论文标题
使用简单的深钢筋学习方法适应Wi-Fi率
Wi-Fi Rate Adaptation using a Simple Deep Reinforcement Learning Approach
论文作者
论文摘要
最近的Wi-Fi修订的复杂性日益增加，这使最佳速率适应（RA）成为挑战。由于配置参数的较大组合以及无线通道的可变性，因此使用经典算法或启发式模型来解决RA变得不可行。已经提出了基于机器学习的解决方案，以应对这种复杂性。但是，他们通常使用复杂的模型，并且在实际情况下实现非常困难。我们为Wi-Fi网络中的自动RA提出了一种简单的深度强化学习方法，该方法命名为数据驱动的速率适应算法（DARA）。 Dara是符合标准的。它仅基于发射机接收到的框架的信噪比（SNR）的观察，动态调整Wi-Fi调制方案（MCS）。我们的仿真结果表明，与Minstrel高吞吐量（HT）相比，DARA的吞吐量高达15 \％，并且等于理想的Wi-Fi RA算法的性能。
The increasing complexity of recent Wi-Fi amendments is making optimal Rate Adaptation (RA) a challenge. The use of classic algorithms or heuristic models to address RA is becoming unfeasible due to the large combination of configuration parameters along with the variability of the wireless channel. Machine Learning-based solutions have been proposed in the state of art, to deal with this complexity. However, they typically use complex models and their implementation in real scenarios is difficult. We propose a simple Deep Reinforcement Learning approach for the automatic RA in Wi-Fi networks, named Data-driven Algorithm for Rate Adaptation (DARA). DARA is standard-compliant. It dynamically adjusts the Wi-Fi Modulation and Coding Scheme (MCS) solely based on the observation of the Signal-to-Noise Ratio (SNR) of the received frames at the transmitter. Our simulation results show that DARA achieves up to 15\% higher throughput when compared with Minstrel High Throughput (HT) and equals the performance of the Ideal Wi-Fi RA algorithm.
