ICASSP 2023 SPGC

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. Some example use cases include: User-cell site association, fingerprint-based localization, physical-layer security, optimal power control, path planning, and activity detection.

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.

The top 5 ranked teams will be invited to submit a 2-page paper and present it at ICASSP 2023. The accepted papers will be published in the ICASSP proceedings. The teams that present their work at ICASSP are also invited to submit a full paper about their work to IEEE Open Journal of Signal Processing.

The deadline for submission of the trained models, and the test codes is February 3, 2023.

Support on the dataset and the instructions will be provided by the organizing team.

IMPORTANT NOTE: The intellectual property (IP) of the shared/submitted material (e.g. code) will not be transferred to the challenge organizers. When such material is made publicly available by a participant, an appropriate license should accompany.

Task #1: Pathloss Prediction

The first task is to predict the pathloss radio map given the city map and the transmitter location.

Task #2: Coverage classification

In coverage (or service area) classification, the goal is to classify a region of interest according to their pathloss values being above or below some pre-determined pathloss value, given a Tx with known location and the city map. The goal is then to predict the coverage map from the city map and transmitter location.