Federated Deep learning for CSI estimation in Massive MIMO environments
- Submission Dates:
- 10/14/2022 to 10/24/2022
- Citation Author(s):
- Submitted by:
- Shih-Chun Lin
- Last updated:
- Mon, 10/24/2022 - 04:43
- DOI:
- 10.21227/7png-jb17
- License:
- Creative Commons Attribution
2401 Views- Categories:
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.
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/1m60WuDv2xcwhHeGtgwsG8iZFUA35z7UXlaaF...
Participants can discuss with the organizers on Slack here:
https://join.slack.com/t/itw2022federa-bzl1204/shared_invite/zt-1hutpzcx...
Competition Dataset Files
| Attachment | Size |
|---|---|
 nomadic_dataset.zip | 474.67 MB |
| ultra_dense.zip | 17.04 GB |
| dataset_for_competition.zip | 2.67 GB |
