Multiple-input multiple-output (MIMO) is an enabling technology to meet the growing demand for faster and more reliable communications in wireless networks with a large number of terminals, but it can also be applied for position estimation of a terminal exploiting multipath propagation from multiple antennas. In this paper, we investigate new convolutional neural network (CNN) structures for exploiting MIMO-based channel state information (CSI) to improve indoor positioning. We evaluate and compare the performance of three variants of the proposed CNN structure to five NN structures proposed in the scientific literature using the same sets of training-evaluation data. The results demonstrate that the proposed residual convolutional NN structure improves the accuracy of position estimation and keeps the total number of weights lower than the published NN structures. The proposed CNN structure yields from 2cm to 10cm better position accuracy than known NN structures used as a reference.
翻译:多种投入的多重产出(MIMO)是一种使技术,它能够满足大量终端的无线网络对更快和更可靠的通信日益增长的需求,但也可用于对利用多天线多路传播的终端进行定位估计。在本文件中,我们调查利用IMO频道国家信息的新的进化神经网络(CNN)结构以改善室内定位。我们利用同样的成套培训评价数据,对拟议的CNN结构的三个变体的性能与科学文献中提议的5个NN结构的性能进行评估和比较。结果显示,拟议的剩余CNNN结构提高了定位估计的准确性,并使总重量低于已公布的NNN结构。拟议的CNN结构比已知用作参考的NN结构的精度从2厘米到10厘米不等。