The envisioned sixth-generation (6G) networks anticipate robust support for diverse applications, including massive machine-type communications, ultra-reliable low-latency communications, and enhanced mobile broadband. Intelligent Reflecting Surfaces (IRS) have emerged as a key technology capable of intelligently reconfiguring wireless propagation environments, thereby enhancing overall network performance. Traditional optimization techniques face limitations in meeting the stringent performance requirements of 6G networks due to the intricate and dynamic nature of the wireless environment. Consequently, Deep Learning (DL) techniques are employed within the IRS framework to optimize wireless system performance. This paper provides a comprehensive survey of the latest research in DL-aided IRS models, covering optimal beamforming, resource allocation control, channel estimation and prediction, signal detection, and system deployment. The focus is on presenting promising solutions within the constraints of different hardware configurations. The survey explores challenges, opportunities, and open research issues in DL-aided IRS, considering emerging technologies such as digital twins (DTs), computer vision (CV), blockchain, network function virtualization (NFC), integrated sensing and communication (ISAC), software-defined networking (SDN), mobile edge computing (MEC), unmanned aerial vehicles (UAVs), and non-orthogonal multiple access (NOMA). Practical design issues associated with these enabling technologies are also discussed, providing valuable insights into the current state and future directions of this evolving field.

中文翻译：

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用于 6G 的深度学习辅助智能反射表面：一项调查


设想中的第六代 （6G） 网络预计将为各种应用提供强大的支持，包括大规模机器类通信、超可靠的低延迟通信和增强的移动宽带。智能反射面 （IRS） 已成为一项关键技术，能够智能地重新配置无线传播环境，从而提高整体网络性能。由于无线环境的复杂性和动态性，传统的优化技术在满足 6G 网络的严格性能要求方面面临限制。因此，在 IRS 框架内采用深度学习 （DL） 技术来优化无线系统性能。本文全面综述了 DL 辅助 IRS 模型的最新研究，包括最佳波束成形、资源分配控制、信道估计和预测、信号检测和系统部署。重点是在不同硬件配置的约束下提供有前途的解决方案。该调查探讨了 DL 辅助 IRS 中的挑战、机遇和开放研究问题，考虑了数字孪生 （DT）、计算机视觉 （CV）、区块链、网络功能虚拟化 （NFC）、集成传感和通信 （ISAC）、软件定义网络 （SDN）、移动边缘计算 （MEC）、无人机 （UAV） 和非正交多址访问 （NOMA）。还讨论了与这些使能技术相关的实际设计问题，为这一不断发展的领域的现状和未来方向提供了有价值的见解。