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

Ruben, Queiros,; Almeida, Eduardo Nuno; Fontes, Helder; Ruela, José; Campos, Rui

doi:10.1109/iscc55528.2022.9912784

Cited by 9 publications

(1 citation statement)

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“…To overcome the difficulties, many studies have introduced algorithms for adaptive transmission rate control such as Minstrel [16], collision-aware rate adaptation (CARA) [17], Q-learning-based rate control [18], and deep learning-based approaches [19]. These algorithms control the transmission rates of data frames by adaptively selecting an appropriate modulation and coding scheme (MCS) in response to the changes in the status of the wireless channel between a sender and receiver of the data frames.…”

Section: Introductionmentioning

confidence: 99%

Use of Logarithmic Rates in Multi-Armed Bandit-Based Transmission Rate Control Embracing Frame Aggregations in Wireless Networks

Cho

2023

Applied Sciences

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Herein, we propose the use of the logarithmic values of data transmission rates for multi-armed bandit (MAB) algorithms that adjust the modulation and coding scheme (MCS) levels of data packets in carrier-sensing multiple access/collision avoidance (CSMA/CA) wireless networks. We argue that the utilities of the data transmission rates of the MCS levels may not be proportional to their nominal values and suggest using their logarithmic values instead of directly using their data transmission rates when MAB algorithms compute the expected throughputs of the MCS levels. To demonstrate the effectiveness of the proposal, we introduce two MAB algorithms that adopt the logarithmic rates of the transmission rates. The proposed MAB algorithms also support frame aggregations available in wireless network standards that aim for a high throughput. In addition, the proposed MAB algorithms use a sliding window over time to adapt to rapidly changing wireless channel environments. To evaluate the performance of the proposed MAB algorithms, we used the event-driven network simulator, ns-3. We evaluated their performance using various scenarios of stationary and non-stationary wireless network environments including multiple spatial streams and frame aggregations. The experiment results show that the proposed MAB algorithms outperform the MAB algorithms that do not adopt the logarithmic transmission rates in both the stationary and non-stationary scenarios.

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Section: Introductionmentioning

confidence: 99%