Abstract
Artificial intelligence (AI) based downlink channel state information (CSI) prediction for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems has attracted growing attention recently. However, existing works focus on the downlink CSI prediction for the users under a given environment and is hard to adapt to users in new environment especially when labeled data is limited. To address this issue, we formulate the downlink channel prediction as a deep transfer learning (DTL) problem, and propose the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained in the manner of classical deep learning and is then fine-tuned for new environments. To further improve the transfer efficiency, we propose the meta-learning algorithm that trains the network by alternating inner-task and across-task updates and then adapts to a new environment with a small number of labeled data. Simulation results show that the direct-transfer algorithm achieves better performance than the deep learning algorithm, which implies that the transfer learning benefits the downlink channel prediction in new environments. Moreover, the meta-learning algorithm significantly outperforms the direct-transfer algorithm, which validates its effectiveness and superiority.
Original language | English (US) |
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Article number | 9175003 |
Pages (from-to) | 7485-7497 |
Number of pages | 13 |
Journal | IEEE Transactions on Communications |
Volume | 68 |
Issue number | 12 |
DOIs | |
State | Published - Dec 2020 |
Keywords
- Deep transfer learning (DTL)
- FDD
- downlink CSI prediction
- few-shot learning
- massive MIMO
- meta-learning
ASJC Scopus subject areas
- Electrical and Electronic Engineering