5G mmWave MIMO systems enable accurate estimation of the user position and mapping of the radio environment using a single snapshot when both the base station (BS) and user are equipped with large antenna arrays. However, massive arrays are initially expected only at the BS side, likely leaving users with one or very few antennas. In this paper, we propose a novel method for single-snapshot localization and mapping in the more challenging case of a user equipped with a single-antenna receiver. The joint maximum likelihood (ML) estimation problem is formulated and its solution formally derived. To avoid the burden of a full-dimensional search over the space of the unknown parameters, we present a novel practical approach that exploits the sparsity of mmWave channels to compute an approximate joint ML estimate. A thorough analysis, including the derivation of the Cramér-Rao lower bounds, reveals that accurate localization and mapping can be achieved also in a MISO setup even when the direct line-of-sight path between the BS and the user is severely attenuated.

中文翻译：

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使用单天线接收器的下行链路单快照定位和映射

当基站 (BS) 和用户都配备了大型天线阵列时，5G 毫米波 MIMO 系统可以使用单个快照准确估计用户位置和映射无线电环境。然而，最初预计只在 BS 侧使用大规模阵列，可能会给用户留下一个或很少的天线。在本文中，我们提出了一种新方法，用于在配备单天线接收器的用户更具挑战性的情况下进行单快照定位和映射。联合最大似然 (ML) 估计问题被公式化并正式推导出其解决方案。为了避免对未知参数空间进行全维搜索的负担，我们提出了一种新颖的实用方法，该方法利用毫米波信道的稀疏性来计算近似联合 ML 估计。分析透彻，