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Ultra-wideband (UWB) technology has become very popular for indoor positioning and distance estimation (DE) systems due to its decimeter-level accuracy achieved when using time-of-flight-based techniques. Techniques for DE relying on signal strength (DESS) received less attention. As a consequence, existing benchmarks consist of simple channel characterizations rather than methods aiming to increase accuracy. Further development in DESS may enable lower-cost transceivers to applications that can afford lower accuracies than those based on time-of-flight. Moreover, it is a fundamental building block used by a recently proposed approach that can enable security against cyberattacks to DE which could not be avoided using only time-of-flight-based techniques. In this paper, we aim to benchmark the performance of machine-learning models when used to increase the accuracy of UWB-based DESS …