Multi-task Deep Neural Networks for Massive MIMO CSI Feedback

Autor:	Zhang, Boyuan, Li, Haozhen, Liang, Xin, Gu, Xinyu, Zhang, Lin
Rok vydání:	2022
Předmět:	Computer Science - Information Theory Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Deep learning has been widely applied for the channel state information (CSI) feedback in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system. For the typical supervised training of the feedback model, the requirements of large amounts of task-specific labeled data can hardly be satisfied, and the huge training costs and storage usage of the model in multiple scenarios are hindrance for model application. In this letter, a multi-task learning-based approach is proposed to improve the feasibility of the feedback network. An encoder-shared feedback architecture and the corresponding training scheme are further proposed to facilitate the implementation of the multi-task learning approach. The experimental results indicate that the proposed multi-task learning approach can achieve comprehensive feedback performance with considerable reduction of training cost and storage usage of the feedback model. Comment: 5 pages, 2 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.12442 Zobrazit plný text záznamu View this record from Arxiv