DTL-Based CSI Feedback Combined with Continual Learning in FDD Massive MIMO Systems

The Deep Transfer Learning (DTL)-based Channel State Information (CSI) approach exploits the Deep Neural Network (DNN) model to provide low-cost CSI feedback for the target channel. It involves offline training on a given CSI dataset, followed by fine-tuning for a new environment using a small amount of collected CSI data, reducing feedback costs. However, in practical scenarios, the fine-tuned model performs worse on the source channel compared to the original source model. This leads to continued use of the poorly performing target model until it's fine-tuned again with newly acquired data. To address this, we propose combining DTL-based CSI feedback with continual learning. We introduce elastic weight consolidation (EWC) into the loss function during fine tuning. Simulations show that our method significantly reduces the degradation of the target model on the source channel, as measured by NMSE, compared to a method without continual learning.