Channel estimation is a practical challenge for intelligent reflecting surface (IRS) aided wireless communication. As the number of IRS reflecting elements or IRS-aided users increases, the channel training overhead becomes excessively high, which results in long delay and low throughput in data transmission. To tackle this challenge, we propose in this paper a new anchor-assisted channel estimation approach, where two anchor nodes, namely A1 and A2, are deployed near the IRS for facilitating its aided base station (BS) in acquiring the cascaded BS-IRS-user channels required for data transmission. Specifically, in the first scheme, the partial channel state information (CSI) on the element-wise channel gain square of the common BS-IRS link for all users is first obtained at the BS via the anchor-assisted training and feedback. Then, by leveraging such partial CSI, the cascaded BS-IRS-user channels are efficiently resolved at the BS with additional training by the users. While in the second scheme, the BS-IRS-A1 and A1-IRS-A2 channels are first estimated via the training by A1. Then, with additional training by A2, all users estimate their individual cascaded A2-IRS-user channels simultaneously. Based on the CSI fed back from A2 and all users, the BS resolves the cascaded BS-IRS-user channels efficiently. In both schemes, the quasi-static channels among the fixed BS, IRS, and two anchors are estimated off-line only, which greatly reduces the real-time training overhead. Simulation results demonstrate that our proposed anchor-assisted channel estimation schemes achieve superior performance as compared to existing IRS channel estimation schemes, under various practical setups. In addition, the first proposed scheme outperforms the second one when the number of antennas at the BS is sufficiently large, and vice versa.

中文翻译：

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智能反射面辅助多用户通信的锚辅助信道估计

对于智能反射面（IRS）辅助的无线通信来说，信道估计是一项实际挑战。随着IRS反射元素或IRS辅助用户的数量增加，信道训练开销会变得过高，从而导致较长的延迟和较低的数据传输吞吐量。为了应对这一挑战，我们在本文中提出了一种新的锚定辅助信道估计方法，其中在IRS附近部署两个锚定节点A1和A2，以帮助其辅助基站（BS）获取级联BS-IRS -数据传输所需的用户通道。具体地，在第一方案中，首先经由锚定辅助训练和反馈在BS处获得针对所有用户的，公共BS-IRS链路的逐元素信道增益平方的部分信道状态信息（CSI）。然后，通过利用这样的部分CSI，级联的BS-IRS用户信道在BS处通过用户的额外训练而被有效地解析。在第二方案中，首先通过A1的训练来估计BS-IRS-A1和A1-IRS-A2信道。然后，在A2的额外培训下，所有用户同时估计其各自的级联A2-IRS用户信道。BS基于从A2和所有用户反馈的CSI，有效地解析级联的BS-IRS用户信道。在这两种方案中，固定BS，IRS和两个锚点之间的准静态信道仅是离线估计的，这大大减少了实时训练开销。仿真结果表明，在各种实际设置下，与现有的IRS信道估计方案相比，我们提出的锚辅助信道估计方案具有更高的性能。