标题： 基于空间复用导频的散射环境感知联合多用户信道估计与定位 显示英文标题

标题： Scattering Environment Aware Joint Multi-user Channel Estimation and Localization with Spatially Reused Pilots

摘要： 用户数量的增加导致导频开销增加，而有限的导频资源使得难以使用正交导频支持所有用户。 通过充分捕捉多用户（MU）环境的固有物理特性，可以降低导频成本并提高信道估计性能。 实际上，附近的用户可能共享相同的散射体，而距离较远的用户则倾向于具有正交信道。 本文提出了一种双时域方法，用于MIMO-OFDM系统中多用户上行链路信道估计和定位，该方法充分捕捉了MU的空间特性。 为了准确表示MU信道的结构，信道在三维位置域中进行建模。 在长时域阶段，时空间时间多重信号分类（TST-MUSIC）算法最初为每个用户提供散射体位置的粗略近似，随后通过基于密度的空间聚类应用噪声（DBSCAN）算法的散射体关联算法进行细化。 然后，基站利用这些先验信息应用基于图着色的用户分组算法，实现导频的空间复用，减少导频开销。 在短时域阶段，引入了一种低复杂度的散射环境感知位置域Turbo信道估计（SEA-LD-TurboCE）算法，以合并来自MU的重叠散射体信息，在空间复用导频下促进高精度的联合MU信道估计和定位。 仿真结果验证了所提出方案在信道估计和定位性能方面优于基线方案。 显示英文摘要

摘要： The increasing number of users leads to an increase in pilot overhead, and the limited pilot resources make it challenging to support all users using orthogonal pilots. By fully capturing the inherent physical characteristics of the multi-user (MU) environment, it is possible to reduce pilot costs and improve the channel estimation performance. In reality, users nearby may share the same scatterer, while users further apart tend to have orthogonal channels. This paper proposes a two-timescale approach for joint MU uplink channel estimation and localization in MIMO-OFDM systems, which fully captures the spatial characteristics of MUs. To accurately represent the structure of the MU channel, the channel is modeled in the 3-D location domain. In the long-timescale phase, the time-space-time multiple signal classification (TST-MUSIC) algorithm initially offers a rough approximation of scatterer positions for each user, which is subsequently refined through the scatterer association algorithm based on density-based spatial clustering of applications with noise (DBSCAN) algorithm. The BS then utilizes this prior information to apply a graph-coloring-based user grouping algorithm, enabling spatial division multiplexing of pilots and reducing pilot overhead. In the short timescale phase, a low-complexity scattering environment aware location-domain turbo channel estimation (SEA-LD-TurboCE) algorithm is introduced to merge the overlapping scatterer information from MUs, facilitating high-precision joint MU channel estimation and localization under spatially reused pilots. Simulation results verify the superior channel estimation and localization performance of our proposed scheme over the baselines.