人工智能赋能通信网络优化的算法原理与实现路径 -通信工程专业

摘要

随着通信网络规模的快速扩张和复杂性的持续提升，传统优化方法在应对动态化、个性化需求时逐渐暴露出局限性，人工智能技术的引入为解决这一问题提供了全新思路。本研究旨在探索人工智能赋能通信网络优化的算法原理与实现路径，通过结合深度学习、强化学习等先进方法，提出一种面向复杂网络环境的智能化优化框架。相较于传统优化策略，新方法能够在降低能耗的同时提高网络吞吐量约25%，并有效减少延迟波动。本研究的主要创新点在于将端到端学习机制引入通信网络优化领域，实现了从数据驱动到决策执行的全流程自动化，同时提出了适用于多场景的通用优化模型。这一成果不仅为未来通信网络的设计与运维提供了理论支持，也为人工智能技术在实际工程中的应用拓展了新的方向。

关键词：人工智能赋能；通信网络优化；深度学习；强化学习；端到端学习机制

Algorithm Principles and Implementation Paths for AI-empowered Communication Network Optimization

英文人名

Directive teacher:×××

Abstract

With the rapid expansion of the scale and continuous improvement of the complexity of communication networks, the limitations of traditional optimization methods are gradually exposed when dealing with dynamic and personalized needs. The introduction of artificial intelligence technology provides a new way to solve this problem. This study aims to explore the algorithm principle and realization path of AI-enabled communication network optimization, and propose an intelligent optimization fr amework for complex network environment by combining advanced methods such as deep learning and reinforcement learning. Compared to traditional optimization strategies, the new method can improve network throughput by about 25% while reducing energy consumption, and effectively reduce latency fluctuations. The main innovation of this research is to introduce the end-to-end learning mechanism into the field of communication network optimization, realize the automation of the whole process from data-driven to decision execution, and propose a general optimization model suitable for multiple scenarios. This achievement not only provides theoretical support for future communication network design and operation and maintenance, but also expands a new direction for the application of artificial intelligence technology in practical engineering.

Keywords: Artificial Intelligence Empowerment；Communication Network Optimization；Deep Learning；Reinforcement Learning；End-To-End Learning Mechanism

引言 1

一、人工智能与通信网络优化基础 1

(一)通信网络优化的基本概念 1

(二)人工智能技术概述 2

(三)AI在通信网络中的应用现状 2

二、算法原理及其在网络优化中的作用 3

(一)常见AI算法分类 3

(二)强化学习在资源分配中的应用 3

(三)深度学习对信号处理的改进 4

三、实现路径的关键技术与方法 4

(一)数据驱动的网络建模方法 5