# Indoor Localization Using 6G Time-Domain Feature and Deep Learning
## 1 Introduction  
Positioning technology plays a crucial role in modern 6G information processing, with applications ranging from indoor navigation to environmental monitoring [1, 2]. From a research perspective, positioning methods can generally be categorized into two types: frequency-domain and time-domain. Frequency-domain methods typically extract positioning information by analyzing the frequency characteristics of signals. In contrast, time-domain methods directly utilize the temporal characteristics of signals, such as time delay estimation for positioning. Frequency-domain methods involve analyzing signals in terms of their frequency content, and they make it easier to isolate frequencies and remove noise. However, the time resolution is poor for these methods. On the contrary, time-domain methods provide better time resolution because they analyze the signal directly as it varies over time. Nevertheless, the methods suffer from poor frequency discrimination as well as being noise-sensitive. Existing literature has extensively explored these two approaches, each demonstrating unique advantages and limitations [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. By means of the frequency-domain, in 2021, [PERSON] introduced a deep learning approach called Structured Intra-Attention Bidirectional Recurrent (SIABR) for CSI-based (Channel State Information) 3D THz indoor localization, which improved stability and performance in challenging conditions [3]. In 2022, [PERSON] introduced a novel approach to addressthe localization challenge in IEEE 802.11 ay WLAN systems, specifically tailored for indoor environments. The proposed method integrates advanced signal processing with machine learning techniques, utilizing Doppler and angular domain analysis to effectively differentiate between multipath signals. This differentiation is based on variations in range, velocity, and angular orientation of reflected signals [4]. In 2023, [PERSON] proposed an indoor Wi-Fi localization scheme that extracts both spatial and temporal features from signal data. By incorporating a fusion neural network, the proposed method significantly enhanced signal reconstruction accuracy, as demonstrated by numerical evaluations [5]. In the same year, [PERSON] introduced a hybrid approach that integrates an autoencoder-based learning framework with a two-dimensional convolutional neural network (2D-CNN). This method achieved superior performance in terms of both positioning accuracy and floor-level estimation [6]. In 2024, [PERSON] developed a cost-efficient indoor positioning system leveraging 5G downlink multibeam signals. The system demonstrated strong performance in multi-floor environments, achieving a root-mean-square positioning error of under 1.5 m. These results highlight its potential for practical deployment in mobile localization applications [7]. In 2024, [PERSON] proposed a novel deep learning model for solving the MIMO positioning problem, which incorporated two types of attention mechanisms and an improved training scheme to further enhance positioning accuracy [8]. Later, [PERSON] presented a positioning system that extracted and processed CSI from a single Access Point (AP) to localize commercial smartphones [9]. In the same year, [PERSON] explored 6G technology for indoor positioning, focusing on accuracy and reliability using Convolutional Neural Networks (CNNs) with CSI at terahertz frequencies and advanced AI algorithms, which achieved centimeter-level accuracy [10]. By means of the time-domain, in 1999, [PERSON] employed a site-specific model to analyze the performance of millimeter-wave Binary Phase-Shift Keying (BPSK) systems with single cochannel interference. Impulse responses for different room types were computed via the shooting and bouncing rays technique. The Bit Error Rates (BER) and carrier-to-interference ratios were calculated, with findings indicating that interference was more significant in rooms with plasterboard-wall partitions compared to those with concrete-wall partitions [11].