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A potential 6G technology known as intelligent reflecting surface (IRS) hasrecently gained much attention from academia and industry. However, acquiringthe optimized quantized phase shift (QPS) presents challenges for the IRS dueto the phenomenon of signaling storms. In this paper, we attempt to solve theabove problem by proposing two deep learning models, the global attention phaseshift compression network (GAPSCN) and the simplified GAPSCN (S-GAPSCN). InGAPSCN, we propose a novel attention mechanism that emphasizes a greater numberof meaningful features than previous attention-related works. Additionally,S-GAPSCN is built with an asymmetric architecture to meet the practicalconstraints on computation resources of the IRS controller. Moreover, inS-GAPSCN, to compensate for the performance degradation caused by simplifyingthe model, we design a low-computation complexity joint attention-assistedmulti-scale network (JAAMSN) module in the decoder of S-GAPSCN. Simulationresults demonstrate that the proposed global attention mechanism achievesprominent performance compared with the existing attention mechanisms and theproposed GAPSCN can achieve reliable reconstruction performance compared withexisting state-of-the-art models. Furthermore, the proposed S-GAPSCN canapproach the performance of the GAPSCN at a much lower computational cost.

Phase Shift Compression for Control Signaling Reduction in IRS-Aided Wireless Systems: Global Attention and Lightweight Design