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.

翻译：随着第五代(5G)无线系统在世界各地的部署势头不断增强,6G的可能技术正在积极研究讨论中,特别是6G机器学习(ML)的作用预计将加强和协助新兴应用,例如虚拟和扩大现实、车辆自主和计算机视觉。这将产生无线数据传输的很大一部分,包括图像、视频和语音。ML算法通过云服务器上的学习模型进行分类/识别/估计。这要求将数据从边缘设备无线传送到云服务器。频道估计与识别步骤分开处理,对于准确的学习表现至关重要。为了将频道和ML数据的学习结合起来,我们引入隐含的频道学习,以便在不估计无线频道频道的情况下完成ML任务。在这里,ML模型将接受频道破碎数据集的培训,以取代名义数据。没有频道估计,拟议的方法显示,对诸如毫米波和IEEEE 802.11p电视频道等各种情景的图像和语音分类任务大约60%的改进。