Deep Learning for Frame Error Prediction using a DARPA Spectrum Collaboration Challenge (SC2) Dataset
This work addresses frame error prediction for wireless networks in congested shared spectrum, but it is incremental as it applies existing deep learning methods to a new dataset.
The paper tackled frame error prediction in a Collaborative Intelligent Radio Network using a dataset from the DARPA SC2 challenge, investigating scenarios with varied bandwidth/channel strategies and latency constraints, and uncovered predictor characteristics across SNR ranges to enable scalable deep-learning strategies for heterogeneous networks.
We demonstrate a first example for employing deep learning in predicting frame errors for a Collaborative Intelligent Radio Network (CIRN) using a dataset collected during participation in the final scrimmages of the DARPA SC2 challenge. Four scenarios are considered based on randomizing or fixing the strategy for bandwidth and channel allocation, and either training and testing with different links or using a pilot phase for each link to train the deep neural network. We also investigate the effect of latency constraints, and uncover interesting characteristics of the predictor over different Signal to Noise Ratio (SNR) ranges. The obtained insights open the door for implementing a deep-learning-based strategy that is scalable to large heterogeneous networks, generalizable to diverse wireless environments, and suitable for predicting frame error instances and rates within a congested shared spectrum.