Implicit Channel Learning for Machine Learning Applications in 6G Wireless Networks

With the deployment of the fifth generation (5G)
wireless systems gathering momentum across the world, possible
technologies for 6G are under active research discussions. In
particular, the role of machine learning (ML) in 6G is expected
to enhance and aid emerging applications such as virtual and augmented
reality, vehicular autonomy, and computer vision. This
will result in large segments of wireless data traffic comprising
image, video and speech. The ML algorithms process these for
classification/recognition/estimation through the learning models
located on cloud servers. This requires wireless transmission of
data from edge devices to the cloud server. Channel estimation,
handled separately from recognition step, is critical for accurate
learning performance. Toward combining the learning for both
channel and the ML data, we introduce implicit channel learning
to perform the ML tasks without estimating the wireless channel.
Here, the ML models are trained with channel-corrupted datasets
in place of nominal data. Without channel estimation, the
proposed approach exhibits approximately 60% improvement in
image and speech classification tasks for diverse scenarios such
as millimeter wave and IEEE 802.11p vehicular channels.