# Location-free Spectrum Cartography

## TL;DR

This paper introduces a new spectrum cartography method that uses features of positioning signals instead of relying on sensor location estimates, significantly improving map accuracy in indoor and urban environments.

## Contribution

The paper proposes a novel paradigm for spectrum cartography that bypasses the need for precise sensor localization, enhancing performance in multipath-affected scenarios.

## Key findings

- Improved spectral map estimation accuracy in indoor scenarios.
- Simulation results show superiority over localization-based methods.
- Method effectively handles multipath effects.

## Abstract

Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a few. Since existing spectrum cartography techniques require accurate estimates of the sensor locations, their performance is drastically impaired by multipath affecting the positioning pilot signals, as occurs in indoor or dense urban scenarios. To overcome such a limitation, this paper introduces a novel paradigm for spectrum cartography, where estimation of spectral maps relies on features of these positioning signals rather than on location estimates. Specific learning algorithms are built upon this approach and offer a markedly improved estimation performance than existing approaches relying on localization, as demonstrated by simulation studies in indoor scenarios.