Radio maps quantify received signal strength or other magnitudes of the radio frequency environment at every point of a geographical region. These maps play a vital role in a large number of applications such as wireless network planning, spectrum management, and optimization of communication systems. However, empirical validation of the large number of existing radio map estimators is highly limited. To fill this gap, a large data set of measurements has been collected with an autonomous unmanned aerial vehicle (UAV) and a representative subset of these estimators were evaluated on this data. The performance-complexity trade-off and the impact of fast fading are extensively investigated. Although sophisticated estimators based on deep neural networks (DNNs) exhibit the best performance, they are seen to require large volumes of training data to offer a substantial advantage relative to more traditional schemes. A novel algorithm that blends both kinds of estimators is seen to enjoy the benefits of both, thereby suggesting the potential of exploring this research direction further.

翻译：无线电地图量化地理区域内每一点的接收信号强度或其他射频环境参数。这类地图在无线网络规划、频谱管理和通信系统优化等众多应用中发挥着关键作用。然而，现有大量无线电地图估计方法的实证验证仍十分有限。为填补这一空白，本研究利用自主无人机（UAV）采集了大规模测量数据集，并基于该数据评估了若干具有代表性的估计方法。研究深入探讨了性能-复杂度权衡以及快衰落的影响。尽管基于深度神经网络（DNNs）的复杂估计方法展现出最优性能，但相较于传统方案，其需要大量训练数据才能获得显著优势。本文提出的一种融合两类估计方法的新型算法兼具二者优势，从而表明该研究方向具有进一步探索的价值。