Joint Training of the Superimposed Direct and Reflected Links in Reconfigurable Intelligent Surface Assisted Multiuser Communications
Joint Training of the Superimposed Direct and Reflected Links in Reconfigurable Intelligent Surface Assisted Multiuser Communications
复制标题
可重构智能表面辅助多用户通信中直接链路和反射链路叠加的联合训练
DOI：
10.1109/tgcn.2022.3143226
复制
发表时间：
2022-06-01
影响因子：
4.8
通讯作者：
Hanzo, Lajos
中科院分区：
文献类型：
--
作者：
An, Jiancheng;Xu, Chao;Hanzo, Lajos
在可重构智能表面（RIS）辅助系统中，通道状态信息的获取和反射系数的优化是主要的设计挑战。本文提出了一种新的基于信道训练的协议，该协议能够在性能、导频开销和复杂性之间实现灵活的权衡。更具体地说，首先，我们设想了一个整体协议，它本质上融合了现有的信道估计和无源波束形成优化，以创建一个新的统一方案。其次，我们提出了一个新的渠道培训框架。与传统的信道估计安排相比，我们的新框架将训练阶段分为几个阶段，并且具有直接估计叠加的端到端信道的引人注目的好处，而不是单独估计直接bs -用户和反映的RIS链路，这在面对高多普勒迁移率时不会使自己接近瞬时重新配置。因此，RIS反映系数通过比较多个训练周期内的目标函数值来优化，从而获得最佳性能，尽管它降低了复杂性，减少了信令和飞行员开销。第三，分析了存在信道估计误差时基于信道估计的协议和基于信道训练的协议的理论性能。最后，通过数值模拟验证了理论分析的正确性。特别是，仿真结果表明，在存在信道估计误差的情况下，基于信道训练的协议比基于信道估计的协议更具竞争力。
In reconfigurable intelligent surface (RIS)-assisted systems the acquisition of channel state information and the optimization of reflecting coefficients constitute major design challenges. In this paper, a novel channel training-based protocol is proposed, which is capable of striking a flexible trade-off between performance, pilot overhead and complexity. More specifically, first of all, we conceive a holistic protocol that intrinsically amalgamates the existing channel estimation and passive beamforming optimization for creating a new unified scheme. Secondly, we propose a new channel training framework. In contrast to the conventional channel estimation arrangements, our new framework divides the training phase into several periods and has the compelling benefit of directly estimating the superimposed end-to-end channel instead of separately estimating the direct BS-user and reflected RIS links, which would not lend itself to near-instantaneous reconfiguration in the face of high-Doppler mobility. As a result, the RIS reflecting coefficients are optimized by comparing the objective function values over multiple training periods, which leads to optimal performance, despite its reduced complexity as well as reduced signaling and pilot overhead. Thirdly, we analyze the theoretical performance of both the channel estimation-based protocol and the channel training-based protocol in the presence of channel estimation errors. Finally, our theoretical analysis is confirmed by numerical simulations. In particular, the simulation results demonstrate that our channel training-based protocol is more competitive than the channel estimation-based protocol in the presence of channel estimation errors.