Semantic Pilot Design for Data-Aided Channel Estimation Using a Large Language Model

TL;DR

This paper introduces a novel semantic pilot design leveraging large language models to improve data-aided channel estimation in text-inclusive transmissions, outperforming traditional methods.

Contribution

It is the first to utilize semantic information from LLMs for reliable symbol selection in channel estimation.

Findings

Achieves lower normalized mean squared error.

Reduces phase error of the estimated channel.

Decreases bit error rate.

Abstract

This paper proposes a semantic pilot design for data-aided channel estimation in text-inclusive data transmission, using a large language model (LLM). In this scenario, channel impairments often appear as typographical errors in the decoded text, which can be corrected using an LLM. The proposed method compares the initially decoded text with the LLM-corrected version to identify reliable decoded symbols. A set of selected symbols, referred to as a semantic pilot, is used as an additional pilot for data-aided channel estimation. To the best of our knowledge, this work is the first to leverage semantic information for reliable symbol selection. Simulation results demonstrate that the proposed scheme outperforms conventional pilot-only estimation, achieving lower normalized mean squared error and phase error of the estimated channel, as well as reduced bit error rate.