相关概念视频
Multi-input and Multi-variable systems
In the absence...
Short-distance Transport of Resources
Ampere-Maxwell's Law: Problem-Solving
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
Multicompartment Models: Overview
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Collisions in Multiple Dimensions: Problem Solving
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
Chemotaxis and Direction of Cell Migration
您也可能阅读
相关文章
通过共同作者、期刊和引用图与本文相关的文章。
Correction: Ma et al. A Lightweight, Low-Frequency, Broadband Underwater Acoustic Transducer with Ternary Symmetric Excitation: Integrating KNN and Terfenol-D for Enhanced Performance. <i>2026</i>, <i>26</i>, 3645.
Correction: He et al. An Edge-Computing-Based Emotion-Aware Adaptive Lighting System for Intelligent Cockpits. <i>Sensors</i> 2026, <i>26</i>, 3489.
基于Q学习的多代理移动管理,用于mmWAVE蜂系统中的多连接.
1Department of Information and Communication Engineering, Inha University, Inchon 22212, Republic of Korea.
本研究介绍了在毫米波 (mmWave) 蜂系统中用于移动性管理的层次式多代理Q学习方法. 拟议的方法增强了多重连接性,提高了交付概率和光谱效率.
科学领域:
- 无线通信系统无线通信系统
- 移动网络管理 移动网络管理
- 机器学习在电信中的应用.
背景情况:
- 毫米波 (mmWave) 蜂系统面临途径损失和阻塞的挑战,需要先进的移动性管理.
- 大规模的多输入多输出 (MIMO) 系统对于毫米波来说至关重要,但增加了对链路故障的易感性.
- 多连接性对于满足下一代蜂网络的高容量和可靠性需求至关重要.
研究的目的:
- 提出一种新的基于学习的多代理分布式Q流动性管理方案.
- 为了提高毫米波蜂系统的多连接性.
- 通过层次结构来解决模型复杂性和加速学习.
主要方法:
- 一个分层的多代理分布式Q学习算法的开发.
- 模拟使用来自Wireless Insite.site的现实的城市测量数据集.
- 与独立的Q学习和启发式方案进行性能比较.
主要成果:
- 拟议方案在交付概率方面表现得更好.
- 与基线方法相比,观察到更高的光谱效率.
- 层次结构有效地管理复杂性,加快学习过程.
更多相关视频
07:49Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
Published on: November 26, 2019
06:28A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
Published on: August 26, 2018
结论:
- 多代理分布式Q学习方法为毫米波多连接中的移动性管理提供了有效的解决方案.
- 在蜂网络中,等级结构对复杂的学习任务是有益的.
- 拟议方案为可靠和高效的毫米波通信提供了一个可行的策略.
