Hello everyone, In this meeting will there be a session on ML-PHY channel estimation challenge? also is there someone to answer our questions about the same challenge?

Yes we have

Thanks

you can write your questions here. They will answer

sure, thank you

Hello, just missed the first which challenge problem is being discussed at this time?

Franc is discussing https://www.upf.edu/web/wnrg/ai_challenge

Thanks alot

[ML-PHY channel estimation] HI, we have a question about the beamformer and combiners vector generation. From the matlab code used to generate the training data, we can see that for generating the beamformers(Ntrain*Lt) and combiners(Ntrain*Lr), there is a seed rng(1) for the random generation of beamformers and combiners for the number of training symbols.for the receiver to get the beamforming and combining vectors can we use the same seed with Ntrain = number of symbols(20 in the first test set) and can be used for the corresponding symbols for testing?

or are we expected to decode the data without the knowledge of these beamformers and combiners? beacuse we cant form the sensing matrix without them

https://arxiv.org/abs/1910.03510

https://github.com/fwilhelmi/machine_learning_aware_architecture_wlans

Now: Barcelona Neural Net-working Center (BNN-UPC) https://bnn.upc.edu/challenge2020

could we create our own dataset for the said problem by using deep reinforcement learning online method

Hi .. my question is there are three categories of problem statements .. Unrestricted, Restricted problems and Problems which are under progress ..... Can I select a problem which is under prgress?or the problems which are Unrestcited are only avialble to the contestants

Answer for Muhammad Usman: please select "Unrestricted Problems".

Thanks

Hint for those working in the beam selection problem: you may find useful the new tool Raymobtime_visualizer.py to understand your scenario and eventually “debug”. It is available at: https://github.com/lasseufpa/ITU-Challenge-ML5G-PHY/blob/master/Visualizer/Raymobtime_visualizer.py specially to understand the bias that the scenario imposes (for example: unbalanced dataset)

Where we could find the data ?

https://ai5gchallenge.ufpa.br/

[ML phy channel estimation] can we have a walkthrough on details and expectation channel estimation challange?

Visit the website for submitting solutions.

just a moment