Wi-Fi Rate Adaptation using a Simple Deep Reinforcement Learning Approach
Wi-Fi Rate Adaptation using a Simple Deep Reinforcement Learning Approach
复制标题
使用简单的深度强化学习方法进行 Wi-Fi 速率自适应
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Rui Campos
中科院分区：
文献类型：
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作者：
Rúben Queirós;E. N. Almeida;Helder Fontes;J. Ruela;Rui Campos
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沿着无线信道的可变性，使用经典算法或启发式模型来解决RA变得不可行。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（达拉）。达拉符合标准。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，达拉实现更高的吞吐量相比，Minstrel高吞吐量（HT）和理想的Wi-Fi RA算法的性能相匹配。
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. 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 higher throughput when compared with Minstrel High Throughput (HT) and matches the performance of the Ideal Wi-Fi RA algorithm.