International Conference on Information and Knowledge Technology

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FiReT: A Neural Radiance Fields Framework for Wireless Field Reconstruction and Transmitter Placement

Authors :

Negar Pouya¹ Armin Soleymani² Gholamreza Moradi³ Farzaneh Abdollahi⁴

1- دانشگاه صنعتی امیرکبیر (پلی‌تکنیک تهران) 2- دانشگاه صنعتی امیرکبیر (پلی‌تکنیک تهران) 3- دانشگاه صنعتی امیرکبیر (پلی‌تکنیک تهران) 4- دانشگاه صنعتی امیرکبیر (پلی‌تکنیک تهران)

Keywords :

neural radiance fields (NeRF)،wireless channel estimation،transmitter placement optimization،deep learning for wireless networks،sparse measurements

Abstract :

We introduce FiReT, a deep learning framework designed for wireless channel estimation and transmitter placement optimization. Accurately modeling channel behavior in complex indoor spaces remains a critical challenge, as conventional approaches rely on exhaustive site surveys that demand dense measurements and significant human effort. To overcome these limitations, our method leverages recent advances in Neural Radiance Fields (NeRF) to learn a continuous representation of the wireless radiation field from sparse observations. In our formulation, receivers are randomly positioned throughout the environment while the transmitter location is systematically varied, allowing the model to explore alternative deployment scenarios. This design enables the estimation of wireless channels at unseen points and facilitates the identification of the globally optimal transmitter position that ensures robust coverage across the entire space. Through extensive evaluation, we show that our approach maintains high prediction accuracy with far fewer measurements than traditional methods, demonstrating the promise of NeRF-based learning in guiding efficient and scalable transmitter deployment for future wireless networks.