SPAWC21: ML for Multi-Domain Localization and Signal Recognition Data Competition Call for Participation!

[Tim O'Shea]

Call for IEEE SPAWC Data Competition Paper Submissions & Competitors!

 

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2021

September 27 – 30, 2021  --   Lucca, Italy (And Online Hybrid Event)

 

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SPAWC is hosting two exciting data competition this year at the intersection of wireless communications and machine learning and soliciting competitors both through paper submissions on approaches to these problems and through direct participation in the competitions through submission of scored results.

 

We will accept the submission of full papers with proposed approaches and solutions to the data competition problem statements and datasets through July 5^th and will accept competition solution entries through the beginning of the conference on September 27^th through the data competition sites hosted on Kaggle and eval.ai.

 

Full details for the data competition event may be found at the official conference website at https://www.spawc2021.com/data-competition/

Challenge #1 (Industrial Multi-Domain Localization) focuses on Industry 4.0 centric robust and versatile positioning of robotic devices using a combination of Camera-based, IMU-Based and ultra-wideband (UWB) based data observations requiring the fusion of multiple domains of observation to maximize precision.  As robust and precise radio emitter localization is a key component in future industry and factory applications, we are excited to launch this data-driven challenge as part of SPAWC 21.

https://www.kaggle.com/c/ieeespawc21localization/data

 

Challenge #2 (Wideband Signal Recognition) focuses on rapid spectrum awareness and signal recognition to enable radio spectrum access coordination, anomaly detection, spectrum sharing, spectrum analytics and spectrum monitoring applications.  As real-time spectrum awareness and spectrum aware decision making may be key components to beyond-5G and 6G communications systems, we are excited to launch this data-driven signal recognition competition as part of SPAWC this year to compare and contrast new promising approaches to the problem.

https://eval.ai/web/challenges/challenge-page/1057/overview

 

Both address key challenge areas where machine learning has demonstrated extremely promising initial results in related areas, but where we believe these datasets provide an exciting new step in expanding these results to multi-domain and to broad recognition challenges beyond what has been the principal focus in research publications thus far.   Thus, we hope by posing these two challenges we can excite new researchers to propose, compare and publish new approaches to these problems which can help accelerate and mature these domains at the intersection of communications systems and machine learning.

 

We’re soliciting full workshop papers via EDAS (https://edas.info/newPaper.php?c=28267) from competitors wishing to publish their approaches and ideas as well as competition submissions via Kaggle and eval.ai which may be submitted directly via the competition websites above until the beginning of the Conference event.

 

Best Regards,

Tim O’Shea

Data Competition Chair, SPAWC21