| Paper Abstract and Keywords |
| Presentation |
2024-02-16 10:50
Adapter-Based Fine-Tuning for Multi-Task Learning Based CSI Feedback in FDD Massive MIMO Systems
Mayuko Inoue, Tomoaki Ohtsuki (Keio Univ) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
A multi-task learning-based Channel State Information (CSI) feedback has been proposed to obtain the CSI of the downlink communication channel at a Base Station (BS) in massive multiple-input multiple-output (MIMO) frequency-division-duplex (FDD) system. After pre-training the encoder and the decoder with an equal mix of CSI datasets from multiple channel environments, the decoder is fine-tuned with a small amount of CSI datasets from each channel environment. In this way, CSI feedback for multiple-channel environments can be realized with only one type of encoder in the User Equipment (UE). On the other hand, the BS needs to have enough decoders for each channel environment. We also believe that new expressions currently learned by fine-tuning in each layer of decoders can be learned more effectively by multiple layers. Based on these considerations, we propose adapter-based fine-tuning. We consider three types of Adapters: Adapter 1, Adapter 2, and Adapter 3. Adapter 1, which consists of two convolutional layers, channel weights, and attention, effectively learns new representations through fine-tuning. Adapters 2 and 3, which have fewer parameters than the decoder convolutional layer, reduce the number of fine-tuning parameters. Simulation results show that Adapter 1 improves the Normalized Mean Square Error (NMSE) between the estimated CSI and the reconstructed CSI by an average of 0.4 dB compared to the conventional multi-task learning-based CSI feedback. Adapters 2 and 3 reduce the number of parameters that need to be retained in the BS by 29% and 34%, respectively, compared to multi-task learning-based CSI feedback. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
CSI feedback / multi-task learning / fine-tuning / / / / / |
| Reference Info. |
ITE Tech. Rep., vol. 48, no. 5, BCT2024-27, pp. 25-28, Feb. 2024. |
| Paper # |
BCT2024-27 |
| Date of Issue |
2024-02-08 (BCT) |
| ISSN |
Online edition: ISSN 2424-1970 |
| Download PDF |
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| Conference Information |
| Committee |
BCT IEEE-BT |
| Conference Date |
2024-02-15 - 2024-02-16 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Nagoya International Center |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
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| Paper Information |
| Registration To |
BCT |
| Conference Code |
2024-02-BCT-BT |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
Adapter-Based Fine-Tuning for Multi-Task Learning Based CSI Feedback in FDD Massive MIMO Systems |
| Sub Title (in English) |
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| Keyword(1) |
CSI feedback |
| Keyword(2) |
multi-task learning |
| Keyword(3) |
fine-tuning |
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| 1st Author's Name |
Mayuko Inoue |
| 1st Author's Affiliation |
Keio University (Keio Univ) |
| 2nd Author's Name |
Tomoaki Ohtsuki |
| 2nd Author's Affiliation |
Keio University (Keio Univ) |
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| Speaker |
Author-1 |
| Date Time |
2024-02-16 10:50:00 |
| Presentation Time |
20 minutes |
| Registration for |
BCT |
| Paper # |
BCT2024-27 |
| Volume (vol) |
vol.48 |
| Number (no) |
no.5 |
| Page |
pp.25-28 |
| #Pages |
4 |
| Date of Issue |
2024-02-08 (BCT) |