There are vast number of configurable parameters in a Radio Access Telecom
Network. A significant amount of these parameters is configured by Radio Node
or cell based on their deployment setting. Traditional methods rely on domain
knowledge for individual parameter configuration, often leading to sub-optimal
results. To improve this, a framework using a Deep Generative Graph Neural
Network (GNN) is proposed. It encodes the network into a graph, extracts
subgraphs for each RAN node, and employs a Siamese GNN (S-GNN) to learn
embeddings. The framework recommends configuration parameters for a multitude
of parameters and detects misconfigurations, handling both network expansion
and existing cell reconfiguration. Tested on real-world data, the model
surpasses baselines, demonstrating accuracy, generalizability, and robustness
against concept drift.

使用深度生成图神经网络 (GNN) 框架对无线电接入电信网络中的可配置参数进行优化和检测，提高配置精确度，并处理网络扩展和现有单元重配置产生的错误配置。

使用深层生成图神经网络进行移动网络配置推荐

Mobile Network Configuration Recommendation using Deep Generative Graph  Neural Network

Misconfigurations are the major causes of software failures. Existing
configuration validation techniques rely on manually written rules or test
cases, which are expensive to implement and maintain, and are hard to be
comprehensive. Leveraging machine learning (ML) and natural language processing
(NLP) for configuration validation is considered a promising direction, but has
been facing challenges such as the need of not only large-scale configuration
data, but also system-specific features and models which are hard to
generalize. Recent advances in Large Language Models (LLMs) show the promises
to address some of the long-lasting limitations of ML/NLP-based configuration
validation techniques. In this paper, we present an exploratory analysis on the
feasibility and effectiveness of using LLMs like GPT and Codex for
configuration validation. Specifically, we take a first step to empirically
evaluate LLMs as configuration validators without additional fine-tuning or
code generation. We develop a generic LLM-based validation framework, named
Ciri, which integrates different LLMs. Ciri devises effective prompt
engineering with few-shot learning based on both valid configuration and
misconfiguration data. Ciri also validates and aggregates the outputs of LLMs
to generate validation results, coping with known hallucination and
nondeterminism of LLMs. We evaluate the validation effectiveness of Ciri on
five popular LLMs using configuration data of six mature, widely deployed
open-source systems. Our analysis (1) confirms the potential of using LLMs for
configuration validation, (2) understands the design space of LLMbased
validators like Ciri, especially in terms of prompt engineering with few-shot
learning, and (3) reveals open challenges such as ineffectiveness in detecting
certain types of misconfigurations and biases to popular configuration
parameters.

使用大型语言模型进行配置验证的可行性和有效性的初步研究，发展了集成不同语言模型的通用验证框架 Ciri，并评估了其在六个成熟、广泛部署的开源系统的配置数据上的验证效果。