TiRE-GAN: Task-Incentivized Generative Learning for Radiomap Estimation

TL;DR

This paper introduces TiRE-GAN, a novel generative model that combines physical radio propagation principles with data-driven learning to accurately estimate radiomaps from sparse observations, improving resource management in wireless networks.

Contribution

The paper proposes TiRE-GAN, a task-incentivized generative learning framework that integrates a radio depth map and task-driven feedback for enhanced radiomap estimation.

Findings

Radio depth map effectively captures propagation patterns.

TiRE-GAN outperforms existing methods in radiomap accuracy.

The model demonstrates efficiency in sparse observation scenarios.

Abstract

To characterize radio frequency (RF) signal power distribution in wireless communication systems, the radiomap is a useful tool for resource allocation and network management. Usually, a dense radiomap is reconstructed from sparse observations collected by deployed sensors or mobile devices. To leverage both physical principles of radio propagation models and data statistics from sparse observations, this work introduces a novel task-incentivized generative learning model, namely TiRE-GAN, for radiomap estimation. Specifically, we first introduce a radio depth map to capture the overall pattern of radio propagation and shadowing effects, following which a task-driven incentive network is proposed to provide feedback for radiomap compensation depending on downstream tasks. Our experimental results demonstrate the power of the radio depth map to capture radio propagation information, and the efficiency of the proposed TiRE-GAN for radiomap estimation.