# Learning Power Spectrum Maps from Quantized Power Measurements

arXiv: https://arxiv.org/abs/1606.02679 · HTML: https://mlsanity.com/paper/1606.02679/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2016.

## Built on by (most cited first, 14 of 14)

- [Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Models](https://mlsanity.com/paper/2105.00177/) (2021)
- [Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs](https://mlsanity.com/paper/2201.04125/) (2022)
- [Quantized Radio Map Estimation Using Tensor and Deep Generative Models](https://mlsanity.com/paper/2303.01770/) (2023)
- [ACT-GAN: Radio map construction based on generative adversarial networks with ACT blocks](https://mlsanity.com/paper/2401.08976/) (2024)
- [Spatial Transformers for Radio Map Estimation](https://mlsanity.com/paper/2411.01211/) (2024)
- [Radio Map Estimation via Latent Domain Plug-and-Play Denoising](https://mlsanity.com/paper/2501.13472/) (2025)
- [Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection](https://mlsanity.com/paper/2604.05788/) (2026)
- [Radio Map Estimation: Empirical Validation and Analysis](https://mlsanity.com/paper/2310.11036/) (2023)
- [Accelerating Regularized Attention Kernel Regression for Spectrum Cartography](https://mlsanity.com/paper/2604.25138/) (2026)
- [Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensing](https://mlsanity.com/paper/2607.00887/) (2026)
- [UAV-aided RF Mapping for Sensing and Connectivity in Wireless Networks](https://mlsanity.com/paper/2205.03335/) (2022)
- [RADIANCE: Radio-Frequency Adversarial Deep-learning Inference for Automated Network Coverage Estimation](https://mlsanity.com/paper/2308.10584/) (2023)
- [Neural Beam Field for Spatial Beam RSRP Prediction](https://mlsanity.com/paper/2508.06956/) (2025)
- [Learning the Channel Gain from Anywhere to Anywhere via Cross-environment Transformer Estimators](https://mlsanity.com/paper/2605.08211/) (2026)

## Abstract (arXiv)

Power spectral density (PSD) maps providing the distribution of RF power across space and frequency are constructed using power measurements collected by a network of low-cost sensors. By introducing linear compression and quantization to a small number of bits, sensor measurements can be communicated to the fusion center with minimal bandwidth requirements. Strengths of data- and model-driven approaches are combined to develop estimators capable of incorporating multiple forms of spectral and propagation prior information while fitting the rapid variations of shadow fading across space. To this end, novel nonparametric and semiparametric formulations are investigated. It is shown that PSD maps can be obtained using support vector machine-type solvers. In addition to batch approaches, an online algorithm attuned to real-time operation is developed. Numerical tests assess the performance of the novel algorithms.