response system will benefit from advanced
sensor fusion and AI by formulating a real time dynamic model.

提出了一种智能互动应急响应系统，利用人工智能和传感器融合技术，优化城市中家庭、道路和公共设施等三个应用场景中发生事故的实时动态模型，进而将现有的应急响应系统转变为智能化互动系统，提高公共服务和居民生活质量。

智慧城市范围内智能应急交互响应系统的疏散管理框架

Evacuation Management Framework towards Smart City-wide Intelligent  Emergency Interactive Response System

Multi-access-Mobile Edge Computing (MEC) is a promising solution for
computationally demanding rigorous applications, that can meet 6G network
service requirements. However, edge servers incur high computation costs during
task processing. In this paper, we proposed a technique to minimize the total
computation and communication overhead for optimal resource utilization with
joint computational offloading that enables a green environment. Our
optimization problem is NP-hard; thus, we proposed a decentralized
Reinforcement Learning (dRL) approach where we eliminate the problem of
dimensionality and over-estimation of the value functions. Compared to baseline
schemes our technique achieves a 37.03% reduction in total system costs.

我们提出了一种优化资源利用的技术，实现联合计算卸载以减少总计算和通信开销，并构建一个绿色环境。与基准方案相比，我们的技术将总系统成本减少了 37.03%。