Next-slot OFDM-CSI Prediction: Multi-head Self-attention or State Space Model?

TL;DR

This paper compares multi-head self-attention and state-space models for 5G OFDM channel state prediction, finding MSAs generally outperform SSMs in MIMO scenarios, guiding future deep learning architecture choices.

Contribution

It provides a comprehensive benchmark of MSA and SSM layers for 5G CSI prediction, highlighting their strengths and limitations across different scenarios.

Findings

SSMs outperform MSAs in SISO cases.

MSAs outperform SSMs in MIMO scenarios.

Overall, MSAs are favored for future 5G DL applications.

Abstract

The ongoing fifth-generation (5G) standardization is exploring the use of deep learning (DL) methods to enhance the new radio (NR) interface. Both in academia and industry, researchers are investigating the performance and complexity of multiple DL architecture candidates for specific one-sided and two-sided use cases such as channel state estimation (CSI) feedback, CSI prediction, beam management, and positioning. In this paper, we set focus on the CSI prediction task and study the performance and generalization of the two main DL layers that are being extensively benchmarked within the DL community, namely, multi-head self-attention (MSA) and state-space model (SSM). We train and evaluate MSA and SSM layers to predict the next slot for uplink and downlink communication scenarios over urban microcell (UMi) and urban macrocell (UMa) OFDM 5G channel models. Our numerical results demonstrate that SSMs exhibit better prediction and generalization capabilities than MSAs only for SISO cases. For MIMO scenarios, however, the MSA layer outperforms the SSM one. While both layers represent potential DL architectures for future DL-enabled 5G use cases, the overall investigation of this paper favors MSAs over SSMs.