AI-Driven Channel State Information (CSI) Extrapolation for 6G: Current Situations, Challenges and Future Research

TL;DR

This paper reviews the current state, challenges, and future directions of AI-driven channel state information extrapolation techniques for 6G, emphasizing their importance in reducing overhead in high-mobility and large-scale systems.

Contribution

It provides the first comprehensive review of CSI extrapolation methods for 6G, analyzing performance metrics, comparing model-driven and AI-driven approaches, and discussing open datasets and future research challenges.

Findings

AI-driven methods show promise in improving CSI extrapolation accuracy

Model-driven approaches are effective but face scalability issues

Open-source datasets are crucial for training AI models in CSI extrapolation

Abstract

CSI extrapolation is an effective method for acquiring channel state information (CSI), essential for optimizing performance of sixth-generation (6G) communication systems. Traditional channel estimation methods face scalability challenges due to the surging overhead in emerging high-mobility, extremely large-scale multiple-input multiple-output (EL-MIMO), and multi-band systems. CSI extrapolation techniques mitigate these challenges by using partial CSI to infer complete CSI, significantly reducing overhead. Despite growing interest, a comprehensive review of state-of-the-art (SOTA) CSI extrapolation techniques is lacking. This paper addresses this gap by comprehensively reviewing the current status, challenges, and future directions of CSI extrapolation for the first time. Firstly, we analyze the performance metrics specific to CSI extrapolation in 6G, including extrapolation accuracy, adaption to dynamic scenarios and algorithm costs. We then review both model-driven and artificial intelligence (AI)-driven approaches for time, frequency, antenna, and multi-domain CSI extrapolation. Key insights and takeaways from these methods are summarized. Given the promise of AI-driven methods in meeting performance requirements, we also examine the open-source channel datasets and simulators that could be used to train high-performance AI-driven CSI extrapolation models. Finally, we discuss the critical challenges of the existing research and propose perspective research opportunities.