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 model failure cases, predicting a
higher-rate MCS can result in a failed transmission. In this case,
retransmission is required, which largely degrades the system
throughput. To address this issue, we formulate the adaptive modulation
and coding (AMC) problem as a multi-label multi-class classification
problem. The proposed formulation allows more control over what the
model predicts in failure cases. In this context, we propose 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 is
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. It is shown that some criteria for
dataset selection consistently behave better than others. To confirm the
performance, we applied the proposed model for adapting the IEEE
802.11ax standard in outdoor propagation scenarios. The simulation
results show that the proposed loss function reduces up to 50% of
retransmissions compared to traditional loss functions. Finally, we
propose an optimal subdataset selection criterion.