Mip-NeWRF: Enhanced Wireless Radiance Field with Hybrid Encoding for Channel Prediction

TL;DR

Mip-NeWRF introduces a physics-informed neural framework with hybrid encoding and a coarse-to-fine sampling strategy to significantly improve indoor wireless channel prediction accuracy and convergence speed in complex environments.

Contribution

The paper proposes Mip-NeWRF, a novel hybrid encoding and ray-based neural framework that enhances robustness, accuracy, and convergence in wireless channel prediction.

Findings

Reduces NMSE by 14.3 dB compared to baselines

Uses hybrid positional encoding for scale robustness and detail

Employs curriculum learning for faster convergence

Abstract

Recent work on wireless radiance fields represents a promising deep learning approach for channel prediction, however, in complex environments these methods still exhibit limited robustness, slow convergence, and modest accuracy due to insufficiently refined modeling. To address this issue, we propose Mip-NeWRF, a physics-informed neural framework for accurate indoor channel prediction based on sparse channel measurements. The framework operates in a ray-based pipeline with coarse-to-fine importance sampling: frustum samples are encoded, processed by a shared multilayer perceptron (MLP), and the outputs are synthesized into the channel frequency response (CFR). Prior to MLP input, Mip-NeWRF performs conical-frustum sampling and applies a scale-consistent hybrid positional encoding to each frustum. The scale-consistent normalization aligns positional encodings across scene scales, while the hybrid encoding supplies both scale-robust, low-frequency stability to accelerate convergence and fine spatial detail to improve accuracy. During training, a curriculum learning schedule is applied to stabilize and accelerate convergence of the shared MLP. During channel synthesis, the MLP outputs, including predicted virtual transmitter presence probabilities and amplitudes, are combined with modeled pathloss and surface interaction attenuation to enhance physical fidelity and further improve accuracy. Simulation results demonstrate the effectiveness of the proposed approach: in typical scenarios, the normalized mean square error (NMSE) is reduced by 14.3 dB versus state-of-the-art baselines.