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For frequency division duplex systems, the essential downlink channel stateinformation (CSI) feedback includes the links of compression, feedback,decompression and reconstruction to reduce the feedback overhead. One efficientCSI feedback method is the Auto-Encoder (AE) structure based on deep learning,yet facing problems in actual deployments, such as selecting the deploymentmode when deploying in a cell with multiple complex scenarios. Rather thandesigning 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 usemultiple relatively simple AE networks to handle subregions' CSI. However, bothrequire high memory capacity for user equipment (UE) and are not suitable forlow-level devices. In this paper, we propose a new user-friendly-designedframework based on the latter multi-tasking mode. Via Multi-Task Learning, ourframework, Single-encoder-to-Multiple-decoders (S-to-M), designs the multipleindependent AEs into a joint architecture: a shared encoder corresponds tomultiple task-specific decoders. We also complete our framework with GateNet asa classifier to enable the base station autonomously select the righttask-specific decoder corresponding to the subregion. Experiments on thesimulating multi-scenario CSI dataset demonstrate our proposed S-to-M'sadvantages over the other benchmark modes, i.e., significantly reducing themodel complexity and the UE's memory consumption

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