Intelligent reflecting surfaces (IRSs) are considered a promising technology that can smartly reconfigure the wireless environment to enhance the performance of future wireless networks. However, the deployment of IRSs still faces challenges due to highly dynamic and mobile unmanned aerial vehicle (UAV) enabled wireless environments to achieve higher capacity. This paper sheds light on the different deployment strategies for IRSs in future terrestrial and non-terrestrial networks. Specifically, in this paper, we introduce key theoretical concepts underlying the IRS paradigm and discuss the design aspects related to the deployment of IRSs in 6G networks. We also explore optimization-based IRS deployment techniques to improve system performance in terrestrial and aerial IRSs. Furthermore, we survey model-free reinforcement learning (RL) techniques from the deployment aspect to address the challenges of achieving higher capacity in complex and mobile IRS-assisted UAV wireless systems. Finally, we highlight challenges and future research directions from the deployment aspect of IRSs for improving system performance for the future 6G network.
翻译:智能反射表面(IRS)被认为是一种大有希望的技术,能够明智地重新配置无线环境,提高未来无线网络的性能;然而,由于高度动态和移动无人驾驶飞行器使无线环境能够实现更高容量,IRS的部署仍面临挑战;本文件阐明了未来地面和非地面网络IRS的不同部署战略;具体地说,我们在本文件中介绍了IRS范式背后的关键理论概念,并讨论了在6G网络中部署IRS的设计方面;我们还探索了基于优化的IRS部署技术,以改善地面和空中IRS的系统性能;此外,我们从部署方面调查无型强化学习技术,以应对在复杂和移动IRS辅助无人机的UAV无线系统中实现更高能力的挑战;最后,我们强调了IRS部署方面的挑战和未来研究方向,以改善未来6G网络的系统性能。