# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 1 Introduction
The data dimension gap between different datasets is large, and the prediction needs to modify the model structure. However, simple dimensional changes can lead to missing features. 2. Since the necessary location information is not embedded, the feature information of CSI is not enough to be the input of the deep learning model. 3. Existing work does not consider the global feature interaction between different antennas. 4. Different channel differential extractors are executed in parallel, which affects the convergence speed of the model. In this paper, we aim to develop an efficient method, dual-encoder-condensed convolution (DECC), to enhance the positioning accuracy and to reduce the time overheads of indoor positioning problems. Firstly, we design a layer of a convolutional neural network to align the size of the CSI; then, we embed the positional encoding matrix to enrich the position features. Secondly, in the middle of the proposed model, we design a novel encoder structure to perform a multi-head dot product operation on the combined CSI feature matrix to obtain the mutual information between antennas in multiple CSI features. Finally, we develop a convolution block with two convolution kernels of different sizes for serial feature extraction of CSI feature matrices. Our main contributions are summarized as follows. 1. We developed a Conv-for-Origin structure. It adopts a layer of multichannel convolutional neural network and positional encoding matrix to align the size of the original CSI matrix and embed position information between different sub-features, which could solve the problem of size inconsistency and lack of position information between different sub-feature datasets. 2. We propose a novel network architecture, the dual-encoder structure. It adopts two encoder structures to calculate the dot product of CSI features through Conv-for-Origin with different weights, and obtains the mutual information between different antennas in CSI features. Furthermore, due to the existence of multiple attention, the encoder can automatically delineate the molecular space to improve the richness of CSI features. 3. We propose a dual-encoder structure. It adopts two convolution kernels of different sizes to fully extract the CSI feature information. The larger one is responsible for extracting the mixed channel differences, and the smaller one is responsible for extracting the local features of the antennas and stacking them in sequence, thereby improving the positioning accuracy. Because the two different convolutions are performed in the same horizontal direction, the convergence rate of the model is very fast. We conduct extensive empirical studies in the indoor scenarios and urban canyon scenarios in Section 4. The results show that (1) our proposed method significantly improves the positioning accuracy error; (2) the DECC approach achieves a lower time overheads and a stronger robustness in terms of signal-to-noise ratio (SNR) and data scalability. We analyze the feasibility of using this model in real time and show a feasible scheme in Section 5. The rest of the paper is organized as follows. A brief overview of the related work is given in Section 2. Section 3 presents our proposed approachDECC. Section 4 presents our experimental results, and Section 5 finally concludes the paper.