Source CSI Dataset for Multi-Task CSI Feedback
Mayuko Inoue, Tomoaki Otsuki Ohtsuki · 2024
The muti-task Channel State Information (CSI) feedback is a downlink CSI feedback approach. It focuses on Clustered Delay Line (CDL) channel models as the channel environments. There are five different CDL channel models, from CDL-A to CDL-E. In this method, the autoencoder (source model) is pre-trained using the CDL-ALL dataset, which is an equal mixture of each CDL channel dataset (source data) rather than a single CDL channel dataset. The decoder at the BS is subsequently fine-tuned using a small amount of CSI data from the target channel environment (target data) to generate a target model. The source model has a significant influence on the reconstruction performance of the target model, the amount of data, and the number of training epochs required to obtain it. This paper investigates the mixing ratio of each CDL channel dataset used as source data to enhance the CSI reconstruction performance of the source model in the muti-task CSI feedback. Furthermore, we explore the possibility of enhancing the CSI reconstruction performance of the target model by employing the improved source model for fine-tuning the target model. To determine the mixing ratio, two distinct criteria for source model performance are employed. The simulation results identified several mixing ratios of source data in these criteria that improve the reconstruction performance of both the source and the target models compared to the use of CDL-ALL as a source dataset in the multi-task CSI feedback.