Hierarchical Sparse Coding for Wireless Link Prediction in an Airborne Scenario

Abstract

We build a data-driven hierarchical inference model to predict wireless link quality between a mobile unmanned aerial vehicle (UAV) and ground nodes. Clustering, sparse feature extraction, and non-linear pooling are combined to improve Support Vector Machine (SVM) classification when a limited training set does not comprehensively characterize data variations. Our approach first learns two layers of dictionaries by clustering packet reception data. These dictionaries are used to perform sparse feature extraction, which expresses link state vectors first in terms of a few prominent local patterns, or features, and then in terms of co-occurring features along the flight path. In order to tolerate artifacts like small positional shifts in field-collected data, we pool large magnitude features among overlapping shifted patches within windows. Together, these techniques transform raw link measurements into stable feature vectors that capture environmental effects driven by radio range limitations, antenna pattern variations, line-of-sight occlusions, etc. Link outage prediction is implemented by an SVM that assigns a common label to feature vectors immediately preceding gaps of successive packet losses, predictions are then fed to an adaptive link layer protocol that adjusts forward error correction rates, or queues packets during outages to prevent TCP timeout. In our harsh target environment, links are unstable and temporary outages common, so baseline TCP connections achieve only minimal throughput. However, connections under our predictive protocol temporarily hold packets that would otherwise be lost on unavailable links, and react quickly when the UAV link is restored, increasing overall channel utilization.