Multi-Modal Neural Radio Radiance Field for Localized Statistical Channel Modelling

TL;DR

This paper introduces MM-LSCM, a self-supervised multi-modal neural framework combining RSRP and LiDAR data to improve localized statistical channel modeling for next-generation networks.

Contribution

It proposes a novel dual-branch neural architecture with volume rendering and self-supervised training, integrating environmental data for enhanced channel modeling accuracy.

Findings

Outperforms traditional methods in channel reconstruction accuracy

Demonstrates robustness to environmental noise

Effectively integrates multi-modal data for better spatial awareness

Abstract

This paper presents MM-LSCM, a self-supervised multi-modal neural radio radiance field framework for localized statistical channel modeling (LSCM) for next-generation network optimization. Traditional LSCM methods rely solely on RSRP data, limiting their ability to model environmental structures that affect signal propagation. To address this, we propose a dual-branch neural architecture that integrates RSRP data and LiDAR point cloud information, enhancing spatial awareness and predictive accuracy. MM-LSCM leverages volume-rendering-based multi-modal synthesis to align radio propagation with environmental obstacles and employs a self-supervised training approach, eliminating the need for costly labeled data. Experimental results demonstrate that MM-LSCM significantly outperforms conventional methods in channel reconstruction accuracy and robustness to noise, making it a promising solution for real-world wireless network optimization.