MTCA: Multi-Task Channel Analysis for Wireless Communication

TL;DR

This paper introduces MTCA, a multi-task framework that enhances wireless communication by jointly analyzing channel state information, significantly improving accuracy across tasks like prediction, extrapolation, identification, and classification.

Contribution

MTCA is a novel multi-task learning framework that effectively integrates multiple CSI analysis tasks, outperforming previous methods in wireless communication scenarios.

Findings

Achieved 100% accuracy in channel identification and scenario classification.

Improved channel prediction by 20.1% over previous methods.

Enhanced antenna-domain extrapolation performance by 54.5%.

Abstract

In modern wireless communication systems, the effective processing of Channel State Information (CSI) is crucial for enhancing communication quality and reliability. However, current methods often handle different tasks in isolation, thereby neglecting the synergies among various tasks and leading to extract CSI features inadequately for subsequent analysis. To address these limitations, this paper introduces a novel Multi-Task Channel Analysis framework named MTCA, aimed at improving the performance of wireless communication even sensing. MTCA is designed to handle four critical tasks, including channel prediction, antenna-domain channel extrapolation, channel identification, and scenario classification. Experiments conducted on a multi-scenario, multi-antenna dataset tailored for UAV-based communications demonstrate that the proposed MTCA exhibits superior comprehension of CSI, achieving enhanced performance across all evaluated tasks. Notably, MTCA reached 100% prediction accuracy in channel identification and scenario classification. Compared to the previous state-of-the-art methods, MTCA improved channel prediction performance by 20.1% and antenna-domain extrapolation performance by 54.5%.