Skip to main content

Competition

Archived Competition

Federated Deep learning for CSI estimation in Massive MIMO environments

Dataset image
Submission Dates:
to
Citation Author(s):
Sibren De Bast (KU Leuven)
Sofie Pollin (KU Leuven)
Submitted by:
Shih-Chun Lin
Last updated:
DOI:
10.21227/7png-jb17
Categories:
ACCESS DATASET CITE SHARE/EMBED

Abstract

Machine learning methods are poised to drastically improve the performance of many aspects of communication engineering, across all layers of the communication stack: from the physical layer to the application one.  In this competition, we focus on the problem of federated training of a deep CSI compressor for massive MIMO in 5G protocols and beyond.

Instructions:

Objective:

A set of remote users observe a set of pilot signals as transmitted by a MIMO base station (BS) and are tasked with the distributed training of a compressor for the channel estimate. The training of this compressor occurs in a distributed manner, with the BS orchestrating the training and maintaining a centralized model. Training must occur within a set communication budget and model size. 

Data:

The data is generated at https://ieee-dataport.org/open-access/ultra-dense-indoor-mamimo-csi-dataset

Competition specs:

https://docs.google.com/document/d/1m60WuDv2xcwhHeGtgwsG8iZFUA35z7UXlaaF9El2ZaU/edit?usp=sharing

Participants can discuss with the organizers on Slack here:
https://join.slack.com/t/itw2022federa-bzl1204/shared_invite/zt-1hutpzcxu-gKUKd0nsnOFm0xbwqRR9eQ

COMPETITION DATASET FILES

You must be an approved participant in this data competition to access dataset files.
To request access you must first login.

Login

QUESTIONS?