Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan.
Department of Information and Computer Science, Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan.
Sensors (Basel). 2022 Aug 18;22(16):6211. doi: 10.3390/s22166211.
In this paper, we address the challenging task of estimating the distance between different users in a Millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (mMIMO) system. The conventional Time of Arrival (ToA) and Angle of Arrival (AoA) based methods need users under the Line-of-Sight (LoS) scenario. Under the Non-LoS (NLoS) scenario, the fingerprint-based method can extract the fingerprint that includes the location information of users from the channel state information (CSI). However, high accuracy CSI estimation involves a huge overhead and high computational complexity. Thus, we design a new type of fingerprint generated by beam sweeping. In other words, we do not have to know the CSI to generate fingerprint. In general, each user can record the Received Signal Strength Indicator (RSSI) of the received beams by performing beam sweeping. Such measured RSSI values, formatted in a matrix, could be seen as beam energy image containing the angle and location information. However, we do not use the beam energy image as the fingerprint directly. Instead, we use the difference between two beam energy images as the fingerprint to train a Deep Neural Network (DNN) that learns the relationship between the fingerprints and the distance between these two users. Because the proposed fingerprint is rich in terms of the users' location information, the DNN can easily learn the relationship between the difference between two beam energy images and the distance between those two users. We term it as the DNN-based inter-user distance (IUD) estimation method. Nonetheless, we investigate the possibility of using a super-resolution network to reduce the involved beam sweeping overhead. Using super-resolution to increase the resolution of low-resolution beam energy images obtained by the wide beam sweeping for IUD estimation can facilitate considerate improvement in accuracy performance. We evaluate the proposed DNN-based IUD estimation method by using original images of resolution 4 × 4, 8 × 8, and 16 × 16. Simulation results show that our method can achieve an average distance estimation error equal to 0.13 m for a coverage area of 60 × 30 m. Moreover, our method outperforms the state-of-the-art IUD estimation methods that rely on users' location information.
在本文中，我们解决了在毫米波（mmWave）大规模多输入多输出（mMIMO）系统中估计不同用户之间距离的难题。传统的到达时间（ToA）和到达角（AoA）方法需要用户在视距（LoS）场景下。在非视距（NLoS）场景下，基于指纹的方法可以从信道状态信息（CSI）中提取包含用户位置信息的指纹。然而，高精度 CSI 估计涉及巨大的开销和高计算复杂度。因此，我们设计了一种新的由波束扫描生成的指纹。换句话说，我们不需要知道 CSI 就能生成指纹。一般来说，每个用户都可以通过执行波束扫描来记录接收波束的接收信号强度指示（RSSI）。这些测量的 RSSI 值以矩阵的形式表示，可以看作是包含角度和位置信息的波束能量图像。然而，我们并没有直接将波束能量图像用作指纹。相反，我们将两个波束能量图像之间的差值用作指纹，来训练一个深度学习网络（DNN），学习指纹与这两个用户之间的距离之间的关系。由于所提出的指纹在用户位置信息方面非常丰富，因此 DNN 可以轻松地学习两个波束能量图像之间的差值与这两个用户之间的距离之间的关系。我们称之为基于 DNN 的用户间距离（IUD）估计方法。然而，我们研究了使用超分辨率网络来减少所涉及的波束扫描开销的可能性。使用超分辨率技术来提高通过宽波束扫描获得的低分辨率波束能量图像的分辨率，以方便地提高 IUD 估计的精度性能。我们通过使用分辨率为 4×4、8×8 和 16×16 的原始图像来评估所提出的基于 DNN 的 IUD 估计方法。仿真结果表明，我们的方法可以在 60×30 m 的覆盖范围内实现平均距离估计误差等于 0.13 m。此外，我们的方法优于依赖用户位置信息的最新 IUD 估计方法。
