NeRF2: Neural Radio-Frequency Radiance Fields

Abstract

Although Maxwell discovered the physical laws of electromagnetic waves 160 years ago, how to precisely model the propagation of an RF signal in an electrically large and complex environment remains a long-standing problem. The difficulty is in the complex interactions between the RF signal and the obstacles (e.g., reflection, diffraction, etc.). Inspired by the great success of using a neural network to describe the optical field in computer vision, we propose a neural radio-frequency radiance field, NeRF${}^{\text{2}}$, which represents a continuous volumetric scene function that makes sense of an RF signal's propagation. Particularly, after training with a few signal measurements, NeRF${}^{\text{2}}$ can tell how/what signal is received at any position when it knows the position of a transmitter. As a physical-layer neural network, NeRF${}^{\text{2}}$ can take advantage of the learned statistic model plus the physical model of ray tracing to generate a synthetic dataset that meets the training demands of application-layer artificial neural networks (ANNs). Thus, we can boost the performance of ANNs by the proposed turbo-learning, which mixes the true and synthetic datasets to intensify the training. Our experiment results show that turbo-learning can enhance performance with an approximate 50% increase. We also demonstrate the power of NeRF${}^{\text{2}}$ in the field of indoor localization and 5G MIMO.

References in corpus (11)

• Adam: A Method for Stochastic Optimization
• Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
• Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
• NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
• LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
• Dead on Arrival: An Empirical Study of The Bluetooth 5.1 Positioning System
• Towards Flexible Wireless Charging for Medical Implants Using Distributed Antenna System
• mD-Track: Leveraging Multi-Dimensionality in Passive Indoor Wi-Fi Tracking
• Block-NeRF: Scalable Large Scene Neural View Synthesis
• Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems
• NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis

Cited by in corpus (7)

• Artificial Intelligence of Things: A Survey
• RetailOpt: Opt-In, Easy-to-Deploy Trajectory Estimation from Smartphone Motion Data and Retail Facility Information
• WixUp: A General Data Augmentation Framework for Wireless Perception in Tracking of Humans
• F${}^4$-CKM: Learning Channel Knowledge Map with Radio Frequency Radiance Field Rendering
• An Efficient and Explainable KAN Framework for Wireless Radiation Field Prediction
• AI-Driven Radio Propagation Prediction in Automated Warehouses using Variational Autoencoders
• Point-Cloud-Assistant Localized Statistical Channel Prediction by Tangent Gaussian Splatting