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
- Creative Commons Attribution
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.
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.
The data is generated at https://ieee-dataport.org/open-access/ultra-dense-indoor-mamimo-csi-dataset
Participants can discuss with the organizers on Slack here:
Competition Dataset Files