ProSpire: Proactive Spatial Prediction of Radio Environment Using Deep Learning

TL;DR

ProSpire is a deep learning framework that enables proactive spatial prediction of radio environments for spectrum sharing, using crowdsourced data and a novel image translation model to improve prediction accuracy and interference management.

Contribution

It introduces a supervised deep learning approach, RSSu-net, for proactive radio environment prediction without area maps, leveraging crowdsourcing and outperforming existing methods.

Findings

Achieves 5 dB mean absolute error in signal strength prediction.

Creates proactive boundaries with 97% probability of avoiding interference.

RSSu-net outperforms comparable methods by 19% in prediction accuracy.

Abstract

Spatial prediction of the radio propagation environment of a transmitter can assist and improve various aspects of wireless networks. The majority of research in this domain can be categorized as 'reactive' spatial prediction, where the predictions are made based on a small set of measurements from an active transmitter whose radio environment is to be predicted. Emerging spectrum-sharing paradigms would benefit from 'proactive' spatial prediction of the radio environment, where the spatial predictions must be done for a transmitter for which no measurement has been collected. This paper proposes a novel, supervised deep learning-based framework, ProSpire, that enables spectrum sharing by leveraging the idea of proactive spatial prediction. We carefully address several challenges in ProSpire, such as designing a framework that conveniently collects training data for learning, performing the predictions in a fast manner, enabling operations without an area map, and ensuring that the predictions do not lead to undesired interference. ProSpire relies on the crowdsourcing of transmitters and receivers during their normal operations to address some of the aforementioned challenges. The core component of ProSpire is a deep learning-based image-to-image translation method, which we call RSSu-net. We generate several diverse datasets using ray tracing software and numerically evaluate ProSpire. Our evaluations show that RSSu-net performs reasonably well in terms of signal strength prediction, 5 dB mean absolute error, which is comparable to the average error of other relevant methods. Importantly, due to the merits of RSSu-net, ProSpire creates proactive boundaries around transmitters such that they can be activated with 97% probability of not causing interference. In this regard, the performance of RSSu-net is 19% better than that of other comparable methods.