AI Enlightens Wireless Communication: Analyses, Solutions and Opportunities on CSI Feedback

TL;DR

This paper reviews the first AI competition for wireless channel feedback, discussing data analysis, neural network design, and quantization, highlighting challenges and future research directions in AI-driven wireless systems.

Contribution

It provides a systematic overview of the WAIC, detailing the framework, enhancement schemes, and competition results for AI-based CSI feedback in wireless communication.

Findings

Different enhancement schemes improve CSI feedback performance

The competition results demonstrate the effectiveness of AI methods

Challenges and future research areas are identified

Abstract

In this paper, we give a systematic description of the 1st Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AI Work Group. Firstly, the framework of full channel state information (F-CSI) feedback problem and its corresponding channel dataset are provided. Then the enhancing schemes for DL-based F-CSI feedback including i) channel data analysis and preprocessing, ii) neural network design and iii) quantization enhancement are elaborated. The final competition results composed of different enhancing schemes are presented. Based on the valuable experience of 1st WAIC, we also list some challenges and potential study areas for the design of AI-based wireless communication systems.