In this letter, we consider channel estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) systems, where an IRS is deployed to assist the data transmission from the base station (BS) to a user. It is shown that for the purpose of joint active and passive beamforming, the knowledge of a large-size cascade channel matrix needs to be acquired. To reduce the training overhead, the inherent sparsity in mmWave channels is exploited. By utilizing properties of Katri-Rao and Kronecker products, we find a sparse representation of the cascade channel and convert cascade channel estimation into a sparse signal recovery problem. Simulation results show that our proposed method can provide an accurate channel estimate and achieve a substantial training overhead reduction.

中文翻译：

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用于智能反射面辅助毫米波系统的压缩信道估计

在这封信中，我们考虑了智能反射面 (IRS) 辅助毫米波 (mmWave) 系统的信道估计，其中部署了 IRS 以协助从基站 (BS) 到用户的数据传输。结果表明，为了联合主动和被动波束成形，需要获取大尺寸级联信道矩阵的知识。为了减少训练开销，利用了毫米波信道中固有的稀疏性。通过利用 Katri-Rao 和 Kronecker 产品的特性，我们找到了级联信道的稀疏表示，并将级联信道估计转换为稀疏信号恢复问题。仿真结果表明，我们提出的方法可以提供准确的信道估计并显着减少训练开销。