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

February 09, 2026

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📝 Original Info

Title: LLM4XCE: Large Language Models for Extremely Large-Scale Massive MIMO Channel Estimation
ArXiv ID: 2512.08955
Date: 2025-11-28
Authors: Renbin Li, Shuangshuang Li, Peihao Dong

📝 Abstract

Extremely large-scale massive multiple-input multiple-output (XL-MIMO) is a key enabler for sixthgeneration (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 framew...

📄 Full Content

With the advancement of sixth-generation mobile communication (6G), extremely large-scale massive multiple-input multiple-output (XL-MIMO) systems have emerged as a key enabling technology for high-capacity and highly reliable communications, owing to their massive antenna arrays, ultrahigh spectral efficiency, and spatial resolution [1], [2]. However, the accurate acquisition of channel state information (CSI) remains a major performance bottleneck, especially in hybrid-field environments involving near-field and far-field coexistence, rich multipath propagation and fine-grained beam control. In such cases,

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