LLM4XCE: Large Language Models for Extremely Large-Scale Massive MIMO Channel Estimation
This addresses a key problem for 6G network development by improving channel estimation in hybrid-field scenarios, representing an incremental advancement through the novel application of LLMs to this domain.
The paper tackles the challenge of accurate channel estimation in extremely large-scale massive MIMO systems under hybrid-field conditions by proposing LLM4XCE, a framework that leverages large language models for semantic modeling, achieving superior estimation accuracy and generalization performance compared to state-of-the-art methods.
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.