Network slicing is a key feature in 5G/NG cellular networks that creates
customized slices for different service types with various quality-of-service
(QoS) requirements, which can achieve service differentiation and guarantee
service-level agreement (SLA) for each service type. In Wi-Fi networks, there
is limited prior work on slicing, and a potential solution is based on a
multi-tenant architecture on a single access point (AP) that dedicates
different channels to different slices. In this paper, we define a flexible,
constrained learning framework to enable slicing in Wi-Fi networks subject to
QoS requirements. We specifically propose an unsupervised learning-based
network slicing method that leverages a state-augmented primal-dual algorithm,
where a neural network policy is trained offline to optimize a Lagrangian
function and the dual variable dynamics are updated online in the execution
phase. We show that state augmentation is crucial for generating slicing
decisions that meet the ergodic QoS requirements.

在本研究中，我们提出了一个灵活、受限的学习框架，可以满足 Wi-Fi 网络中的网络切片要求。具体地，我们采用了一种无监督学习的方法，利用一个状态增强的原始 - 对偶算法，训练离线的神经网络策略来优化拉格朗日函数，并在执行阶段在线更新双变量动力学，以满足遍历的 QoS 要求。

学习切割 Wi-Fi 网络：一种状态增强的原对偶方法

Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach

Resource sharing between multiple workloads has become a prominent practice
among cloud service providers, motivated by demand for improved resource
utilization and reduced cost of ownership. Effective resource sharing, however,
remains an open challenge due to the adverse effects that resource contention
can have on high-priority, user-facing workloads with strict Quality of Service
(QoS) requirements. Although recent approaches have demonstrated promising
results, those works remain largely impractical in public cloud environments
since workloads are not known in advance and may only run for a brief period,
thus prohibiting offline learning and significantly hindering online learning.
In this paper, we propose RAPID, a novel framework for fast, fully-online
resource allocation policy learning in highly dynamic operating environments.
RAPID leverages lightweight QoS predictions, enabled by
domain-knowledge-inspired techniques for sample efficiency and bias reduction,
to decouple control from conventional feedback sources and guide policy
learning at a rate orders of magnitude faster than prior work. Evaluation on a
real-world server platform with representative cloud workloads confirms that
RAPID can learn stable resource allocation policies in minutes, as compared
with hours in prior state-of-the-art, while improving QoS by 9.0x and
increasing best-effort workload performance by 19-43%.

提出了一种 RAPID 框架，它能够在高度动态的操作环境中实现快速的、完全在线的资源分配策略学习，从而有效地共享资源，提高服务利用率和降低所有权成本。RAPID 利用轻量级的 QoS 预测，通过启用领域知识启发技术来实现样本效率和偏差降低，以比先前的工作快几个数量级的速率来指导策略学习，从而实现学习稳定的资源分配策略，提高 QoS 9 倍，并提高了 19-43% 的最佳努力工作性能。

RAPID: 动态公共云环境中快速在线策略学习的实现

RAPID: Enabling Fast Online Policy Learning in Dynamic Public Cloud  Environments

To accommodate diverse Quality-of-Service (QoS) requirements in the 5th
generation cellular networks, base stations need real-time optimization of
radio resources in time-varying network conditions. This brings high computing
overheads and long processing delays. In this work, we develop a deep learning
framework to approximate the optimal resource allocation policy that minimizes
the total power consumption of a base station by optimizing bandwidth and
transmit power allocation. We find that a fully-connected neural network (NN)
cannot fully guarantee the QoS requirements due to the approximation errors and
quantization errors of the numbers of subcarriers. To tackle this problem, we
propose a cascaded structure of NNs, where the first NN approximates the
optimal bandwidth allocation, and the second NN outputs the transmit power
required to satisfy the QoS requirement with given bandwidth allocation.
Considering that the distribution of wireless channels and the types of
services in the wireless networks are non-stationary, we apply deep transfer
learning to update NNs in non-stationary wireless networks. Simulation results
validate that the cascaded NNs outperform the fully connected NN in terms of
QoS guarantee. In addition, deep transfer learning can reduce the number of
training samples required to train the NNs remarkably.

本文提出了一种基于深度学习的资源分配策略，通过优化带宽和传输功率来最小化基站的总功耗，并使用级联结构的神经网络和深度迁移学习来应对无线网络中的非平稳性。实验结果表明，相对于全连接神经网络，在 QoS 保证方面，级联神经网络的性能更好。