Abstract
The problem of selecting the modulation and coding scheme (MCS) that
maximizes the system throughput, known as link adaptation, has been
investigated extensively, especially for IEEE 802.11 (WiFi) standards.
Recently, deep learning has widely been adopted as an efficient solution
to this problem. However, in failure cases, predicting a higher-rate MCS
can result in a failed transmission. In this case, a retransmission is
required, which largely degrades the system throughput. To address this
issue, we model the adaptive modulation and coding (AMC) problem as a
multi-label multi-class classification problem. The proposed modeling
allows more control over what the model predicts in failure cases. We
also design a simple, yet powerful, loss function to reduce the number
of retransmissions due to higher-rate MCS classification errors. Since
wireless channels change significantly due to the surrounding
environment, a huge dataset has been generated to cover all possible
propagation conditions. However, to reduce training complexity, we train
the CNN model using part of the dataset. The effect of different
subdataset selection criteria on the classification accuracy is studied.
The proposed model adapts the IEEE 802.11ax communications standard in
outdoor scenarios. The simulation results show the proposed loss
function reduces up to 50% of retransmissions compared to traditional
loss functions.