标题： 自适应相位偏移信息压缩用于IRS系统：一种提示条件变量速率框架 显示英文标题

标题： Adaptive Phase Shift Information Compression for IRS Systems: A Prompt Conditioned Variable Rate Framework

摘要： 智能反射表面（IRSs）已成为提高未来无线网络频谱和能量效率的关键技术。 然而，由于在带宽受限的控制线路上频繁传输相位偏移信息（PSI）所带来的过高开销，实际实现受到阻碍。 当前基于深度学习的压缩方法缓解了这一问题，但受到解码器复杂度高、对动态信道适应性不足和静态压缩比的限制。 本研究提出了一种提示条件化的PSI压缩系统，将受大型模型启发的提示学习整合到PSI压缩过程中，以解决这些问题。 一种结合软提示拼接与逐特征线性调制（FiLM）的混合提示技术，实现了在不同信噪比（SNRs）、衰落类型和压缩比下的自适应编码。 此外，一种可变速率技术通过潜在掩码将压缩比融入提示嵌入中，使单一模型能够灵活平衡重建精度。 另外，一种轻量级深度卷积门控（DWCG）解码器在最小复杂度下实现了精确的特征重建。 全面的仿真表明，所提出的框架相比传统自动编码器基线显著降低了NMSE，在各种信道条件下保持鲁棒性，并在一个模型中支持可变压缩比。 这些发现强调了该框架作为下一代无线网络中实时IRS控制的可扩展和高效解决方案的潜力。 显示英文摘要

摘要： Intelligent reflecting surfaces (IRSs) have become a vital technology for improving the spectrum and energy efficiency of forthcoming wireless networks. Nevertheless, practical implementation is obstructed by the excessive overhead associated with the frequent transmission of phase shift information (PSI) over bandwidth-constrained control lines. Current deep learning-based compression methods mitigate this problem but are constrained by elevated decoder complexity, inadequate flexibility to dynamic channels, and static compression ratios. This research presents a prompt-conditioned PSI compression system that integrates prompt learning inspired by large models into the PSI compression process to address these difficulties. A hybrid prompt technique that integrates soft prompt concatenation with feature-wise linear modulation (FiLM) facilitates adaptive encoding across diverse signal-to-noise ratios (SNRs), fading kinds, and compression ratios. Furthermore, a variable rate technique incorporates the compression ratio into the prompt embeddings through latent masking, enabling a singular model to adeptly balance reconstruction accuracy. Additionally, a lightweight depthwise convolutional gating (DWCG) decoder facilitates precise feature reconstruction with minimal complexity. Comprehensive simulations indicate that the proposed framework significantly reduces NMSE compared to traditional autoencoder baselines, while ensuring robustness across various channel circumstances and accommodating variable compression ratios within a single model. These findings underscore the framework's promise as a scalable and efficient solution for real-time IRS control in next-generation wireless networks.