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In wireless communications, the pathloss (or large scale fading coefficient) quantifies the loss of signal strength between a transmitter (Tx) and a receiver (Rx) due to large scale effects, such as free-space propagation loss, and interactions of the radio waves with the obstacles (which block line-of sight, like buildings, vehicles, pedestrians), e.g. penetrations, reflections and diffractions.
Many present or envisioned applications in wireless communications explicitly rely on the knowledge of the pathloss function, and thus, estimating pathloss is a crucial task.
Deterministic simulation methods such as ray-tracing are well-known to provide very good estimations of pathloss values. However, their high computational complexity renders them unsuitable for most of the envisioned applications.
In the very recent years, many research groups have developed deep learning-based methods which achieve a comparable accuracy with respect to ray-tracing, but with orders of magnitude lower computational times, making accurate pathloss estimations available for the applications.
In order to foster research and facilitate fair comparisons among the methods, we provide a novel pathloss radio map dataset based on ray-tracing simulations and launch the First Pathloss Radio Map Prediction Challenge. In addition to the pathloss prediction task, the challenge also includes coverage classification as a second independent task, where the locations in a city map should be classified to be above or below a given pathloss value.
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