使用简单深度强化学习方法的Wi-Fi速率自适应
Wi-Fi Rate Adaptation using a Simple Deep Reinforcement Learning Approach
摘要：
最近Wi-Fi修订的复杂性不断增加，使得最佳速率自适应（RA）成为一项挑战。由于配置参数的大组合以及无线信道的可变性，使用经典算法或启发式模型来解决RA变得不可行。在现有技术中已经提出了基于机器学习的解决方案来处理这种复杂性。然而，它们通常使用复杂的模型，并且在实际场景中难以实现。我们提出了一种简单的深度强化学习方法用于Wi-Fi网络中的自动RA，称为数据驱动的速率自适应算法（DARA）。DARA符合标准。它仅根据对发射机接收帧的信噪比（SNR）的观察来动态调整Wi-Fi调制和编码方案（MCS）。我们的仿真结果表明，DARA实现高达15%的吞吐量相比，Minstrel高吞吐量（HT），等于理想的Wi-Fi RA算法的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
