Low Complexity CSI Feedback Method Using Reformer

To leverage antenna diversity effectively in Massive Multiple-Input Multiple-Output (MIMO) systems, Channel State Information (CSI) utilization at the base station (BS) is crucial. In Frequency Division Duplexing (FDD) MIMO setups, downlink CSI is acquired by user equipment (UE) and transmitted back to the BS. However, this feedback overhead diminishes communication throughput, necessitating CSI compression technologies. Methods such as traditional compression or more advanced ones such as Transformer-based neural networks which aim to address this issue suffer from low compression rates or high computational cost. In this context, Reformers emerge as a potential solution as they employ Locality-Sensitive Hashing (LSH) attention, offering a comparable accuracy to Transformers but with reduced computational demands. In this paper, we propose a CSI feedback method using Reformer. Specifically, by replacing the multi-head attention of the CSI feedback method using Transformer with the LSH attention of Reformer, we aimed to achieve similar CSI reconstruction accuracy while reducing computational complexity. Computer simulations confirm that the CSI feedback method using Reformer reduces computational complexity by about 12% while achieving comparable CSI reconstruction accuracy compared to the CSI feedback method using Transformer.