知识定义多模态网络按需服务体系研究

doi:10.11959/j.issn.1000-436x.2022076

摘要/Abstract

摘要：

针对管理、控制、数据三平面解耦的未来移动通信网络系统，提出了一种知识定义多模态网络按需服务体系架构。该架构仿照生物多态性的原理，将“网络知识”作为贯穿多个平面的“基因”主干，分场景、分层次地提取关键局部网络知识。通过构建逻辑统一的网络知识空间图谱，根据具体业务对特定知识的依赖诉求，不同的局部知识之间可以进行交换并形成有机联动。网络知识将能够面向不同服务场景、跨越多级服务层次、融合多种服务指标，为网络整体的优化管理提供引导和支撑。知识定义多模态网络可以帮助移动通信网络应对复杂多变的业务需求，使最终用户动态而多样化的需求可以得到及时、有效的满足和保障。

关键词: 知识定义网络, 多模态网络, 服务体验, 网络智能化, 人工智能

Abstract:

Given the consideration of future generation mobile communication network systems with decoupled administration, control, and data planes, a knowledge-defined polymorphic network (KDPN) attainable service architecture was proposed.In the proposed architecture, while mimicking the biological polymorphism, “network knowledge” was acted as a genetic core connecting all the planes, where local network knowledge was extracted from different scenarios and layers of the network.By constructing a network knowledge graph with unified logic, and depending on the service requirement for specific knowledge, different local knowledge could be exchanged and collaborated.Network knowledge will provide guidance and foundation for general network optimization and management across different service scenarios, service layers, and service objectives.KDPN will help the communication networks cope with sophisticated and dynamic service demands and provide dynamic and diversified services to general users promptly and effectively with quality of experience assurances.

Key words: knowledge defined networking, polymorphic network, quality of experience, network intelligence, artificial intelligence

中图分类号:

TN91/TN92

王敬宇, 庄子睿. 知识定义多模态网络按需服务体系研究[J]. 通信学报, 2022, 43(4): 71-82.

Jingyu WANG, Zirui ZHUANG. Research on a knowledge-defined polymorphic network attainable service architecture[J]. Journal on Communications, 2022, 43(4): 71-82.

图/表 10

图1

图2

图3

图4

表1

系统参数"

参数	含义
n^M	服务质量指标数量
n^F	服务功能数量
n^R	网络资源数量
n^L	网络设备配置语言数量
n^D	网络需求上限
$m$	服务质量指标向量
$f$	服务功能向量
$r$	网络资源向量
$l$	网络设备配置语言向量
G(V,E)	网络智能管理域域间关系图
H^v(U,O)	网络智能管理域域内关系图
$Ψ^{v}$	域间知识
$Θ^{v}$	域内知识
$h_{v}^{t}$	系统隐含状态
$k_{v}^{t}$	知识特征向量
$Q$	查询向量
$K$	键向量
$V$	值向量
M_t(?)	知识特征生成函数
R (?)	系统性能读出函数

表1

图5

图6

图7

图8

表2

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算法	平均值	标准差
SPF	0.806 0	0.055 2
TE	0.716 2	0.086 0
DRL	0.599 2	0.090 8
KDPN域内	0.530 6	0.076 6
KDPN总体	0.568 3	0.081 3