Structure-Aware Multimodal LLM Framework for Trustworthy Near-Field Beam Prediction

Technical Deep Dive: Structure-Aware Multimodal LLM Framework for Trustworthy Near-Field Beam Prediction

Overview

This work proposes a structure-aware multimodal large language model (LLM) framework for trustworthy beam prediction in near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems. The key contributions are:

• Multimodal input encoding that fuses GPS data, RGB images, LiDAR, and textual prompts to enable the LLM to reason about the complex spatial dynamics of the environment.
• A structure-aware beam prediction head that decouples the high-dimensional beam index into independent azimuth, elevation, and distance components, mirroring the 3D geometry of the near-field codebook.
• An auxiliary trajectory prediction module that acts as a spatial prior to guide the beam search.
• A confidence-aware adaptive refinement mechanism that triggers targeted beam scanning only when prediction confidence is low, balancing accuracy and pilot overhead.

The proposed framework significantly outperforms state-of-the-art deep learning-based algorithms and efficient near-field beam training baselines in both line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios.

Problem & Context

• In near-field XL-MIMO systems, spherical wavefront propagation couples angular and distance dimensions, producing highly position-sensitive volumetric beam patterns.
• Efficient beam alignment is crucial to maintain reliable communication, but conventional beam training methods suffer from prohibitive overhead and latency, especially in complex 3D environments.
• Beam prediction approaches aim to directly infer optimal beams from historical observations, but wireless-only models lack environmental awareness and suffer from generalization limitations.

Methodology

1. Multimodal Encoding: The framework fuses GPS data, RGB images, LiDAR point clouds, and textual prompts to extract a unified representation of the UAV's kinematic state and the surrounding environment.
2. Structure-Aware Beam Prediction: A cascaded architecture predicts the decoupled azimuth, elevation, and distance indices, mirroring the 3D geometry of the near-field codebook. An auxiliary trajectory prediction module provides spatial guidance.
3. Adaptive Refinement: A confidence-driven mechanism dynamically triggers a small-scale beam sweep only when the prediction confidence is low, balancing accuracy and pilot overhead.

Data & Experimental Setup

• Dataset: Multimodal-LAE-XLMIMO, a comprehensive open-source dataset for XL-MIMO in low-altitude environments, with 215,400 labeled samples across 30 urban scenes.
• Evaluation Metrics: Top-K prediction accuracy, average achievable rate, normalized beamforming gain, and trajectory prediction error.
• Baselines: Deep learning sequence models (RNN, LSTM, M2BeamLLM) and efficient near-field beam training algorithms (Hierarchical Search, Two-stage Search).

Results

1. Accuracy Comparison:
   • The proposed framework without adaptive refinement outperforms all deep learning baselines, achieving a Top-1 joint accuracy of 35% using only GPS input.
   • With adaptive refinement, the framework boosts the Top-1 joint accuracy to 83% across all scenarios, including 78% in challenging NLoS environments.
2. Achievable Rate Comparison:
   • The proposed framework maintains a near-optimal achievable rate, closely tracking the ground truth upper bound.
   • In NLoS scenarios, the adaptive refinement mechanism bridges the performance gap, achieving 78% higher rate than the Two-stage baseline.

Interpretation

• The multimodal encoding and structure-aware prediction effectively capture the complex coupling between near-field beams and the physical environment, enabling superior accuracy.
• The auxiliary trajectory prediction and confidence-aware adaptive refinement are critical for robust performance, especially in challenging NLoS conditions.
• The decoupled beam index prediction simplifies the output space and provides physical interpretability, guiding the learning process.

Limitations & Uncertainties

• The evaluation is limited to a single carrier frequency (7 GHz) and a fixed antenna array size (64x64).
• The dataset, while comprehensive, may not capture the full diversity of real-world near-field XL-MIMO deployments.
• The confidence threshold for adaptive refinement is empirically set and may require further optimization.

What Comes Next

• Extending the framework to handle dynamic environments and user mobility.
• Investigating joint optimization of the beam prediction and communication resources.
• Exploring the framework's generalization to other wireless communication scenarios beyond XL-MIMO.