Grid-Free Radio Map Estimation via Unsupervised Implicit Continuous Representation

Abstract

Radio map estimation (RME), also known as spectrum cartography (SC), aims to estimate instantaneous signal power distribution over a certain space-frequency region. Recent RME approaches typically discretize the to-be-estimated radio map into grid cells under a fixed resolution. Meshing subtly adds structural priors, e.g., low-rankness or deep image priors, to the radio map. These priors can effectively enhance the performance of RME, especially in blind scenarios. However, the downside is all the locations in a grid cell will share the same signal power, which is overly simplistic and contradict the continuity nature of power propagation. This work puts forth a blind grid-free RME framework. We introduce implicit continuous representation (ICR), which learns a mapping between spatial coordinates and power propagation pattern of each transmitter. This mechanism conceptually enables estimating the signal power at any spatial location within a certain region. With some model-based interpretations and designated optimization criteria, the ICR-based framework could be fully unsupervised, using only sampled data for training. This implies that our approach is not prone to the prevalent generalizability issue. Experiments under simulated and ray-tracing datasets verify the effectiveness of the proposed approach.