  A potential 6G technology known as intelligent reflecting surface (IRS) has
recently gained much attention from academia and industry. However, acquiring
the optimized quantized phase shift (QPS) presents challenges for the IRS due
to the phenomenon of signaling storms. In this paper, we attempt to solve the
above problem by proposing two deep learning models, the global attention phase
shift compression network (GAPSCN) and the simplified GAPSCN (S-GAPSCN). In
GAPSCN, we propose a novel attention mechanism that emphasizes a greater number
of meaningful features than previous attention-related works. Additionally,
S-GAPSCN is built with an asymmetric architecture to meet the practical
constraints on computation resources of the IRS controller. Moreover, in
S-GAPSCN, to compensate for the performance degradation caused by simplifying
the model, we design a low-computation complexity joint attention-assisted
multi-scale network (JAAMSN) module in the decoder of S-GAPSCN. Simulation
results demonstrate that the proposed global attention mechanism achieves
prominent performance compared with the existing attention mechanisms and the
proposed GAPSCN can achieve reliable reconstruction performance compared with
existing state-of-the-art models. Furthermore, the proposed S-GAPSCN can
approach the performance of the GAPSCN at a much lower computational cost.
