Passive optical networks (PONs) have become a promising broadband access
network solution. To ensure a reliable transmission, and to meet service level
agreements, PON systems have to be monitored constantly in order to quickly
identify and localize networks faults. Typically, a service disruption in a PON
system is mainly due to fiber cuts and optical network unit (ONU)
transmitter/receiver failures. When the ONUs are located at different distances
from the optical line terminal (OLT), the faulty ONU or branch can be
identified by analyzing the recorded optical time domain reflectometry (OTDR)
traces. However, faulty branch isolation becomes very challenging when the
reflections originating from two or more branches with similar length overlap,
which makes it very hard to discriminate the faulty branches given the global
backscattered signal. Recently, machine learning (ML) based approaches have
shown great potential for managing optical faults in PON systems. Such
techniques perform well when trained and tested with data derived from the same
PON system. But their performance may severely degrade, if the PON system
(adopted for the generation of the training data) has changed, e.g. by adding
more branches or varying the length difference between two neighboring
branches. etc. A re-training of the ML models has to be conducted for each
network change, which can be time consuming. In this paper, to overcome the
aforementioned issues, we propose a generic ML approach trained independently
of the network architecture for identifying the faulty branch in PON systems
given OTDR signals for the cases of branches with close lengths. Such an
approach can be applied to an arbitrary PON system without requiring to be
re-trained for each change of the network. The proposed approach is validated
using experimental data derived from PON system.

本文介绍了一种基于机器学习的波分复用被动式光网络故障检测方法，该方法通过统计和分类处理 OTDR 信号可以有效地实现网络故障检测和定位。

使用机器学习的被动式光网络故障分支识别

Faulty Branch Identification in Passive Optical Networks using Machine  Learning

Radio Access Networks (RANs) for telecommunications represent large
agglomerations of interconnected hardware consisting of hundreds of thousands
of transmitting devices (cells). Such networks undergo frequent and often
heterogeneous changes caused by network operators, who are seeking to tune
their system parameters for optimal performance. The effects of such changes
are challenging to predict and will become even more so with the adoption of
5G/6G networks. Therefore, RAN monitoring is vital for network operators. We
propose a self-supervised learning framework that leverages self-attention and
self-distillation for this task. It works by detecting changes in Performance
Measurement data, a collection of time-varying metrics which reflect a set of
diverse measurements of the network performance at the cell level. Experimental
results show that our approach outperforms the state of the art by 4% on a
real-world based dataset consisting of about hundred thousands timeseries. It
also has the merits of being scalable and generalizable. This allows it to
provide deep insight into the specifics of mode of operation changes while
relying minimally on expert knowledge.

本研究提出一种基于自我监督学习框架的方法，利用自我关注和自我蒸馏技术检测 Performance Measurement 数据中的变化，达到 RAN 网络监测的目的，并在真实数据集上实现了 4% 的性能提升。

自监督变压器架构用于无线接入网络变化检测

Self-Supervised Transformer Architecture for Change Detection in Radio  Access Networks

In the backbone of large-scale networks, origin-to-destination (OD) traffic
flows experience abrupt unusual changes known as traffic volume anomalies,
which can result in congestion and limit the extent to which end-user quality
of service requirements are met. As a means of maintaining seamless end-user
experience in dynamic environments, as well as for ensuring network security,
this paper deals with a crucial network monitoring task termed dynamic
anomalography. Given link traffic measurements (noisy superpositions of
unobserved OD flows) periodically acquired by backbone routers, the goal is to
construct an estimated map of anomalies in real time, and thus summarize the
network `health state' along both the flow and time dimensions. Leveraging the
low intrinsic-dimensionality of OD flows and the sparse nature of anomalies, a
novel online estimator is proposed based on an exponentially-weighted
least-squares criterion regularized with the sparsity-promoting $\ell_1$-norm
of the anomalies, and the nuclear norm of the nominal traffic matrix. After
recasting the non-separable nuclear norm into a form amenable to online
optimization, a real-time algorithm for dynamic anomalography is developed and
its convergence established under simplifying technical assumptions. For
operational conditions where computational complexity reductions are at a
premium, a lightweight stochastic gradient algorithm based on Nesterov's
acceleration technique is developed as well. Comprehensive numerical tests with
both synthetic and real network data corroborate the effectiveness of the
proposed online algorithms and their tracking capabilities, and demonstrate
that they outperform state-of-the-art approaches developed to diagnose traffic
anomalies.

构建了一种在线估计的网络异常映射方法，该方法基于 exponentially-weighted 的最小二乘准则和物流矩阵的核范数，可在实时动态环境下追踪网络流量异常，进而提高网络质量和保障网络安全。