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

## TL;DR

This paper introduces two novel algorithms for reconstructing radio maps from limited measurements by leveraging low tensor rank and smoothness, demonstrating superior performance over existing methods in synthetic tests.

## Contribution

The paper proposes two new algorithms that combine low-rank tensor completion with smoothness constraints for radio map reconstruction, improving accuracy over prior techniques.

## Key findings

- Algorithms outperform state-of-the-art methods in synthetic experiments.
- Reconstruction accuracy improves with combined low-rank and smoothness constraints.
- Synthetic ray tracing data validates the effectiveness of the proposed methods.

## Abstract

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.