Forecasting fine-grained city-scale cellular traffic with sparse crowdsourced measurements

under the supervision of an accuracy assurance network to generate a high-resolution cellular traffic map for prediction. We implement the proposed CrowdGAN in TensorFlow and evaluate its performance using two real-world cellular traffic datasets. Extensive experiments show that CrowdGAN significantly outperforms the baselines on a variety of performance metrics, and achieves at least 47% reduction in root-mean-squared error compared to the state-of-the-art.