6G通信网络的新兴技术:机器学习方法
Annisa Anggun Puspitasari1, To Truong An1, Mohammed H Alsharif2
1Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
机器学习 (ML) 和其衍生品为优化第六代 (6G) 无线网络中新兴技术提供解决方案. 本研究调查了6G进步的ML,深度学习 (DL) 和强化学习 (RL) 算法.
科学领域:
- 电信工程 电信工程 电信工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 第六代 (6G) 无线网络承诺超可靠的低延迟通信 (URLLC),高数据速率和集成传感,基于第五代 (5G) 的成功.
- 像智能反射表面 (IRS) 和无人机 (UAV) 等新兴技术在优化6G的苛刻要求方面提出了挑战.
- 传统的数学方法在为6G优化这些新技术的复杂性而扎.
研究的目的:
- 为6G提供机器学习 (ML),深度学习 (DL) 和强化学习 (RL) 算法的全面概述.
- 解决关于在6G背景下应用这些AI算法的研究缺口.
- 检查ML算法如何为6G网络要求优化新兴技术.
主要方法:
- 文献审查和现有研究的调查.
- 分析机器学习,深度学习和强化学习算法.
- 探索它们的应用与新兴的6G技术相结合,如IRS,无人机和NOMA.
主要成果:
- 机器学习算法及其衍生品被认为是优化复杂6G系统功能的可行解决方案.
- 该研究强调了人工智能在应对6G新兴技术带来的挑战方面的潜力.
- 机器学习与新兴技术的整合对于实现6G网络愿景至关重要.
结论:
- 机器学习,深度学习和强化学习对于克服6G无线通信的挑战至关重要.
- 人工智能算法和新兴技术之间的协同作用将推动6G网络的发展和成功.
- 这项研究强调了人工智能在实现6G雄心勃勃的目标方面的关键作用.
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