Multi-task Learning-based CSI Feedback Design in Multiple Scenarios

TL;DR

This paper introduces a multi-task learning framework with a shared encoder and multiple decoders for CSI feedback in various scenarios, reducing model complexity and device memory requirements.

Contribution

It proposes a novel S-to-M multi-task learning architecture with a classifier to select decoders, improving efficiency over traditional methods.

Findings

Significantly reduces model complexity.

Decreases user equipment memory consumption.

Outperforms benchmark modes in multi-scenario CSI feedback.

Abstract

For frequency division duplex systems, the essential downlink channel state information (CSI) feedback includes the links of compression, feedback, decompression and reconstruction to reduce the feedback overhead. One efficient CSI feedback method is the Auto-Encoder (AE) structure based on deep learning, yet facing problems in actual deployments, such as selecting the deployment mode when deploying in a cell with multiple complex scenarios. Rather than designing an AE network with huge complexity to deal with CSI of all scenarios, a more realistic mode is to divide the CSI dataset by region/scenario and use multiple relatively simple AE networks to handle subregions' CSI. However, both require high memory capacity for user equipment (UE) and are not suitable for low-level devices. In this paper, we propose a new user-friendly-designed framework based on the latter multi-tasking mode. Via Multi-Task Learning, our framework, Single-encoder-to-Multiple-decoders (S-to-M), designs the multiple independent AEs into a joint architecture: a shared encoder corresponds to multiple task-specific decoders. We also complete our framework with GateNet as a classifier to enable the base station autonomously select the right task-specific decoder corresponding to the subregion. Experiments on the simulating multi-scenario CSI dataset demonstrate our proposed S-to-M's advantages over the other benchmark modes, i.e., significantly reducing the model complexity and the UE's memory consumption