# Indoor Localization Using 6G Time-Domain Feature and Deep Learning
## 1 Introduction
An attention-assisted Ultra-Wideband (UWB) ranging error compensation algorithm was proposed by [PERSON] in 2023, enabling the reevaluation of the significance of extracted UWB channel characteristics across different environments. This approach enhanced the performance of the Deep Neural Network (DNN) model [12]. In 2024, [PERSON] proposed a dual-channel neural network to accurately recognize Non-Line-Of-Sight (NLOS) and Line-Of-Sight (LOS) signals in UWB positioning systems. By integrating both time-domain and time-frequency-domain features, the network processed UWB Channel Impulse Response (CIR) signals using a combination of CNN, Time Convolutional Networks (TCNs), and self-attention, achieving high classification accuracy across various complex environments and enhancing positioning performance [13]. In 2024, [PERSON] compared classical fingerprinting with Decision Tree Regressor (DTR)-based algorithms to improve indoor localization in 5G and WiFi environments. This paper demonstrated how machine learning enhanced robustness when radio maps were incomplete during training and discussed the benefits of technology fusion for precise positioning. [14]. In 2024, [PERSON] proposed a paradigm of Localization-oriented Digital Twins (LocDT) with a compound architecture of seven sub-DT layers to characterize the 6G Integrated-Localization-And-Communication (ILAC) feature. LocDT started from a physical environment sublayer to mirror 6G signal interactions within a real-world scenario, along with an ILAC baseband sublayer and a channel frequency polar-coordinate image construction method to provide finer-grained fingerprints [15]. In 2025, Sun constructed a high-precision indoor positioning system by integrating Wi-Fi round-trip time, magnetic field sensing, and pedestrian dead reckoning. Numerical results demonstrated that the proposed fusion model achieved a localization accuracy ranging from 0.64 to 0.91 m [16]. In the same year, [PERSON] proposed an ultra-wideband positioning approach based on a novel position fingerprinting technique and a parallel convolutional neural network. The proposed scheme achieved a mean absolute error of just 0.173 m, indicating high precision in indoor localization tasks [17]. However, we notice that the accuracy of existing methods decreases under high noise and interference, and the CLAB and SLAB mechanisms effectively overcome this problem. Experimental results have demonstrated improvements in stability and anti-interference capabilities. To the best of our knowledge, there has been no research on a time-domain positioning system using ResNet with Channel Local Attention Block (CLAB) and Spatial Local Attention Block (SLAB). This paper first introduces ResNet with CLAB and SLAB for localization by means of time-domain data. The distinctive features of this work include the following:  
1. We employ the time-domain approach using the ResNet to effectively leverage channel information, further enhancing the accuracy of the localization results;
2. We introduce two advanced attention mechanisms, CLAB and SLAB, that significantly boost model performance, elevating its effectiveness to a new level;
3. Existing localization methods often struggle under severe noise and interference, leading to significant accuracy degradation. This paper addresses such limitations by integrating CLAB and SLAB into the ResNet framework, enhancing noise robustness. Experimental results show that under 20% Gaussian noise and an additional 20% interference, our proposed method reduces RMSE by up to 56.5% compared to baseline ResNet, clearly demonstrating superior stability and accuracy in harsh environments. Section 2 discusses the 6G channel model and system design. Section 3 details the proposed ResNet architecture with CLAB and SLAB. Section 4 presents experimental results. Section 5 concludes the study.