Massive multiple-input multiple-output (M-MIMO) aided positioning technologies have been recently recognized as promising solutions to fulfil the high accuracy requirement of indoor localization systems. In this paper, exploiting a distributed M-MIMO framework, we propose to employ a deep belief network (DBN) to analyze the received signal strengths (RSS) generated by a diffraction model, where the impact from interfering persons on the targeted user equipment (UE) is considered. Next, the preliminary DBN estimates are forwarded to a long-short term memory network (LSTMN), where the trajectory information of the targeted UE can be extracted based on much less historical trajectory information than existing solutions. Then, the three-dimension (3D) coordinates of the UE’s positions can be estimated with a back propagation neural network (BPNN) which combines the outputs of DBN and LSTMN. Finally, extensive simulation results are provided to demonstrate the effectiveness of the proposed BPNN scheme.

中文翻译：

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分布式大规模 MIMO 系统的基于衍射 RSS 的多网络辅助 3D 定位

大规模多输入多输出 (M-MIMO) 辅助定位技术最近被认为是满足室内定位系统高精度要求的有前途的解决方案。在本文中，利用分布式 M-MIMO 框架，我们建议采用深度置信网络 (DBN) 来分析衍射模型生成的接收信号强度 (RSS)，其中干扰人对目标用户设备的影响 ( UE) 被考虑。接下来，初步 DBN 估计被转发到长短期记忆网络（LSTMN），与现有解决方案相比，可以基于更少的历史轨迹信息提取目标 UE 的轨迹信息。然后，UE 位置的三维 (3D) 坐标可以通过结合了 DBN 和 LSTMN 输出的反向传播神经网络 (BPNN) 来估计。最后，提供了广泛的仿真结果来证明所提出的 BPNN 方案的有效性。