This paper investigates the uplink cascaded channel estimation for intelligent-reflecting-surface (IRS)-assisted multi-user multiple-input-single-output systems. We focus on a sub-6 GHz scenario in which the channel propagation is not sparse and the number of IRS elements can be larger than the number of BS antennas. A novel channel estimation protocol without the need for on-off amplitude control to avoid the reflection power loss is proposed. The pilot overhead is substantially reduced by exploiting the common-link structure to decompose the cascaded channel coefficients by the multiplication of the common-link variables and the user-specific variables. However, these two types of variables are highly coupled, which makes them difficult to estimate. To address this issue, we formulate an optimization-based joint channel estimation problem, which only utilizes the covariance of the cascaded channel. Then, we design a low-complexity alternating optimization algorithm with efficient initialization for the non-convex optimization problem, which achieves a local optimum solution. To further enhance the estimation accuracy, we propose a new formulation to optimize the training phase shifting configuration for the proposed protocol, and then we solve it using the successive convex approximation algorithm. Comprehensive simulations verify that the proposed algorithm has supreme performance compared to various state-of-the-art baseline schemes.

中文翻译：

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智能反射面辅助多用户 MISO 系统的上行链路级联信道估计

本文研究了智能反射面（IRS）辅助多用户多输入单输出系统的上行链路级联信道估计。我们专注于低于 6 GHz 的场景，其中信道传播不是稀疏的，IRS 元素的数量可以大于 BS 天线的数量。提出了一种无需开关幅度控制即可避免反射功率损耗的新型信道估计协议。通过利用公共链路结构通过公共链路变量和用户特定变量的乘积来分解级联信道系数，大大减少了导频开销。然而，这两种类型的变量是高度耦合的，这使得它们难以估计。为了解决这个问题，我们制定了一个基于优化的联合信道估计问题，它只利用级联通道的协方差。然后，我们针对非凸优化问题设计了一种低复杂度的具有高效初始化的交替优化算法，实现了局部最优解。为了进一步提高估计精度，我们提出了一种新的公式来优化所提出协议的训练相移配置，然后我们使用逐次凸逼近算法对其进行求解。综合仿真验证了所提出的算法与各种最先进的基线方案相比具有最高的性能。为了进一步提高估计精度，我们提出了一种新的公式来优化所提出协议的训练相移配置，然后我们使用逐次凸逼近算法对其进行求解。综合仿真验证了所提出的算法与各种最先进的基线方案相比具有最高的性能。为了进一步提高估计精度，我们提出了一种新的公式来优化所提出协议的训练相移配置，然后我们使用逐次凸逼近算法对其进行求解。综合仿真验证了所提出的算法与各种最先进的基线方案相比具有最高的性能。