Next generation cellular networks will implement radio sensing functions alongside customary communications, thereby enabling unprecedented worldwide sensing coverage outdoors. Deep learning has revolutionised computer vision but has had limited application to radio perception tasks, in part due to lack of systematic datasets and benchmarks dedicated to the study of the performance and promise of radio sensing. To address this gap, we present MaxRay: a synthetic radio-visual dataset and benchmark that facilitate precise target localisation in radio. We further propose to learn to localise targets in radio without supervision by extracting self-coordinates from radio-visual correspondence. We use such self-supervised coordinates to train a radio localiser network. We characterise our performance against a number of state-of-the-art baselines. Our results indicate that accurate radio target localisation can be automatically learned from paired radio-visual data without labels, which is important for empirical data. This opens the door for vast data scalability and may prove key to realising the promise of robust radio sensing atop a unified communication-perception cellular infrastructure. Dataset will be hosted on IEEE DataPort.

翻译：下一代蜂窝网络将在常规通信功能之外实现无线电感知功能，从而在全球范围内实现前所未有的室外感知覆盖。深度学习彻底改变了计算机视觉，但在无线电感知任务中的应用却很有限，部分原因是缺乏专门用于研究无线电感知性能和潜力的系统化数据集和基准。为填补这一空白，我们提出了MaxRay：一个合成的无线电-视觉数据集和基准，用于促进无线电中的精确目标定位。我们进一步提出通过从无线电-视觉对应中提取自坐标，以无监督方式学习在无线电中定位目标。我们利用这种自监督坐标来训练一个无线电定位网络。我们与多个最先进基线对比了性能。结果表明，无需标签即可从配对的无线电-视觉数据中自动学习精确的无线电目标定位，这对经验数据具有重要意义。这为实现大规模数据可扩展性打开了大门，并可能成为在统一通信-感知蜂窝基础设施上实现稳健无线电感知承诺的关键。数据集将托管在IEEE DataPort上。