Wi-Fi Rate Adaptation using a Simple Deep Reinforcement Learning Approach

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Queiros, R (Author) Other; Almeida, EN (Author) Other; Fontes, H (Author) Other; José Ruela (Author) FEUP; Campos, R (Author) FEUP

Conference proceedings International

Title: IEEE Symposium on Computers and Communications, ISCC 2022, Rhodes, Greece, June 30 - July 3, 2022

Pages: 1-3

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Other information

Authenticus ID: P-00W-5ME

DOI: 10.1109/iscc55528.2022.9912784

Abstract (EN): 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)

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 3