论文标题
智能反映表面辅助的MIMO的结构意识到稀疏贝叶斯学习的渠道估计
Structure-aware Sparse Bayesian Learning-based Channel Estimation for Intelligent Reflecting Surface-aided MIMO
论文作者
论文摘要
本文介绍了一种智能反映表面辅助多输入多输出系统的新型级联通道估计技术。由较高频带的通道角稀疏性激励，通道估计问题被提出为固有的kronecker结构的稀疏矢量恢复问题。我们使用稀疏的贝叶斯学习框架解决了问题，该框架导致非凸优化问题。我们基于交替的最小化和单数值分解为问题提供了两种解决方案技术。我们的仿真结果说明了与现有作品相比的准确性和运行时间的出色性能。
This paper presents novel cascaded channel estimation techniques for an intelligent reflecting surface-aided multiple-input multiple-output system. Motivated by the channel angular sparsity at higher frequency bands, the channel estimation problem is formulated as a sparse vector recovery problem with an inherent Kronecker structure. We solve the problem using the sparse Bayesian learning framework which leads to a non-convex optimization problem. We offer two solution techniques to the problem based on alternating minimization and singular value decomposition. Our simulation results illustrate the superior performance of our methods in terms of accuracy and run time compared with the existing works.
