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

TL;DR

This paper proposes implicit channel learning, enabling machine learning tasks in 6G wireless networks without explicit channel estimation, significantly improving classification accuracy across various wireless scenarios.

Contribution

It introduces a novel implicit channel learning method that trains ML models directly on channel-corrupted data, bypassing the need for explicit channel estimation.

Findings

Approximately 60% improvement in classification accuracy.

Effective across diverse wireless channel scenarios.

Reduces reliance on traditional channel estimation methods.

Abstract

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.