智能资源分配用于V2V通信,使用频谱 - 能源效率最大化
Chunning Xu1, Shumo Wang2, Ping Song2
1School of Architecture, Urban Planning & Design Institute, Southest University, Nanjing 210096, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究引入了使用多代理深度Q网络 (MDQN) 的车辆对车辆 (V2V) 通信资源分配的新算法. 它提高了车辆互联网 (IoV) 网络的频谱能源效率 (SEE).
科学领域:
- 无线通信是一种无线通信.
- 网络资源分配 网络资源分配
- 人工智能在网络中的作用
背景情况:
- 传统的资源分配算法在车辆互联网 (IoV) 中面临着车辆对车辆 (V2V) 通信的高可靠性和低延迟需求.
- 现有的方法往往无法优化频谱能效 (SEE) 和关键通信约束.
研究的目的:
- 为V2V通信提出一种新的无线资源分配算法,以解决传统方法的局限性.
- 为了在超低延迟和高可靠性的限制范围内最大限度地提高加权的频谱能量效率 (SEE).
- 为了利用5G网络切片技术来增强IoV通信.
主要方法:
- 开发了一个基于多代理深度Q网络 (MDQN) 的算法,将每个V2V链接视为独立代理.
- MDQN的状态空间,动作集和奖励函数是专门为V2V通信环境设计的.
- 使用MDQN的集中训练来确定最佳的神经网络参数,从而实现资源分配策略的分布式执行.
主要成果:
- 拟议的基于MDQN的算法显著提高了IoV网络的整体频谱能效 (SEE).
- 该方案有效地保持了V2V链路负载传输的高成功率,满足了可靠性要求.
- 模拟结果验证了算法在平衡SEE最大化与延迟和可靠性约束方面的有效性.
结论:
- 与传统方法相比,开发的MDQN算法为V2V通信中的无线资源分配提供了优越的解决方案.
- 5G网络切片和MDQN的整合为优化IoV性能提供了有效的框架.
- 这种方法证明了满足未来自动驾驶汽车网络的苛刻通信要求的可行策略.
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