LOS Scenarios Research Articles

A more detailed mathematical model for analyzing the link budget in UWOCs for both LoS and N-LoS scenarios

A more detailed mathematical model for analyzing the link budget in UWOCs for both LoS and N-LoS scenarios

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.

1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System

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.

Carbon emission scenario simulation and policy regulation in resource-based provinces based on system dynamics modeling

In China, numerous cities are resource-based, with substantial energy consumption, emissions, and pollution and they expand quickly and economically, notably enhancing their carbon emissions. Nevertheless, they have considerable potential for emission reduction. Assuming the “dual-carbon” goal as a backdrop, this study considered Shanxi Province, the largest coal resource-based province in China, for a case in point. It established a carbon emission system dynamics model, constructs five carbon emission systems, namely, economy, energy, population, land, and environment, and sets up four scenarios. Finally, in light of the scenario simulation's outcomes, we explored the optimal path and policy regulations for resource-based cities to reach carbon peaks. The study conclusions show the following: (1) All factors are exhibit correlated with each other, and their influence in the four scenarios is ranked as follows: GDP, energy consumption, industrial structure, total population, land-use structure. (2) Although GDP is a key factor influencing total carbon emissions, regulating only a single factor cannot achieve the carbon emission target. Thus, all factors need to be considered and synergistically regulated to achieve optimal carbon benefits. (3) Carbon emissions are higher and grow faster in the Baseline Development Scenario and the Fast Development Scenario, particularly in the FDS scenario, where they reach 614.48 million tons. Both scenarios exceed the peak carbon target by 4.4% and 7.1%. (4) In the Low-Carbon Optimization model and Resource Saving Scenario have low and slow-growing carbon emissions. Although the RSS has lower carbon emissions of 539.83 million tons, sacrificing sustainable development to reduce these emissions is unrealistic. In comparison, the LOS scenario represents the optimal path for achieving sustainable growth and lowering carbon emissions in Shanxi Province, with emissions totaling 550.99 million tons. This study implements strategies to manage the pace of population expansion, optimize the industrial structure, modify the energy structure, and optimize the allocation of land resources. The results of the study not only do our findings offer data reinforcement and implementation strategies for the low-carbon conversion of similar resource-based cities in China, but also offer case studies for different kinds of resource-based cities that fulfill the “carbon peak” objective.

A Handwriting Recognition System With WiFi

Handwriting recognition systems are a convenient and alternative way of writing in the air with fingers rather than typing on keyboards. However, existing recognition systems are limited by their low accuracy and the requirement to wear dedicated devices. To address these issues, we propose WiWrite, an accurate contactless handwriting recognition system that allows users to write in the air without wearing any wearable devices. Specifically, we employ a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSI division scheme</i> to process the noisy raw WiFi channel state information (CSI), which stabilizes the CSI phase and reduces noise in CSI amplitude. To automatically retain low noise data for identification in the LOS scenario, we propose a self-paced dense convolutional network (SPDCN), which is a self-paced loss function based on a modified convolutional neural network coupled with a dense convolutional network. Furthermore, to achieve accurate handwriting recognition in the NLOS scenario, we combine ADOA and PCA algorithms to remove location-induced interference and extract action features. Comprehensive experiments show the merits of WiWrite, revealing that the recognition accuracy for the same-size input and different-size input are 93.6% and 89.0%, respectively. Moreover, WiWrite can achieve accurate recognition regardless of environment and target diversity in LOS and NLOS scenarios.

Ship-to-ship maritime wireless channel modeling under various sea state conditions based on REL model

The propagation of electromagnetic waves on land and sea is significantly different. Although the Los scenario is significant in marine wireless communication, the marine wireless channel exists an obvious two-ray phenomenon due to the strong reflection path reflected through the sea surface. By modeling the measured data of marine wireless channels, this paper calculates the radio propagation characteristics of the Pearl River estuary. In addition, the wave fluctuations and high humidity environment will also impact the properties of the marine wireless channel. Therefore, sea surface morphology models under multiple wind speeds are built. To estimate the path loss in the same area under different conditions, the Monte Carlo method is employed to quantify the results. The simulation results show that the electric wave propagation gradually degenerated from the round earth loss (REL) model to the free space model with increasing wind speed. Moreover, the distribution of the shadow fading varies with distance. The findings provide references for the network planning of marine communication.

Multiuser Wireless Hand Gesture Recognition by Spatial Beamforming

Hand gesture recognition through wireless sensing is a new method of human-machine interaction, and is an important research direction for next generation integrated sensing and communication systems. Recently, although there are plenty of works on wireless sensing and hand gesture recognition, most of them are applied in single user scenario. Since the recognition algorithm is mainly realized by analyzing the variation of wireless propagation channel, the simultaneous movement of multiple people will cause superposition of the dynamic channel responses and cause interference to each other. This article proposes to use spatial beamforming to alleviate the mutual influence of multiuser hand gestures. Since the hands of multiple users reflect the same signal, the channel responses are usually not orthogonal even if the users are well separated in different locations, and there is no reference signals to help distinguishing the channel response reflected by different users. We propose a preamble gesture scheme to estimate the spatial channel of dynamic reflections under the impact of strong phase noise, and use the Doppler variation feature to verify the channel response belongs to hand movement. We have a thorough analysis of the inter-user interference impacted by the transmitter-user-receiver geometry relations, user movement speeds and directions. The interference suppression effect is demonstrated by prototype experiments under real LOS and NLOS scenarios exploiting LTE signals.

Derivation ofK-Factor Detection Statistics to Discriminate Between LOS and NLOS Scenarios

The Rician <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -factor has been extensively used to characterize indoor and outdoor wireless propagation channels based on experimental and analytical models. In this paper, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -factor statistics are derived analytically and experimentally validated for LOS and NLOS scenarios. Receiver operating characteristics (ROC) are formulated based on the derived detection statistics such that an optimal decision threshold can be selected to better enhance LOS/NLOS detection accuracy compared to usual methods. The proposed approach is shown to have a greater LOS/NLOS discrimination accuracy than other state-of-the-art approaches. Furthermore, there is evidence that a second metric can be obtained which characterizes the current environment and provides a confidence value, which can be used to inform further decisions.

Placement Matters

WiFi-based contactless sensing has found numerous applications in the fields of smart home and health care owning to its low-cost, non-intrusive and privacy-preserving characteristics. While promising in many aspects, the limited sensing range and interference issues still exist, hindering the adoption of WiFi sensing in real world. In this paper, inspired by the SNR (signal-to-noise ratio) metric in communication theory, we propose a new metric named SSNR (sensing-signal-to-noise-ratio) to quantify the sensing capability of WiFi systems. We theoretically model the effect of transmitter-receiver distance on sensing coverage. We show that in LoS scenario, the sensing coverage area increases first from a small oval to a maximal one and then decreases. When the transmitter-receiver distance further increases, the coverage area is separated into two ovals located around the two transceivers respectively. We demonstrate that, instead of applying complex signal processing scheme or advanced hardware, by just properly placing the transmitter and receiver, the two well-known issues in WiFi sensing (i.e., small range and severe interference) can be greatly mitigated. Specifically, by properly placing the transmitter and receiver, the coverage of human walking sensing can be expanded by around 200%. By increasing the transmitter-receiver distance, a target's fine-grained respiration can still be accurately sensed with one interferer sitting just 0.5 m away.

Experimental Investigation of Body-Centric Indoor Localization Using Compact Wearable Antennas and Machine Learning Algorithms

A simple and effective body-centric localization algorithm has been proposed in this article using ultra-wideband (UWB) wearable technology. The algorithm has been validated through measurement campaigns with a human subject volunteer in an indoor environment. The algorithm takes into account statistical channel parameter analysis, machine learning (ML) algorithms, and time-of-arrival (TOA)-based range estimation/data fusion techniques. Two channel parameters namely path loss magnitude and rms delay spread are proposed as classification features to be applied to the ML algorithm to accurately classify the off-body channel links into LOS, PNLOS, and NLOS scenarios. Multiclass support vector machine (MC-SVM) classifier along with SMOTE algorithm to take into account class imbalance is applied with the channel classification accuracy of 98.63%. Threshold-based range estimation algorithms are applied in order to mitigate NLOS scenarios caused mainly due to the presence of the human subject. Results report human localization accuracy in 0.5–3 cm range using TDOA data fusion technique for target estimation. Further validation is presented considering wide range of Tx–Rx distance, presence of another obstruction between the Tx and Rx links, and performance in different environment which shows the suitability of the proposed methodology.

Assessment of 5G as an ambient signal for outdoor backscattering communications

The aim of this article is to evaluate the applicability of 5G technology as a possible ambient signal for backscattering communications (AmBC). This evaluation considers both urban macro-cellular, small cell as well as rural highway environments. The simulations are performed in outdoor areas including analysis about 5G implementation strategies in different scenarios. Essential aspects of 5G radio network topology such as frequency domain (3.5 GHz and 26 GHz) and antenna locations (offering line-of-sight, LOS) are highlighted and turned to applicability scenarios with AmBC. The LOS scenarios are evaluated to determine the widest applicability area of 5G for AmBC. Typical AmBC applications are studied including collection of data from several sensors to receivers. Evaluation of the applicability of 5G was based on propagation related simulations and calculations utilising the ray tracing technique and the radar equation. The results demonstrate that 5G can be used as an ambient signal for backscattering communications for short ranges for typical sensor sizes. It is also observed that the range of communication is heavily dependent on the the size of the sensor.

X-law detection scheme for monobit transmitted-reference ultra wideband receivers

&lt;p&gt;In ultra-wideband (UWB) communications, monobit receivers offer a low complexity implementation but at the same time exhibit a great performance loss. In this paper, a novel detection scheme, denoted as &lt;em&gt;x&lt;/em&gt;-law detection (XLD), is proposed to diminish the performance loss caused by employing monobit analog-to-digital converters in transmitted-reference (TR) UWB receivers. Simulation results show that if the optimal value is employed for &lt;em&gt;x&lt;/em&gt;, the XLD-based monobit weighted TR (MWTR) receiver can achieve 14.2~15.5 dB and 8~9.2 dB performance gain over the conventional MWTR receiver in LOS and NLOS scenarios, respectively. Moreover, the XLD-based MWTR receiver performance with the optimal value of &lt;em&gt;x&lt;/em&gt; is only 1.6~3 dB away from the optimum MWTR receiver performance in intra-vehicle UWB channels. Additionally, the XLD-based MWTR receiver is not sensitive to the summation interval. This feature decreases the receiver complexity and guarantees a robust performance over different multipath channels. The significant performance improvement of the XLD scheme comes at a limited complexity increase. Thus, the XLD approach is a good candidate for TR-based and other training-based monobit receivers requiring low complexity, high performance, and low power consumption.&lt;/p&gt;

Upper Bounds for the Capacity for Severely Fading MIMO Channels under a Scale Mixture Assumption.

A cornerstone in the modeling of wireless communication is MIMO systems, where a complex matrix variate normal assumption is often made for the underlying distribution of the propagation matrix. A popular measure of information, namely capacity, is often investigated for the performance of MIMO designs. This paper derives upper bounds for this measure of information for the case of two transmitting antennae and an arbitrary number of receiving antennae when the propagation matrix is assumed to follow a scale mixture of complex matrix variate normal distribution. Furthermore, noncentrality is assumed to account for LOS scenarios within the MIMO environment. The insight of this paper illustrates the theoretical form of capacity under these key assumptions and paves the way for considerations of alternative distributional choices for the channel propagation matrix in potential cases of severe fading, when the assumption of normality may not be realistic.

Improving the optical link for UVLC using MIMO technique

Abstract This paper proposed a theoretical treatment to study underwater wireless optical communications (UWOC) system with different modulation schemes by multiple input-multiple output (MIMO) technology in coastal water. MIMO technology provides high-speed data rates with longer distance link. This technique employed to assess the system by BER, Q. factor and data rate under coastal water types. The reliability of the system is examined by the techniques of 1Tx/1Rx, 2Tx/2Rx, 3Tx/3Rx and 4Tx/4Rx. The results shows the proposed technique by MIMO can get the better performance compared with the other techniques in terms of BER. Theoretical results were obtained to compare between PIN and APD photodetectors. Besides, 32-PPM is robust and considers a suitable modulation scheme for obtaining the low BER and high Q. factor in LoS scenarios.

Clustering-Based Nonstationary Massive MIMO Channel Modeling at 1.4725 GHz

The paper reports on the radio propagation characteristics of Massive MIMO. The realistic measurements are conducted in typical outdoor LOS and NLOS scenarios with the bandwidth of 100 MHz at the carrier frequency of 1.4725 GHz. In this paper the channel propagation in spectrum and space domains are investigated by employing the high-precision parameter estimation algorithm. Based on big data technology, we propose the multipath clustering algorithm and subinterval programming to bring deeper insight into the cluster evolution over the antenna array axis. The works focus on the correlation, and the result is in accordance with the theory of the cluster’s visible region. Furthermore, a non-WSSUS (non-wide sense stationary uncorrelated scattering) channel analytical model is established. The whole research work aims to contribute the radio channel modeling of the 5G Massive MIMO communication system.

Performance of the non-iterative ToA-based positioning algorithms in complex indoor environments

The Gauss-Newton algorithm has been widely used in GNSS positioning and indoor positioning applications, but it requires an initial guess. An unfavorable initial guess may cause the iteration divergence. This issue is particularly important in indoor positioning scenarios but has not attracted enough attention. The non-iterative closed-form algorithms are suitable to provide the initial guess since they are divergence-free but sub-optimal. This study systematically evaluated performance of four representative non-iterative closed-form algorithms, namely the differenced squared range (DSR) algorithm, the Bancroft algorithm, and constrained virtual parameter (CVP) algorithm in terms of positioning precision, robustness, and computation efficiency. The performance of these algorithms is evaluated with both simulative data and real data. The results indicate that the CVP algorithm achieves 0.2–0.6 m accuracy in the static scenarios and about 2 m accuracy in the kinematic scenario, which outperforms the Gauss-Newton algorithm and the other two non-iterative approaches. The Gauss-Newton approach also achieves promising results in the LOS scenario, but it is more vulnerable to the NLOS observations. The DSR algorithm and the Bancroft algorithms are sub-optimal, robust to NLOS biases, but their performance in real data test depends on the test scenario. The CVP algorithm is free of divergence issue, robust to NLOS bias, and computationally efficient and achieves the best positioning accuracy in all four algorithms, thus should be recommended in highly non-linear localization estimation scenarios, such as the indoor positioning applications.

Millimetre wave channel modeling based on grey genetic optimization model

In this paper, grey genetic optimization model (GGOM) is proposed for predicting insufficient channel parameters without increasing the amount of measurement data. Based on the millimetre wave 28 GHz indoor measurement data for both LOS and NLOS scenarios, the GGOM model is compared with traditional back propagation (BP) and grey model (GM) to analyse channel parameters like delay spread, excess delay and azimuth spread. Results show that the fitness of GGOM is better than the grey model in improving the stability of system. It works well with insufficient data (size less than 30) in most cases as it is set regardless of the specific scene and measurement data. This is verified by QuaDRiGa platform by generating uniformly distributed and interpolated data between the experimental measurement data. GGOM fits best with the measurement data compared with other prediction methods in channel characterization. Moreover, the mean absolute percentage error (MAPE) for GGOM is the least compared with GM and BP methods. The proposed GGOM model has good performance in modeling insufficient data of propagation channel, practically.

Evaluation of dynamic Localization System Based on UWB and Wi-Fi for Indoor Environments

Due to limitations and complexity in indoor communications, many wireless technologies deployed to correspond with those challenges. The Ultra-Wideband (UWB) wireless communication for indoor environments is one of the widespread bands developed for convey high data rate. The other widespread band is 2.4GHz based on Wi-Fi technology. Each of the mentioned bands could be convenient for specific areas and not for others based on long or short-range communications. In this paper, both bands are applied for indoor environments to find the target’s locations. A new technique based on Multi-Scale Bands with Dynamic approach (MSBD) is applied to implement an optimum system. The three dimensional (3D) case study building designed based on Wireless Insite Package with real building geometric measurements. It is worth to mention that both bands are applied based on dynamic parameters to assign specific parameters for specific environments. The average error in the whole building based on static parameters using UWB is (1.023m) while (0.452m) based on individual 2.4GHZ. The obtained results confirm that the proposed MSBD technique is significantly outperformed individual band with static parameters. The enhancement is noted from error range which improved from (1.023m) to (0.08m) for UWB and (0.452m) to (0.15m) for 2.4GHz. For optimum system, the UWB was deployed for a distance under (10m) and Wi-Fi for other distance. By deploying both technologies and using the dynamic system, the performance of the proposed system worked with accurate measurements in short and long-distance in addition to both LOS and NLOS scenarios identify relevant articles in literature searches, great care should be taken in constructing both.

Investigation of multiple input–single output technique for wireless optical communication system under coastal water

In this paper, a simulation was proposed to study underwater visible light communication (UVLC) system using a multiple input–single output (MISO) technique with different modulation schemes under coastal water. MISO technique has enhanced the distance link with high speed data rate compared with the traditional technique of single input–single output (SISO), MISO technique was also employed to examine the performance of BER and data rate under different Jerlov types of coastal water. Various impairments such as distance link, pathloss, channel loss and the attenuation of coastal water were introduced into the transmission of the optical signal to assess the reliability of the MISO technique. The simulation analysis shows the proposed technique can outperform the SISO technique in terms of BER and Q. factor. Moreover, the Q. factor of up to 16 dB and 10−10 BER under J9C of coastal water by using (4Tx/1Rx) technique. The radiation angle of LED and the attenuation coefficient of coastal water were restricted for achieving longer distances link. The system accomplished a distance link about 2.9 and 13.5 m for coastal water types J9C and JIC respectively. The 32-PPM is a suitable modulation for obtaining a best distance link for LoS and NLoS scenarios.

SEARE: A System for Exercise Activity Recognition and Quality Evaluation Based on Green Sensing

Green-computing technology and energy-saving design have become the focus of research in various fields in recent years. As a ubiquitously deployed infrastructure, WiFi can be considered as a platform for green sensing, and a plethora of efforts have been made in WiFi-based passive detection recently. However, little work has been done on the exercise activity recognition. In this paper, we propose SEARE, a novel energy-efficient solution using WiFi for exercise activity recognition. It is prototyped by fine-grained CSI extracted from existing commercial WiFi devices. Different from traditional features like mean or max value exploited in previous activity recognition works, involving either time or frequency information, we select CSI-waveform shape as activity feature, which contains the information from both of these two domains. A series of de-noise methods are designed, including low-pass, PCA, and median filtering, where PCA can remove the in-band noise that traditional low-pass filters fail to do. Finally the evaluation of activities quality can be made. Extensive experimental result validates the great performance of SEARE in both LOS and NLOS scenarios, with average recognition accuracies of 97.8 and 91.2 percent respectively.