The concept of reconfiguring wireless propagation environments using intelligent reflecting surfaces (IRS)s has recently emerged, where an IRS comprises of a large number of passive reflecting elements that can smartly reflect the impinging electromagnetic waves for performance enhancement. Previous works have shown promising gains assuming the availability of perfect channel state information (CSI) at the base station (BS) and the IRS, which is impractical due to the passive nature of the reflecting elements. This paper makes one of the preliminary contributions of studying an IRS-assisted multi-user multiple-input single-output (MISO) communication system under imperfect CSI. Different from the few recent works that develop least-squares (LS) estimates of the IRS-assisted channel vectors, we exploit the prior knowledge of the large-scale fading statistics at the BS to derive the Bayesian minimum mean squared error (MMSE) channel estimates under a protocol in which the IRS applies a set of optimal phase shifts vectors over multiple channel estimation sub-phases. The resulting mean squared error (MSE) is both analytically and numerically shown to be lower than that achieved by the LS estimates. Joint designs for the precoding and power allocation at the BS and reflect beamforming at the IRS are proposed to maximize the minimum user signal-to-interference-plus-noise ratio (SINR) subject to a transmit power constraint. Performance evaluation results illustrate the efficiency of the proposed system and study its susceptibility to channel estimation errors.

中文翻译：

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智能反射面辅助多用户MISO通信：信道估计和波束形成设计

最近出现了使用智能反射表面（IRS）重新配置无线传播环境的概念，其中IRS由大量无源反射元素组成，这些元素可以智能地反射撞击的电磁波以增强性能。假设在基站（BS）和IRS可获得完美的信道状态信息（CSI），先前的工作已经显示出令人鼓舞的收益，由于反射元件的无源特性，这是不切实际的。本文是研究不完善CSI下IRS辅助的多用户多输入单输出（MISO）通信系统的初步贡献之一。与最近为数不多的对IRS辅助频道向量进行最小二乘（LS）估计的作品不同，我们利用BS上大型衰落统计量的先验知识来推导贝叶斯最小均方误差（MMSE）信道估计，该协议采用IRS在多个信道估计子阶段上应用一组最佳相移矢量的协议。所得的均方误差（MSE）在分析和数值上均低于LS估计所达到的均方误差。提出了针对BS处的预编码和功率分配以及IRS处的反射波束形成的联合设计，以使受发射功率约束的最小用户信号干扰加噪声比（SINR）最大化。性能评估结果说明了所提出系统的效率，并研究了其对信道估计误差的敏感性。