Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography

Abstract

Radio maps can be utilized to characterize a parameter of interest in a communication channel, such as the received signal strength, at every point of a certain geographical region. This article presents an introductory tutorial to radio map estimation, where radio maps are constructed using spatially distributed measurements. After describing the applications of this kind of maps, this article delves into estimation approaches. Starting by simple regression techniques, gradually more sophisticated algorithms are introduced until reaching state-of-the-art estimators. The presentation of this versatile toolkit is accompanied with toy examples to build up intuition and gain insight into the foundations of radio map estimation. As a secondary objective, this article attempts to reconcile the sometimes conflicting terminology in the literature and to connect multiple bodies of literature and sub-communities that have been working separately in this context.

Dense Depth-Guided Generalizable NeRF

“…However, due to the factors mentioned above (such as the need for more detailed environmental consideration at higher frequencies and the need for fast simulations with higher deployment density), RT simulations are too computationally intensive for large-scale network deployment in 6G systems. As a result, simplified model-based approaches like the dominant path model [9], or fine-tuning of generic pathloss models (e.g., 3GPP path gain model) with limited measurement data [10], [11] have been proposed over the years. However, these approaches have found only limited acceptance by network operators due to their insufficient accuracy in predicting the propagation characteristics of signals in complex environments.…”

“…For example, an atrous rate of r = 2 doubles the FoV of the filter, while an atrous rate of r = 3 triples it. The standard convolution can be seen as a special case of (11) where r = 1.…”