论文标题
IRS辅助无线系统的控制信号传导减少的相移压缩：全球关注和轻巧设计
Phase Shift Compression for Control Signaling Reduction in IRS-Aided Wireless Systems: Global Attention and Lightweight Design
论文作者
论文摘要
一种被称为智能反射表面（IRS）的潜在6G技术最近引起了学术界和工业的关注。但是，由于信号风暴的现象，获取优化的量化相移（QP）对IRS提出了挑战。在本文中，我们试图通过提出两个深度学习模型：全球注意相位移动压缩网络（GAPSCN）和简化的GAPSCN（S-GAPSCN）来解决上述问题。在GAPSCN中，我们提出了一种新颖的注意机制，该机制强调了比以前与注意力相关的作品更大的有意义的特征。此外，S-企业是使用不对称架构构建的，以满足IRS控制器计算资源的实际限制。此外，在S-企业中，为了补偿通过简化模型引起的性能降低，我们设计了S-GAPSCN解码器中的低功能复杂性联合注意力辅助多尺度网络（JAAMSN）模块。仿真结果表明，与现有的注意机制相比，提出的全球注意力机制达到了突出的性能，与现有的最新模型相比，所提出的GAPSCN可以实现可靠的重建性能。此外，拟议的S-GAPSCN可以以低得多的计算成本来处理GAPSCN的性能。
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.
