# Tensor Completion for Radio Map Reconstruction using Low Rank and Smoothness

arXiv: https://arxiv.org/abs/1905.13689 · HTML: https://mlsanity.com/paper/1905.13689/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 7 of 7)

- [Quantized Radio Map Estimation Using Tensor and Deep Generative Models](https://mlsanity.com/paper/2303.01770/) (2023)
- [Spatial Transformers for Radio Map Estimation](https://mlsanity.com/paper/2411.01211/) (2024)
- [ProSpire: Proactive Spatial Prediction of Radio Environment Using Deep Learning](https://mlsanity.com/paper/2308.10193/) (2023)
- [Hybrid Model and Data Driven Algorithm for Online Learning of Any-to-Any Path Loss Maps](https://mlsanity.com/paper/2107.06677/) (2021)
- [Learning the Channel Gain from Anywhere to Anywhere via Cross-environment Transformer Estimators](https://mlsanity.com/paper/2605.08211/) (2026)
- [Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion](https://mlsanity.com/paper/2607.01777/) (2026)
- [Radiomap Blind Prediction under Incomplete Observation: Error Characterization and Correctable Propagation-Prior Learning](https://mlsanity.com/paper/2609.32836/) (2026)

## Abstract (arXiv)

Radio maps are important enablers for many applications in wireless networks, ranging from network planning and optimization to fingerprint based localization. Sampling the complete map is prohibitively expensive in practice, so methods for reconstructing the complete map from a subset of measurements are increasingly gaining attention in the literature. In this paper, we propose two algorithms for this purpose, which build on existing approaches that aim at minimizing the tensor rank while additionally enforcing smoothness of the radio map. Experimental results with synthetic measurements derived via ray tracing show that our algorithms outperform state of the art techniques.