Missing Data Inference for Crowdsourced Radio Map Construction: An Adversarial Auto-Encoder Method

Radio environment monitoring is crucial for many network engineering applications. Integrated with mobile crowdsourcing (MCS), radio map can be updated by mobile users in a low-cost manner. However, the crowdsourced measurement data may get quite sparse, and contain noises and errors. Therefore, how to efficiently infer missing data under low-quality measurements is critical in crowdsourced radio map construction. Existing inference methods like matrix completion require certain strict conditions, e.g. missing at completely random (MACR), which is impractical in the city-scale sensing. To address these issues, we propose a deep learning scheme based on adversarial auto-encoder (AAE) to handle measurements with large missing regions and complicated loss patterns. Specifically, this scheme applies variational auto-encoder (VAE) to infer missing data, and further utilizes the adversarial nets to play a min-max game with the VAE to improve recovery quality. Comprehensive experiments on three real datasets show that the proposed scheme can outperform state-of-the-art methods under large missing rates and low-quality measurements.