论文标题
关于非平稳无线电环境中无线电资源管理的深度学习
On Deep Learning for Radio Resource Management in A Non-stationary Radio Environment
论文作者
论文摘要
本文研究了非平稳无线电环境中资源管理的学习方法的实际局限性。我们建议仔细设计的两个学习模型，以支持用户移动性下的速率最大化目标。我们研究了实用系统，例如潜伏期和可靠性对使用深度学习模型最大化的速率最大化的影响。对于在非平稳环境中的常见测试，我们提出了一种通用数据集生成方法，用于在不同的学习模型和传统的最佳资源管理解决方案方面进行基准测试。我们的结果表明，学习模型与培训限制其应用有关的实用挑战。这些模型需要特定环境设计才能达到最佳算法的准确性。
This paper studies practical limitations of learning methods for resource management in non-stationary radio environment. We propose two learning models carefully designed to support rate maximization objective under user mobility. We study the effects of practical systems such as latency and reliability on the rate maximization with deep learning models. For common testing in the non-stationary environment we present a generic dataset generation method to benchmark across different learning models versus traditional optimal resource management solutions. Our results indicate that learning models have practical challenges related to training limiting their applications. The models need environment-specific design to reach the accuracy of an optimal algorithm.
