LLM4XCE: Large Language Models for Extremely Large-Scale Massive MIMO Channel Estimation

TL;DR

This paper introduces LLM4XCE, a novel framework using large language models to improve channel estimation in XL-MIMO systems, effectively handling hybrid-field conditions with high accuracy and efficiency.

Contribution

The paper proposes a new LLM-based channel estimation method that leverages semantic modeling and fine-tuning of Transformer layers for XL-MIMO systems, addressing hybrid-field challenges.

Findings

Outperforms existing methods in hybrid-field conditions

Achieves higher estimation accuracy

Demonstrates strong generalization performance

Abstract

Extremely large-scale massive multiple-input multiple-output (XL-MIMO) is a key enabler for sixth-generation (6G) networks, offering massive spatial degrees of freedom. Despite these advantages, the coexistence of near-field and far-field effects in hybrid-field channels presents significant challenges for accurate estimation, where traditional methods often struggle to generalize effectively. In recent years, large language models (LLMs) have achieved impressive performance on downstream tasks via fine-tuning, aligning with the semantic communication shift toward task-oriented understanding over bit-level accuracy. Motivated by this, we propose Large Language Models for XL-MIMO Channel Estimation (LLM4XCE), a novel channel estimation framework that leverages the semantic modeling capabilities of large language models to recover essential spatial-channel representations for downstream tasks. The model integrates a carefully designed embedding module with Parallel Feature-Spatial Attention, enabling deep fusion of pilot features and spatial structures to construct a semantically rich representation for LLM input. By fine-tuning only the top two Transformer layers, our method effectively captures latent dependencies in the pilot data while ensuring high training efficiency. Extensive simulations demonstrate that LLM4XCE significantly outperforms existing state-of-the-art methods under hybrid-field conditions, achieving superior estimation accuracy and generalization performance.