NLOS Environments Research Articles

Maximizing the IRS’s Gains in NLOS Communication through Optimal Positioning

Background: Recently, an IRS has been proposed as a low-cost and effective solution for performance improvement in wireless communication. However, the configuration and setup necessary to derive optimal results have not been identified yet. Objective: The paper seeks to build the knowledge by investigating the effects of IRS in the LOS and NLOS environment to verify the achievable gains from each and examine how different IRS positions impact the BER. Methods: An IRS model was simulated in MATLAB to investigate the channel capacity gain of an IRS in LOS and NLOS scenarios for the verification of performance gains. Further, the effects of the IRS positioning (with respect to transmitter and end-user) on the BER were investigated. Results: The channel capacity of an IRS system was found to be lower by 12Mbps compared to that of a direct link in a LOS scenario. This demonstrates that a passive IRS is only beneficial in the absence of a line of sight. In the case of a NLOS, for an SNR of 50, MIMO transmitter diversity and receiver diversity had channel capacities of 1.0 Mbps and 4.5 Mbps, respectively while the IRS system achieved 22 Mbps. As such, it is more beneficial to create an alternative path via the IRS than to increase the number of transmit or receive antennas. The positioning of the IRS had a significant impact on the BER. From the results, positioning the IRS mid-way between the AP and STA led to up to 57.69% improvements in BER compared to close to STA or close to AP arrangements. Conclusion: The results prove that an IRS is a cost-effective, low-power consumption solution for overcoming challenges associated with NLOS communication. The work also identified the ideal positioning of the IRS to be the midpoint between AP and STA, for the WLAN system. Further research will be needed to verify performance in other systems.

Multiple-Wave Source Localization Using UAVs in NLOS Environments

Multiple-Wave Source Localization Using UAVs in NLOS Environments

Consistent and accurate TDOA localization in dynamic NLOS environments via prior information independent estimation

Consistent and accurate TDOA localization in dynamic NLOS environments via prior information independent estimation

Fuzz C-Means Clustering Algorithm for Hybrid TOA and AOA Localization in NLOS Environments

Fuzz C-Means Clustering Algorithm for Hybrid TOA and AOA Localization in NLOS Environments

Improving the Localization Accuracy and Robustness of a UWB System Using VB-CSRUKF and RTS in Harsh Underground NLOS Environments

Improving the Localization Accuracy and Robustness of a UWB System Using VB-CSRUKF and RTS in Harsh Underground NLOS Environments

WiFi-Aided Ultra Wideband Localization in Indoor NLoS Environment

WiFi-Aided Ultra Wideband Localization in Indoor NLoS Environment

Received Signal Strength Characterization in Mixed LOS and NLOS Environments

Received Signal Strength Characterization in Mixed LOS and NLOS Environments

Passive IoT Localization Technology Based on SD-PDOA in NLOS and Multi-Path Environments

Passive IoT Localization Technology Based on SD-PDOA in NLOS and Multi-Path Environments

Single UAV-Based Wave Source Localization in NLOS Environments

Single UAV-Based Wave Source Localization in NLOS Environments

ACGA: Adaptive Conjugate Gradient Algorithm for non-line-of-sight hybrid TDOA-AOA localization

This study proposes a novel hybrid localization method that combines the time difference of arrival (TDOA) and angle of arrival (AOA) measurements to achieve improved accuracy with two base stations. Unlike other subspace-based methods that require calculating the eigenbasis, our proposed method constructs a Krylov subspace basis using the residual vectors of the Conjugate Gradient (CG) algorithm, which reduces computational complexity. In our approach, the measurements obtained from AOA estimation serve as initial estimates in our Adaptive Conjugate Gradient Algorithm (ACGA). ACGA incorporates an innovative adaptation mechanism that utilizes the residual error for updating and refining the estimated target position. This adaptation strategy allows our algorithm to iteratively improve localization accuracy, especially in the presence of NLOS errors. To assess the performance of our method, we conduct exhaustive simulations, considering various NLOS error scenarios and comparing them against conventional methods and extensively developed algorithms. The results demonstrate that our proposed method surpasses existing techniques, achieving a more densely spaced estimated position of the target in noisy environments with only two base stations. Furthermore, our method approaches the Cramer Rao Lower Bound (CRLB), indicating its high accuracy and efficiency in estimating the target position in typical NLOS environments. The effectiveness and potential of the proposed hybrid TDOA-AOA localization method are evident from the superior performance demonstrated in our simulations. These findings highlight the practical significance of our approach for real-world applications where accurate target localization is crucial, such as wireless communication, radar systems, and autonomous navigation.

LOS와 N-LOS 환경에서 측량장비를 활용한 UWB 실내 위치 검증 및 정확도 분석

LOS와 N-LOS 환경에서 측량장비를 활용한 UWB 실내 위치 검증 및 정확도 분석

Time Delay Characteristics Analysis of UWB Diffraction Propagation in Indoor NLOS Environment

Time Delay Characteristics Analysis of UWB Diffraction Propagation in Indoor NLOS Environment

A Transformer-Based Signal Denoising Network for AoA Estimation in NLoS Environments

The next-generation communications impose requirements on integrated sensing and communication. However, the non-line-of-sight propagation in indoor complex environments poses great challenges to common localization techniques. In this letter, we propose a signal denoising network based on the transformer and temporal attention to improve the angle-of-arrival estimation accuracy. In the proposed network, the channel impulse response is denoised and reconstructed to mitigate errors. Then, two database are constructed based on self-built ultra-wideband transceivers in indoor environments for validation. Results show that the proposed network outperforms other machine learning methods in terms of angle-of-arrival estimation accuracy.

Geomagnetic Positioning-Aided Wi-Fi FTM Localization Algorithm for NLOS Environments

WiFi-based indoor localization using the fine time measurement (FTM) protocol has become a popular technique. However, in harsh Non-Line-of-Sight (NLoS) environments, WiFi FTM positioning (WFP) suffers from poor performance. In this letter, a novel WiFi FTM localization method with the assistance of geomagnetic positioning (GP) is proposed. To ensure the accuracy of GP, an enhanced mind evolutionary algorithm (EMEA) is designed. A fine-grained WiFi position estimation method using the overlapping searching area (OSA) and the coincident points selection strategy is proposed. Experimental results show that the EMEA-based GP improves the localization performance of WFP in NLOS environments, the mean localization error (ME) and root-mean-square error (RMSE) of the GP-aided WFP are 1.82 m and 2.08 m, respectively. Compared with the classic WFP using the weighted least square (WLS) method, the ME and RMSE are reduced by 51.7% and 52.4%, respectively.

A Novel NLOS Suppression Algorithm for Indoor Location based on FCM and REKF

Abstarct. The indoor positioning based on wireless sensor networks (WSN) has become one of the research hotpots. However, the NLOS propagation of the distance signals greatly challenges the accuracy and robustness of the algorithm. In this paper, we take the suppression of NLOS as the core goal and proposed the FCM-REKF-based positioning method. We firstly identify the signal states through the fuzzy c-means clustering (FCM), for the measurement distance judged to be NLOS, a refactoring method based on FCM is used. Then the corrected distance is smoothed by Kalman filter, and the Robust Extended Kalman Filter is used to calculate the final position. The simulation results show that our method has higher accuracy than EKF, REKF and IMM-EKF under NLOS environment.

Harris hawks optimization algorithm and BP neural network for ultra-wideband indoor positioning.

Traditional back propagation neural networks (BPNNs) for ultrawideband (UWB) indoor localization can effectively improve localization accuracy, although there is high likelihood of becoming trapped in nearby minima. To solve this problem, the random weights and thresholds of the BPNN are optimized using the Harris Hawks optimization algorithm (HHO) to obtain the optimal global solution to enhance the UWB indoor positioning accuracy and NLOS resistance. The results show that the predicted trajectory of the HHO and BPNN hybrid algorithm (HHO-BP) matches the actual position in the two-dimensional localization scenario with four base stations; the optimized average positioning error is effectively reduced in both indoor LOS and NLOS environments. In the LOS environment, the total mean error of the traditional BPNN algorithm is 6.52 cm, which is 26.99% better than the UWB measurement error; in the NLOS environment, the total mean error of the conventional BPNN is 14.82 cm, which is 50.08% better than the UWB measurement error. The HHO-BP algorithm is further optimized on this basis, and the total mean error in the LOS environment is 4.50 cm, which is 22.57% better than the conventional BPNN algorithm; in the NLOS environment, the total mean error is 9.56 cm, which is 17.54% better than the conventional BPNN algorithm. The experimental findings suggest that the approach has greater calibration accuracy and stability than BPNN, making it a viable choice for scenarios requiring high positional precision.

Research on Indoor UWB Positioning Based on Expectation Maximization in NLOS Environment

SummaryUltra‐wide band (UWB) technology is especially suitable for indoor positioning because of its strong anti‐interference ability, it can achieve centimeter‐level positioning accuracy in the line of sight (LOS) environment, but it is challenging to construct a robust and high‐precision algorithm in indoor complex and changeable environments. Therefore, it establishes an improved standard Kalman filter (SKF) based on expectation maximization (SKF‐EM), which models the prediction error covariance and measurement noise using the maximum likelihood criterion, estimates the filter parameters online through the expectation maximum theory, and updates the Kalman filter gain according to the Kalman filter based on dropped measurements method (SKF‐DMM). Experimental results show that: in case of serious occlusion, the maximum positioning error of SKF‐EM algorithm is 1.03 m, 39% lower than that of SKF and 60.2% lower than that of UWB.

Millimeter Wave and Sub-Terahertz Spatial Statistical Channel Model for an Indoor Office Building

Millimeter-wave (mmWave) and sub-Terahertz (THz) frequencies are expected to play a vital role in 6G wireless systems and beyond due to the vast available bandwidth of many tens of GHz. This paper presents an indoor 3-D spatial statistical channel model for mmWave and sub-THz frequencies based on extensive radio propagation measurements at 28 and 140 GHz conducted in an indoor office environment from 2014 to 2020. Omnidirectional and directional path loss models and channel statistics such as the number of time clusters, cluster delays, and cluster powers were derived from over 15,000 measured power delay profiles. The resulting channel statistics show that the number of time clusters follows a Poisson distribution and the number of subpaths within each cluster follows a composite exponential distribution for both LOS and NLOS environments at 28 and 140 GHz. This paper proposes a unified indoor statistical channel model for mmWave and sub-Terahertz frequencies following the mathematical framework of the previous outdoor NYUSIM channel models. A corresponding indoor channel simulator is developed, which can recreate 3-D omnidirectional, directional, and multiple input multiple output (MIMO) channels for arbitrary mmWave and sub-THz carrier frequency up to 150 GHz, signal bandwidth, and antenna beamwidth. The presented statistical channel model and simulator will guide future air-interface, beamforming, and transceiver designs for 6G and beyond.

Reliable and Practical Bluetooth Backscatter With Commodity Devices

Recently backscatter communication with commodity radios has received significant attention since specialized hardware is no longer needed. The state-of-the-art BLE backscatter system, FreeRider, realizes ultra-low-power BLE backscatter communication entirely using commodity devices. It, however, suffers from several key reliability issues, including unreliable two-step modulation, productive-data dependency, and lack of interference countermeasures. To address these problems, we propose RBLE, a robust BLE backscatter system that works with an excitation BLE device and a single BLE receiver. First, it uses BLE signals with partial single tones as excitations, making single-bit modulation much more robust. Then it designs dynamic channel configuration that enables channel hopping to avoid interfered channels. Moreover, it presents BLE packet regeneration that uses adaptive encoding to further enhance reliability for various channel conditions. The prototype is implemented using TI BLE radios, iPhones, Android phones, and customized tags with FPGAs. Empirical results demonstrate that RBLE achieves more than 17x uplink goodput gains over FreeRider under indoor LoS, NLoS, and outdoor environments. We also show that RBLE can realize uplink ranges of up to 25 m for indoors and 56 m for outdoors.

Robust RSS-Based Source Localization With Unknown Model Parameters in Mixed LOS/NLOS Environments

In this paper, we address the received-signal-strength (RSS) based source localization problem in mixed line-of-sight/non-line-of-sight (LOS/NLOS) environments, where the model parameters are unknown. To accommodate the additional path losses incurred by NLOS signal propagations, we introduce a random variable to the outdoor RSS measurement model to represent the additional path loss. Based on this modified model, we propose a robust localization method for the case of unknown model parameters. Specifically, we introduce a balancing parameter and express the additional path loss term as the sum of the balancing parameter and an error term, and then formulate a robust weighted least squares (RWLS) problem to jointly estimate the source location, the unknown model parameters, and the balancing parameter. The RWLS problem is solved in an iterative manner, where the S-Lemma and the semidefinite relaxation technique are used. Both simulated and real experimental data verify that the proposed method works well in both dense and sparse NLOS environments.