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Keywords = Channel Impulse Response (CIR)

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32 pages, 2219 KiB  
Article
Intelligent Health Monitoring in 6G Networks: Machine Learning-Enhanced VLC-Based Medical Body Sensor Networks
by Bilal Antaki, Ahmed Hany Dalloul and Farshad Miramirkhani
Sensors 2025, 25(11), 3280; https://doi.org/10.3390/s25113280 - 23 May 2025
Cited by 1 | Viewed by 1129
Abstract
Recent advances in Artificial Intelligence (AI)-driven wireless communication are driving the adoption of Sixth Generation (6G) technologies in crucial environments such as hospitals. Visible Light Communication (VLC) leverages existing lighting infrastructure to deliver high data rates while mitigating electromagnetic interference (EMI); however, patient [...] Read more.
Recent advances in Artificial Intelligence (AI)-driven wireless communication are driving the adoption of Sixth Generation (6G) technologies in crucial environments such as hospitals. Visible Light Communication (VLC) leverages existing lighting infrastructure to deliver high data rates while mitigating electromagnetic interference (EMI); however, patient movement induces fluctuating signal strength and dynamic channel conditions. In this paper, we present a novel integration of site-specific ray tracing and machine learning (ML) for VLC-enabled Medical Body Sensor Networks (MBSNs) channel modeling in distinct hospital settings. First, we introduce a Q-learning-based adaptive modulation scheme that meets target symbol error rates (SERs) in real time without prior environmental information. Second, we develop a Long Short-Term Memory (LSTM)-based estimator for path loss and Root Mean Square (RMS) delay spread under dynamic hospital conditions. To our knowledge, this is the first study combining ray-traced channel impulse response modeling (CIR) with ML techniques in hospital scenarios. The simulation results demonstrate that the Q-learning method consistently achieves SERs with a spectral efficiency (SE) lower than optimal near the threshold. Furthermore, LSTM estimation shows that D1 has the highest Root Mean Square Error (RMSE) for path loss (1.6797 dB) and RMS delay spread (1.0567 ns) in the Intensive Care Unit (ICU) ward, whereas D3 exhibits the highest RMSE for path loss (1.0652 dB) and RMS delay spread (0.7657 ns) in the Family-Type Patient Rooms (FTPRs) scenario, demonstrating high estimation accuracy under realistic conditions. Full article
(This article belongs to the Special Issue Recent Advances in Optical Wireless Communications)
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17 pages, 7596 KiB  
Article
Phase Estimation Using an Optimization Algorithm to Improve Ray-Based Blind Deconvolution Performance
by Wonjun Yang and Dong-Gyun Han
J. Mar. Sci. Eng. 2025, 13(4), 704; https://doi.org/10.3390/jmse13040704 - 1 Apr 2025
Viewed by 448
Abstract
Ray-based blind deconvolution (RBD) is a technique for estimating the source-to-receiver array channel impulse response (CIR) without prior knowledge of the source waveform. Given its diverse applications, including source–receiver range estimation and the inversion of ocean waveguide parameters, RBD has been actively studied [...] Read more.
Ray-based blind deconvolution (RBD) is a technique for estimating the source-to-receiver array channel impulse response (CIR) without prior knowledge of the source waveform. Given its diverse applications, including source–receiver range estimation and the inversion of ocean waveguide parameters, RBD has been actively studied in underwater acoustics. However, the accuracy of CIR estimation in RBD may be compromised by phase uncertainty in the source waveform, necessitating enhancements in its performance. This paper proposes a method to improve RBD performance by estimating the phase of the source waveform using an optimization algorithm. Specifically, the particle swarm optimization (PSO) algorithm is employed to minimize phase estimation errors by optimizing the time delay for each receiver to maximize the beamformer output. The effectiveness of the proposed method was evaluated using two types of source signals: ship noise and linear frequency modulation (LFM), which corresponded to relatively low- and high-frequency sources, respectively. Performance comparisons with conventional RBD across various source-to-vertical line array distances revealed that the proposed method yielded more compact arrival paths with reduced time spread and a higher signal-to-noise ratio at short distances in the low-frequency band, and it consistently outperformed conventional RBD at all distances in the high-frequency band. Full article
(This article belongs to the Topic Advances in Underwater Acoustics and Aeroacoustics)
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21 pages, 2351 KiB  
Article
On the Design of Effective New Radio Sounding Reference Signal-Based Channel Estimation: Linear Regression with Channel Impulse Response Refinement
by Yoon-Seok Choi, Ji-Young Hwang and Sang-Won Choi
Electronics 2025, 14(7), 1374; https://doi.org/10.3390/electronics14071374 - 29 Mar 2025
Viewed by 518
Abstract
In this paper, we introduce a robust framework for linear regression–based channel estimation (CE) designed for multipath channel environments within a new radio (NR) sounding reference signal (SRS) system. The main contribution of this study is to show that integrating channel impulse response [...] Read more.
In this paper, we introduce a robust framework for linear regression–based channel estimation (CE) designed for multipath channel environments within a new radio (NR) sounding reference signal (SRS) system. The main contribution of this study is to show that integrating channel impulse response (CIR) refinement with existing CE schemes significantly improves CE performance in terms of normalized mean squared error (NMSE). Specifically, our approach employs thresholding-based CIR refinement to eliminate noise tap components effectively, discern the lengths of dominant tap elements, and augment linear regression–based CE’s efficacy. Specifically, it is shown that increasing the number of channel taps for threshold setting further enhances the performance of regression–based CE by leveraging CIR refinement. By utilizing an optimized threshold design, our results reveal close performance compared to both ideal tap information-based regression and the theoretical performance of linear minimum mean square error (LMMSE) estimation, whose findings are substantiated by numerical analyses employing our proposed polynomial regression–based channel estimation (PRCE) and DFT regression–based channel estimation (DRCE) schemes. Full article
(This article belongs to the Special Issue Advances in Signals and Systems Research)
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22 pages, 1440 KiB  
Article
Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals
by Marcin Bednarz and Tomasz P. Zielinski
Sensors 2025, 25(2), 337; https://doi.org/10.3390/s25020337 - 9 Jan 2025
Cited by 2 | Viewed by 1689
Abstract
In this paper, the idea of a radar based on orthogonal frequency division multiplexing (OFDM) is applied to 5G NR Positioning Reference Signals (PRS). This study demonstrates how the estimation of the communication channel using the PRS can be applied for the identification [...] Read more.
In this paper, the idea of a radar based on orthogonal frequency division multiplexing (OFDM) is applied to 5G NR Positioning Reference Signals (PRS). This study demonstrates how the estimation of the communication channel using the PRS can be applied for the identification of objects moving near the 5G NR receiver. In this context, this refers to a 5G NR base station capable of detecting a high-speed train (HST). The anatomy of a 5G NR frame as a sequence of OFDM symbols is presented, and different PRS configurations are described. It is shown that spectral analysis of time-varying channel impulse response weights, estimated with the help of PRS pilots, can be used for the detection of transmitted signal reflections from moving vehicles and the calculation of their time and frequency/Doppler shifts. Different PRS configurations with varying time and frequency reference signal densities are tested in simulations. The peak-to-noise-floor ratio (PNFR) of the calculated radar range–velocity maps (RVM) is used for quantitative comparison of PRS-based radar scenarios. Additionally, different echo signal strengths are simulated while also checking various observation window lengths (FFT lengths). This study proves the practicality of using PRS pilots in remote sensing; however, it shows that the most dense configurations do not provide notable improvements, while also demanding considerably more resources. Full article
(This article belongs to the Special Issue Remote Sensing-Based Intelligent Communication)
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22 pages, 3424 KiB  
Article
A Line of Sight/Non Line of Sight Recognition Method Based on the Dynamic Multi-Level Optimization of Comprehensive Features
by Ziyao Ma, Zhongliang Deng, Zidu Tian, Yingjian Zhang, Jizhou Wang and Jilong Guo
Sensors 2025, 25(2), 304; https://doi.org/10.3390/s25020304 - 7 Jan 2025
Cited by 1 | Viewed by 1098
Abstract
With the advent of the 5G era, high-precision localization based on mobile communication networks has become a research hotspot, playing an important role in indoor emergency rescue in shopping malls, smart factory management and tracking, as well as precision marketing. However, in complex [...] Read more.
With the advent of the 5G era, high-precision localization based on mobile communication networks has become a research hotspot, playing an important role in indoor emergency rescue in shopping malls, smart factory management and tracking, as well as precision marketing. However, in complex environments, non-line-of-sight (NLOS) propagation reduces the measurement accuracy of 5G signals, causing large deviations in position solving. In order to obtain high-precision position information, it is necessary to recognize the propagation state of the signal before distance measurement or angle measurement. In this paper, we propose a dynamic multi-level optimization of comprehensive features (DMOCF) network model for line-of-sight (LOS)/NLOS identification. The DMOCF model improves the expression ability of the deep model by adding a res2 module to the time delay neural network (TDNN), so that fine-grained feature information such as weak reflections or noise in the signal can be deeply understood by the model, enabling the network to realize layer-level feature processing by adding Squeeze and Excitation (SE) blocks with adaptive weight adjustment for each layer. A mamba module with position coding is added to each layer to capture the local patterns of wireless signals under complex propagation phenomena by extracting local features, enabling the model to understand the evolution of signals over time in a deeper way. In addition, this paper proposes an improved sand cat search algorithm for network parameter search, which improves search efficiency and search accuracy. Overall, this new network architecture combines the capabilities of local feature extraction, global feature preservation, and time series modeling, resulting in superior performance in the 5G channel impulse response (CIR) signal classification task, improving the accuracy of the model and accurately identifying the key characteristics of multipath signal propagation. Experimental results show that the NLOS/LOS recognition method proposed in this paper has higher accuracy than other deep learning methods. Full article
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20 pages, 963 KiB  
Article
A Sub-Channel Spatial Homogeneity-Based Channel Estimation Method for Underwater Optical Densely Arrayed MIMO Systems
by Guojin Peng, Hongbing Qiu, Yanlong Li and Junru Wang
J. Mar. Sci. Eng. 2024, 12(11), 2030; https://doi.org/10.3390/jmse12112030 - 10 Nov 2024
Cited by 1 | Viewed by 1123
Abstract
The limited surface area and structural constraints of small underwater communication devices necessitate a dense placement of transmitting and receiving array elements in optical multiple-input multiple-output (MIMO) systems. The compact layout leads to the formation of sub-channels that exhibit notable spatial correlation and [...] Read more.
The limited surface area and structural constraints of small underwater communication devices necessitate a dense placement of transmitting and receiving array elements in optical multiple-input multiple-output (MIMO) systems. The compact layout leads to the formation of sub-channels that exhibit notable spatial correlation and a tendency toward homogeneity. Although sub-channel spatial homogeneity (SSH) may diminish the communication capacity of MIMO systems, it provides a significant advantage by reducing the pilot overhead. In this study, we exploit the inherent SSH and the natural time-domain sparsity of channel impulse response (CIR) in the underwater optical densely arrayed MIMO (UODA-MIMO) system to propose an innovative SSH-based channel estimation (SSH-CE) method. We model the underwater optical CIR at Gbaud rates and integrate it with SSH characteristics. This approach transforms the reconstruction targets of compressive sensing (CS) from conventional CIR samples to prior CIR model parameters and the fitting residuals of the homogeneous sub-channels, reducing the pilot overhead. The simulation results of photon tracing for UODA-MIMO sub-channels in turbid harbor water indicate a monotonic, exponential decay in CIR at Gbaud rates, with transmission delays exceeding 5 nanoseconds for distances over 8 m. Moreover, the correlation coefficients among sub-channels reach a minimum of 0.975, confirming the presence of SSH in UODA-MIMO systems. In comparison to existing CS methods that rely on known sparsity, sparsity adaptation, and the structural sparsity of MIMO channels, the SSH-CE method achieves a lower degree of sparsity in reconstruction targets and a reduced lower bound for pilot requirements under the SPARK criterion. Specifically, the SSH-CE method achieves a reduction in the pilot overhead for reconstructing Nt sub-channels of K-sparse to 2Nt irrespective of CIR residual compensation. Full article
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37 pages, 12365 KiB  
Article
A Novel Underwater Wireless Optical Communication Optical Receiver Decision Unit Strategy Based on a Convolutional Neural Network
by Intesar F. El Ramley, Nada M. Bedaiwi, Yas Al-Hadeethi, Abeer Z. Barasheed, Saleha Al-Zhrani and Mingguang Chen
Mathematics 2024, 12(18), 2805; https://doi.org/10.3390/math12182805 - 10 Sep 2024
Viewed by 2116
Abstract
Underwater wireless optical communication (UWOC) systems face challenges due to the significant temporal dispersion caused by the combined effects of scattering, absorption, refractive index variations, optical turbulence, and bio-optical properties. This collective impairment leads to signal distortion and degrades the optical receiver’s bit [...] Read more.
Underwater wireless optical communication (UWOC) systems face challenges due to the significant temporal dispersion caused by the combined effects of scattering, absorption, refractive index variations, optical turbulence, and bio-optical properties. This collective impairment leads to signal distortion and degrades the optical receiver’s bit error rate (BER). Optimising the receiver filter and equaliser design is crucial to enhance receiver performance. However, having an optimal design may not be sufficient to ensure that the receiver decision unit can estimate BER quickly and accurately. This study introduces a novel BER estimation strategy based on a Convolutional Neural Network (CNN) to improve the accuracy and speed of BER estimation performed by the decision unit’s computational processor compared to traditional methods. Our new CNN algorithm utilises the eye diagram (ED) image processing technique. Despite the incomplete definition of the UWOC channel impulse response (CIR), the CNN model is trained to address the nonlinearity of seawater channels under varying noise conditions and increase the reliability of a given UWOC system. The results demonstrate that our CNN-based BER estimation strategy accurately predicts the corresponding signal-to-noise ratio (SNR) and enables reliable BER estimation. Full article
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14 pages, 24620 KiB  
Article
Improvement of a Green’s Function Estimation for a Moving Source Using the Waveguide Invariant Theory
by Daehwan Kim, Donghyeon Kim, Gihoon Byun, Jeasoo Kim and Heechun Song
Sensors 2024, 24(17), 5782; https://doi.org/10.3390/s24175782 - 5 Sep 2024
Cited by 2 | Viewed by 1342
Abstract
Understanding the characteristics of underwater sound channels is essential for various remote sensing applications. Typically, the time-domain Green’s function or channel impulse response (CIR) is obtained using computationally intensive acoustic propagation models that rely on accurate environmental data, such as sound speed profiles [...] Read more.
Understanding the characteristics of underwater sound channels is essential for various remote sensing applications. Typically, the time-domain Green’s function or channel impulse response (CIR) is obtained using computationally intensive acoustic propagation models that rely on accurate environmental data, such as sound speed profiles and bathymetry. Ray-based blind deconvolution (RBD) offers a less computationally demanding alternative using plane-wave beamforming to estimate the Green’s function. However, the presence of noise can obscure low coherence ray arrivals, making accurate estimation challenging. This paper introduces a method using the waveguide invariant to improve the signal-to-noise ratio (SNR) of broadband Green’s functions for a moving source without prior knowledge of range. By utilizing RBD and the frequency shifts from the striation slope, we coherently combine individual Green’s functions at adjacent ranges, significantly improving the SNR. In this study, we demonstrated the proposed method via simulation and broadband noise data (200–900 Hz) collected from a moving ship in 100 m deep shallow water. Full article
(This article belongs to the Section Environmental Sensing)
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23 pages, 7568 KiB  
Article
1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
by Qiu Wang, Mingsong Chen, Jiajie Liu, Yongcheng Lin, Kai Li, Xin Yan and Chizhou Zhang
Appl. Sci. 2024, 14(17), 7609; https://doi.org/10.3390/app14177609 - 28 Aug 2024
Cited by 3 | Viewed by 2275
Abstract
Ultra-Wideband (UWB) technology is crucial for indoor localization systems due to its high accuracy and robustness in multipath environments. However, Non-Line-of-Sight (NLoS) conditions can cause UWB signal distortion, significantly reducing positioning accuracy. Thus, distinguishing between NLoS and LoS scenarios and mitigating positioning errors [...] Read more.
Ultra-Wideband (UWB) technology is crucial for indoor localization systems due to its high accuracy and robustness in multipath environments. However, Non-Line-of-Sight (NLoS) conditions can cause UWB signal distortion, significantly reducing positioning accuracy. Thus, distinguishing between NLoS and LoS scenarios and mitigating positioning errors is crucial for enhancing UWB system performance. This research proposes a novel 1D-ConvLSTM-Attention network (1D-CLANet) for extracting UWB temporal channel impulse response (CIR) features and identifying NLoS scenarios. The model combines the convolutional neural network (CNN) and Long Short-Term memory (LSTM) architectures to extract temporal CIR features and introduces the Squeeze-and-Excitation (SE) attention mechanism to enhance critical features. Integrating SE attention with LSTM outputs boosts the model’s ability to differentiate between various NLoS categories. Experimental results show that the proposed 1D-CLANet with SE attention achieves superior performance in differentiating multiple NLoS scenarios with limited computational resources, attaining an accuracy of 95.58%. It outperforms other attention mechanisms and the version of 1D-CLANet without attention. Compared to advanced methods, the SE-enhanced 1D-CLANet significantly improves the ability to distinguish between LoS and similar NLoS scenarios, such as human obstructions, enhancing overall recognition accuracy in complex environments. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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16 pages, 4877 KiB  
Article
Channel Knowledge Map Construction Based on a UAV-Assisted Channel Measurement System
by Yanheng Qiu, Xiaomin Chen, Kai Mao, Xuchao Ye, Hanpeng Li, Farman Ali, Yang Huang and Qiuming Zhu
Drones 2024, 8(5), 191; https://doi.org/10.3390/drones8050191 - 11 May 2024
Cited by 4 | Viewed by 2537
Abstract
With the fast development of unmanned aerial vehicles (UAVs), reliable UAV communication is becoming increasingly vital. The channel knowledge map (CKM) is a crucial bridge connecting the environment and the propagation channel that may visually depict channel characteristics. This paper presents a comprehensive [...] Read more.
With the fast development of unmanned aerial vehicles (UAVs), reliable UAV communication is becoming increasingly vital. The channel knowledge map (CKM) is a crucial bridge connecting the environment and the propagation channel that may visually depict channel characteristics. This paper presents a comprehensive scheme based on a UAV-assisted channel measurement system for constructing the CKM in real-world scenarios. Firstly, a three-dimensional (3D) CKM construction scheme for real-world scenarios is provided, which involves channel knowledge extraction, mapping, and completion. Secondly, an algorithm of channel knowledge extraction and completion is proposed. The sparse channel knowledge is extracted based on the sliding correlation and constant false alarm rate (CFAR) approaches. The 3D Kriging interpolation is used to complete the sparse channel knowledge. Finally, a UAV-assisted channel measurement system is developed and CKM measurement campaigns are conducted in campus and farmland scenarios. The path loss (PL) and root mean square delay spread (RMS-DS) are measured at different heights to determine CKMs. The measured and analyzed results show that the proposed construction scheme can effectively and accurately construct the CKMs in real-world scenarios. Full article
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22 pages, 14988 KiB  
Article
Channel Estimation for Underwater Acoustic Communications in Impulsive Noise Environments: A Sparse, Robust, and Efficient Alternating Direction Method of Multipliers-Based Approach
by Tian Tian, Kunde Yang, Fei-Yun Wu and Ying Zhang
Remote Sens. 2024, 16(8), 1380; https://doi.org/10.3390/rs16081380 - 13 Apr 2024
Cited by 3 | Viewed by 2077
Abstract
Channel estimation in Underwater Acoustic Communication (UAC) faces significant challenges due to the non-Gaussian, impulsive noise in ocean environments and the inherent high dimensionality of the estimation task. This paper introduces a robust channel estimation algorithm by solving an [...] Read more.
Channel estimation in Underwater Acoustic Communication (UAC) faces significant challenges due to the non-Gaussian, impulsive noise in ocean environments and the inherent high dimensionality of the estimation task. This paper introduces a robust channel estimation algorithm by solving an l1l1 optimization problem via the Alternating Direction Method of Multipliers (ADMM), effectively exploiting channel sparsity and addressing impulsive noise outliers. A non-monotone backtracking line search strategy is also developed to improve the convergence behavior. The proposed algorithm is low in complexity and has robust performance. Simulation results show that it exhibits a small performance deterioration of less than 1 dB for Channel Impulse Response (CIR) estimation in impulsive noise environments, nearly matching its performance under Additive White Gaussian Noise (AWGN) conditions. For Delay-Doppler (DD) doubly spread channel estimation, it maintains Bit Error Rate (BER) performance comparable to using ground truth channel information in both AWGN and impulsive noise environments. At-sea experimental validations for channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems further underscore the fast convergence speed and high estimation accuracy of the proposed method. Full article
(This article belongs to the Special Issue Advancement in Undersea Remote Sensing II)
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15 pages, 3754 KiB  
Article
Noise-Canceling Channel Estimation Schemes Based on the CIR Length Estimation for IEEE 802.11p/OFDM Systems
by Kyunbyoung Ko and Hanho Wang
Electronics 2024, 13(6), 1110; https://doi.org/10.3390/electronics13061110 - 18 Mar 2024
Cited by 1 | Viewed by 1305
Abstract
This paper investigates methods for noise-canceling channel estimation (NC-CE) to track rapid time-varying channels in IEEE 802.11p/orthogonal frequency division multiplexing (OFDM) systems. To this end, we introduce a novel three-step channel estimation technique based on the estimated length of the channel impulse response [...] Read more.
This paper investigates methods for noise-canceling channel estimation (NC-CE) to track rapid time-varying channels in IEEE 802.11p/orthogonal frequency division multiplexing (OFDM) systems. To this end, we introduce a novel three-step channel estimation technique based on the estimated length of the channel impulse response (CIR). This approach aims to surpass the performance of conventional designs that rely on constructed data pilots (CDPs). In the first step, we not only eliminate noise components but also estimate the channel frequency responses (CFRs) of virtual subcarriers for long preamble parts. Moving on to the second step, we incorporate a modified CDP method without a frequency-domain reliability test and interpolation, taking into account the CFRs of virtual subcarriers obtained at the previous OFDM symbol time. The final step can be implemented as the operation of the inverse fast Fourier transform (IFFT)/nulling/FFT to reduce noise components from the CFRs obtained in the second step and generate CFRs for virtual subcarriers to be used in the next symbol time. The results of our simulations validate the effectiveness of our proposed channel estimation schemes. Full article
(This article belongs to the Special Issue Advances in Wireless and Optical Communication Systems)
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29 pages, 3153 KiB  
Article
Ultra-Wideband Ranging Error Mitigation with Novel Channel Impulse Response Feature Parameters and Two-Step Non-Line-of-Sight Identification
by Hongchao Yang, Yunjia Wang, Shenglei Xu, Jingxue Bi, Haonan Jia and Cheekiat Seow
Sensors 2024, 24(5), 1703; https://doi.org/10.3390/s24051703 - 6 Mar 2024
Cited by 4 | Viewed by 2929
Abstract
The effective identification and mitigation of non-line-of-sight (NLOS) ranging errors are essential for achieving high-precision positioning and navigation with ultra-wideband (UWB) technology in harsh indoor environments. In this paper, an efficient UWB ranging-error mitigation strategy that uses novel channel impulse response parameters based [...] Read more.
The effective identification and mitigation of non-line-of-sight (NLOS) ranging errors are essential for achieving high-precision positioning and navigation with ultra-wideband (UWB) technology in harsh indoor environments. In this paper, an efficient UWB ranging-error mitigation strategy that uses novel channel impulse response parameters based on the results of a two-step NLOS identification, composed of a decision tree and feedforward neural network, is proposed to realize indoor locations. NLOS ranging errors are classified into three types, and corresponding mitigation strategies and recall mechanisms are developed, which are also extended to partial line-of-sight (LOS) errors. Extensive experiments involving three obstacles (humans, walls, and glass) and two sites show an average NLOS identification accuracy of 95.05%, with LOS/NLOS recall rates of 95.72%/94.15%. The mitigated LOS errors are reduced by 50.4%, while the average improvement in the accuracy of the three types of NLOS ranging errors is 61.8%, reaching up to 76.84%. Overall, this method achieves a reduction in LOS and NLOS ranging errors of 25.19% and 69.85%, respectively, resulting in a 54.46% enhancement in positioning accuracy. This performance surpasses that of state-of-the-art techniques, such as the convolutional neural network (CNN), long short-term memory–extended Kalman filter (LSTM-EKF), least-squares–support vector machine (LS-SVM), and k-nearest neighbor (K-NN) algorithms. Full article
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17 pages, 1176 KiB  
Article
A Wideband Non-Stationary 3D GBSM for HAP-MIMO Communication Systems at Millimeter-Wave Bands
by Wancheng Zhang, Linhao Gu, Kaien Zhang, Yan Zhang, Saier Wang and Zijie Ji
Electronics 2024, 13(4), 678; https://doi.org/10.3390/electronics13040678 - 6 Feb 2024
Cited by 3 | Viewed by 1663
Abstract
High-altitude platforms (HAPs) are considered to be the most important equipment for next-generation wireless communication technologies. In this paper, we investigate the channel characteristics under the configurations of massive multiple-input multiple-output (MIMO) space and large bandwidth at millimeter-wave (mmWave) bands, along with the [...] Read more.
High-altitude platforms (HAPs) are considered to be the most important equipment for next-generation wireless communication technologies. In this paper, we investigate the channel characteristics under the configurations of massive multiple-input multiple-output (MIMO) space and large bandwidth at millimeter-wave (mmWave) bands, along with the moving essence of the HAP and ground terminals. A non-stationary three-dimensional (3D) geometry-based stochastic model (GBSM) is proposed for a HAP communication system. We use a cylinder-based geometric modeling method to construct the channel and derive the channel impulse response (CIR). Additionally, the birth–death process of the scatterers is enclosed using the Markov process. Large-scale parameters such as free space loss and rainfall attenuation are also taken into consideration. Due to the relative motion between HAP and ground terminals, the massive MIMO space, and the wide bandwidth in the mmWave band, the channel characteristics of HAP exhibit non-stationarities in time, space, and frequency domains. By deriving the temporal auto-correlation function (ACF), we explore the non-stationarity in the time domain and the impact of various parameters on the correlations across the HAP-MIMO channels. The spatial cross-correlation function (CCF) for massive MIMO scenarios, and the frequency correlation function (FCF) in the mmWave bands are also considered. Moreover, we conduct simulation research using MATLAB. Simulation results show that the theoretical results align well with the simulation results, and this highlights the fact that the constructed 3D GBSM can characterize the non-stationary characteristics of HAP-MIMO channels across the time, space, and frequency domains. Full article
(This article belongs to the Special Issue Feature Papers in Microwave and Wireless Communications Section)
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19 pages, 1410 KiB  
Article
Detecting Weak Underwater Targets Using Block Updating of Sparse and Structured Channel Impulse Responses
by Chaoran Yang, Qing Ling, Xueli Sheng, Mengfei Mu and Andreas Jakobsson
Remote Sens. 2024, 16(3), 476; https://doi.org/10.3390/rs16030476 - 26 Jan 2024
Cited by 1 | Viewed by 1628
Abstract
In this paper, we considered the real-time modeling of an underwater channel impulse response (CIR), exploiting the inherent structure and sparsity of such channels. Building on the recent development in the modeling of acoustic channels using a Kronecker structure, we approximated the CIR [...] Read more.
In this paper, we considered the real-time modeling of an underwater channel impulse response (CIR), exploiting the inherent structure and sparsity of such channels. Building on the recent development in the modeling of acoustic channels using a Kronecker structure, we approximated the CIR using a structured and sparse model, allowing for a computationally efficient sparse block-updating algorithm, which can track the time-varying CIR even in low signal-to-noise ratio (SNR) scenarios. The algorithm employs a conjugate gradient formulation, which enables a gradual refinement if the SNR is sufficiently high to allow for this. This was performed by gradually relaxing the assumed Kronecker structure, as well as the sparsity assumptions, if possible. The estimated CIR was further used to form a residual signal containing (primarily) information of the time-varying signal responses, thereby allowing for the detection of weak target signals. The proposed method was evaluated using both simulated and measured underwater signals, clearly illustrating the better performance of the proposed method. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)
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