在5G车辆物联网中智能优先级感知频谱访问:强化学习方法
Adeel Iqbal1, Tahir Khurshaid2, Yazdan Ahmad Qadri1
1School of Computer Science and Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究引入了一种新的强化学习框架,用于车辆网络中的智能频谱管理. 它平衡了诸如吞吐量,延迟和公平性等性能指标,以满足各种交通需求.
科学领域:
- 无线通信网络是无线通信网络.
- 车载物联网 (V-IoT) 是一种物联网.
- 这是下一代蜂网络.
背景情况:
- 有效的频谱接入对于车载物联网 (V-IoT) 系统至关重要.
- 下一代蜂网络需要动态的服务质量 (QoS) 管理.
- 现有的频谱管理解决方案与车辆环境的动态性质作斗争.
研究的目的:
- 提出一种基于强化学习 (RL) 的新型优先意识频谱管理 (RL-PASM) 框架.
- 为高优先级 (HP),低优先级 (LP) 和最佳努力 (BE) 交通类别动态分配频谱资源.
- 在基于RSU的集中控制系统中评估不同RL算法的性能.
主要方法:
- 将频谱管理环境建模为离散时间马尔科夫决策过程 (MDP).
- 使用情境敏感的奖励函数来保护公平的决策 (访问,抢购,共存,移交).
- 将四种RL算法进行比较:Q学习,双重Q学习,深度Q网络 (DQN) 和演员批评 (AC).
主要成果:
- RL-PASM有效地平衡了吞吐量,延迟,公平性和能源效率.
- DQN实现了最高的平均吞吐量,而Q-Learning提供了最低的平均延迟和最高的能源效率.
- 双重Q学习和演员批评保持了高公平性和低中断概率.
结论:
- 在车辆网络中,RL-PASM为智能,优先意识的频谱访问提供了强大而适应性的解决方案.
- 该框架适用于可扩展和资源受限的部署,特别是边缘受限的车辆环境.
- 选择RL算法允许根据特定的网络优先级 (例如,吞吐量与能源效率) 进行量身定制的优化.
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