  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.
