标题： 智能反射表面辅助的多用户OFDMA：信道估计与训练设计 显示英文标题

标题： Intelligent Reflecting Surface Assisted Multi-User OFDMA: Channel Estimation and Training Design

摘要： 为了实现智能反射表面（IRS）的全部被动波束成形增益，准确的信道状态信息（CSI）是必不可少的，但由于需要估计的信道参数数量过多，随着IRS反射元件的数量以及IRS服务的用户数量的增加而增加，因此在实践中具有挑战性。 为了解决这一挑战，本文提出两种高效的信道估计方案，适用于采用正交频分多址接入（OFDMA）的IRS辅助多用户宽带通信系统中的不同信道配置。 第一种信道估计方案在接入点（AP）同时并行估计所有用户的CSI，适用于任意频率选择性衰落信道。 相比之下，第二种信道估计方案利用所有用户共享相同的（公共）IRS-AP信道这一关键特性，以提高训练效率并支持更多用户，适用于典型的视距（LoS）主导的用户-IRS信道场景。 对于这两种提出的信道估计方案，我们进一步优化了相应的训练设计（包括所有用户的导频音分配和IRS时变反射模式），以最小化信道估计误差。 此外，我们推导并比较了这两种方案在最小训练开销和最大支持用户数方面的基本极限。 仿真结果验证了所提出的信道估计方案和训练设计的有效性，并显示它们在各种基准方案上的显著性能提升。 显示英文摘要

摘要： To achieve the full passive beamforming gains of intelligent reflecting surface (IRS), accurate channel state information (CSI) is indispensable but practically challenging to acquire, due to the excessive amount of channel parameters to be estimated which increases with the number of IRS reflecting elements as well as that of IRS-served users. To tackle this challenge, we propose in this paper two efficient channel estimation schemes for different channel setups in an IRS-assisted multi-user broadband communication system employing the orthogonal frequency division multiple access (OFDMA). The first channel estimation scheme, which estimates the CSI of all users in parallel simultaneously at the access point (AP), is applicable for arbitrary frequency-selective fading channels. In contrast, the second channel estimation scheme, which exploits a key property that all users share the same (common) IRS-AP channel to enhance the training efficiency and support more users, is proposed for the typical scenario with line-of-sight (LoS) dominant user-IRS channels. For the two proposed channel estimation schemes, we further optimize their corresponding training designs (including pilot tone allocations for all users and IRS time-varying reflection pattern) to minimize the channel estimation error. Moreover, we derive and compare the fundamental limits on the minimum training overhead and the maximum number of supportable users of these two schemes. Simulation results verify the effectiveness of the proposed channel estimation schemes and training designs, and show their significant performance improvement over various benchmark schemes.