Line Of Sight Environment Research Articles

Experimental analysis of low-duty cycle campus deployed IoT network using LoRa technology

This study emphases on the implementation and evaluation of a LoRa network in indoor environments through an experimental setup at Amity University, Greater Noida (AUGN) campus. Aimed at scrutinizing the link-level performance and reliability of LoRa technology, extensive measurements have been conducted with prototype device integrating the SX1278 LoRa radio module at 433 MHz. The performance has been analysed in terms of Signal-to-Noise Ratio (SNR), Received Signal Strength Indicator (RSSI), and Packet Reception Rate (PRR) across various line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios. The results reveal that network performance is highly sensitive to parameter configurations, such as spreading factor (SF) and coding rate (CR). In LoS environments, SF = 9 and CR = 4/5 provided an RSSI of −89.12 dBm and an SNR of 9.91 dB, while highly obstructive areas saw optimal performance with SF = 7 and CR = 4/7. The study also identified configurations ensuring 100 % packet delivery accuracy and maximum throughput. This work stands out due to its thorough examination of LoRa for indoor IoT applications, notably addressing duty cycle restrictions, a topic often overlooked in existing literature. The valuable insights presented are crucial for maximizing the potential of LoRa in indoor environments with high population density. The findings strongly advocate for the use of LoRa for real-time environmental monitoring and innovative IoT applications within university campuses.

LOS compensation and trusted NLOS recognition assisted WiFi RTT indoor positioning algorithm

In previous studies, ranging errors in the line of sight (LOS) environment were often overlooked, resulting in an increase in positioning errors. In non-line of sight (NLOS) conditions, the measuring distance hardly participates in the location calculation, leading to the loss of useful positioning information. Thus, this paper proposes a WiFi RTT positioning approach based on LOS compensation and trusted NLOS recognition, which improves the performance of RTT positioning by using the compensated LOS distances and trusted NLOS ranges to estimate location. In the proposed approach, NLOS and LOS identification need to be implemented first. A support vector machine algorithm is used to construct a real-time model that recognizes NLOS and LOS distances based on the proposed classification features. Then, the distances under NLOS and LOS environments are evaluated and compensated, respectively, to obtain reliable NLOS ranges and calibrated LOS measurements. A least squares algorithm is utilized to build the LOS distance compensation model, and Bayesian theorem is applied to recognize the trusted NLOS ranges. The compensated LOS distances and credible NLOS ranges are used for estimating the location. The experimental results demonstrate that the proposed algorithm achieves excellent positioning accuracy, with a mean absolute error of 1.082 m. This signifies a substantial enhancement of 53.34% when compared to the least squares algorithm, a remarkable 60.24% improvement over the weighted centroid positioning algorithm, a significant 36.11% progress in comparison to the RTNLIA algorithm, and a noteworthy 32.82% boost relative to the NLRNB approach.

A Hybrid Differential Detection Scheme for the Ultra-Wideband Orientational Beamforming System

Differential space-time coding methods have been investigated for ultra-wideband communication systems to avoid channel estimation. However, their performance in the line-of-sight (LOS) environment is worse than the recently proposed orientational beamforming (OBF) system under low signal-to-noise ratios (SNRs). On the other hand, the OBF system cannot work well in the non-LOS (NLOS) environment. To address these issues, a hybrid differential detection (HDD) scheme is proposed in this paper, which combines the OBF system with a proposed differential OBF (DOBF) system. First, the DOBF system with a fully differential detection scheme is proposed, whose performance is almost the same as the differential space-time block coding method. However, its detection complexity only linearly increases with the number of transmitting antennas. Then, the HDD scheme is proposed, with its decision statistic being a combination of a modified OBF decision statistic and the DOBF decision statistic. In the NLOS environment, the HDD scheme is reduced to the DOBF system. In the LOS environment, a combination coefficient is obtained by mapping an SNR-related factor through a sigmoid function. The optimal parameters for the sigmoid function are determined through simulations, with which the proposed HDD scheme can achieve overall better performance than the OBF and DOBF systems.

An Adaptive IMU/UWB Fusion Method for NLOS Indoor Positioning and Navigation

Indoor positioning system (IPS) plays an important role in the applications of Internet of Things (IoT), including intelligent hospital, logistics, and warehousing. Ultra-wideband (UWB) based IPS has shown superior performance due to its strong multipath resistance and high temporal resolution. However, the non-line-of-sight (NLOS) situations noticeably degrade both the positioning accuracy and the communication reliability. To address this issue, we first propose a support vector machine (SVM) based channel detection method to distinguish the line-of-sight (LOS) and NLOS conditions. Then, one base station (BS) based distance and angle positioning algorithm with extended Kalman filter (DAPA-EKF) in NLOS environment is proposed. For the LOS environment, least squares (LSs) with EKF processing of acceleration (LS-AEKF) and velocity (LS-VEKF) are developed. To further improve the performance, the combination of time difference of arrival (TDOA) and KF in LOS environment is proposed. Simulation results show that the positioning accuracy of the proposed algorithm is improved in various environments. Finally, validated using more than 1000 testing positions, the positioning accuracy of LS-AEKF is 73.8% to 74.1% higher than that of LS-VEKF among the two proposed algorithms in terms of 3 or 4 BSs metrics.

A Machine Learning Approach to Passive Human Motion Detection Using WiFi Measurements From Commodity IoT Devices

Human motion is a primary indicator of indoor occupancy and activity. Motion sensing has paramount importance in the energy management of modern smart buildings and is employed for automated controls of lighting and heating, ventilation and air conditioning (HVAC) equipments. The all-pervasive WiFi infrastructure in urban buildings offers an opportunistic method of human motion detection through passive sensing of WiFi received signal strength indicator (RSSI) and channel state information (CSI). This technique unfolds a plethora of Building IoT related services, in addition to sustainable energy utilization and reduced emission of greenhouse gases. In this paper, a device free human motion detection method through WiFi RSSI and CSI collected from commercial-off-the-shelf (COTS) IoT devices, is proposed. Utilizing a laptop, a smartphone and an ESP32 as receivers, WiFi RSSI and CSI samples were collected from two residential buildings, to constitute 6 datasets. A four dimensional feature vector that exploits the data spread in time domain, is extracted from the collected samples and utilized to train a two stage ensemble machine learning model. A comparison of various RSSI based datasets indicates a mean cross validation accuracy of up to 99.7% and 97.8% in line of sight (LoS) and through-the-wall scenarios, respectively. The detection accuracy in non LoS environments can be enhanced using CSI based features, enabling motion detection in different rooms using a single WiFi router.

Map-Assisted Constellation Design for mmWave WDM With OAM in Short-Range LOS Environment

We consider a system that integrates positioning and single-user millimeter wave (mmWave) communication, where the communication part adopts wavelength division multiplexing (WDM) and orbital angular momentum (OAM). This paper addresses the multi-dimensional constellation design in short-range line-of-sight (LOS) environment, with stable communication links. We propose a map-assisted method to quantify the system parameters based on positions and reduce real-time computing overhead. We explore the possibility of using a few patterns in the maps, and investigate its performance loss. We first investigate the features of OAM beams, and find that the link gain ratio between any two sub-channels remains unchanged at some specific positions. Then, we prove that a fixed constellation can be adopted for the positions where the link gain matrices are sufficiently close to be proportional. Moreover, we prove that the system can adopt a fixed power vector to generate a multi-dimensional constellation if the difference between fixed power vector and optimal power vector is small. Finally, we figure out that the constellation design for all receiver positions can be represented by a few constellation sets.

Channel measurements and propagation characterization for indoor terahertz communication

This article presents measurements and simulations of terahertz indoor channels. The measurements are performed in two environments, line-of-sight (LoS) and reflected non-line-of-sight (RNLoS). For the LoS environment, the path loss of different distances and the influence of absorbers are investigated. For the RNLoS environment, different materials are used as reflectors. The time-frequency characteristics of indoor channels are measured by changing the transceiver angle, such as path loss, angle expansion, and power delay profile. And simulations are conducted to verify the measured data. The results show that in the LoS environment, strong multipath caused by the transmitter and receiver electronics is presented. In the RNLoS environment, the path loss is similar to the free-space path loss when the transmit angle is equivalent to the receive angle. Additionally, the results of the simulation show that the path loss changes regularly when the angle between the reflecting surface and the table changes.

ALNN‐based LOS/NLOS identification in 3D millimetre wave channel

Line-of-sight (LOS)/non-line-of-sight (NLOS) identification is crucial for millimetre-wave (mmW) since it is vulnerable to blocking in the NLOS environment. In this paper, an LOS/NLOS environment identification method is proposed based on angle information learning. Specifically, a neural network named ALNN (angle information learning neural network) is employed, which can learn the difference between the various angle paraments to identify the LOS and NLOS environments for mmW wireless communication systems. First, the 3D mmW channel model is applied to extract the desired angle information. Moreover, an mmW wireless transmission system is constructed to introduce actual measurement data to test the proposed method. Furthermore, various hyperparameters of the ALNN are adjusted to improve identification accuracy. Simulation results show that the proposed method has outstanding identification performance, and the identification accuracy is up to 99.41%.

Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication.

The phase of the channel matrix elements has a significant impact on channel capacity in a mobile multiple-input multiple-output (MIMO) communication system, notably in line-of-sight (LoS) communication. In this paper, the general expression for the phase of the channel matrix at maximum channel capacity is determined. Moreover, the optimal antenna configuration of the 2 × 2 and 3 × 3 transceiver antenna array is realized for LoS communication, providing methods for optimal antenna placement, which can be used in short-range LoS communication and non-scattering environment communication, such as coupling train communication and inter-satellite communication. Simulation results show that the 2 × 2 rectangular antenna array is more suitable for the communication of coupling trains, while the 3 × 3 circular arc antenna array is more suitable for virtual coupling trains according to antenna configurations. Moreover, the 2 × 2 antenna rectangular configuration proposed in this paper has reached the optimal channel in inter-satellite communication, which lays a foundation for the deployment of communication systems.

Physical (PHY) Layer Analysis of Data Transmission in MIMO Wireless Networks in Line- of-Sight (LoS) Environments

This research paper presents the Physical (PHY) layer analysis of multiple input multiple output (MIMO) systems in line-of-sight environments between a user equipment (UE) and a base station when an information data is transmitted. The data transmission in wireless networks often takes place in non-line-of-sight in urban environments and Line-of-Sight (LoS) in rural environments depending on the bandwidth, power constraints. Using MIMO technology, the data transmission takes place with increased number of transmit and receive antennas which results in reduced signal to noise ratio (SNR) requirement. In this research work, simulation for PHY layer metric of probability of error, mean square error (MSE) for data transmission in MIMO wireless networks is obtained Matrix Laboratory (MATLAB) for LoS environment which follows Rician distribution. Probability of error reduces to a significant extent when the Rician factor increases as the line-of-sight component prevails which also takes lesser power in the lower SNR limits. Further, in MIMO systems also the probability of error and MSE reduces in concatenation with single antenna terminals thereby exploring power control a significant possibility for implementation in massive MIMO and millimeter wave communication systems for 5G and 6G systems.

Разработка и анализ системы с множеством излучателей и множеством приёмников для исследовательских целей

Systems with multiple input and multiple output (MIMO) play a key role in modern wireless communication devices. MIMO are used in wireless networks to increase channel throughput and transmission reliability. The implementation of the MIMO system will speed up research in the field of improving communication systems. In this article, we present a system-level analysis of a block level analog RF receiver in a MIMO system in accordance with the performance requirements described in the 3GPP Long-Term Evolution standard. An analytical study of the analog receiving part was carried out. The results show that the proposed system architecture satisfies the given requirements with a good margin. Numerical simulation of the operation of the 44 MIMO system in the line-of sight environments was performed. Taking into account the influence of only the noise of the receiver, a curve is obtained that determines the dependence of the error probability on the distance between the transmitter and receiver. Using this curve, it is possible to determine the distance corresponding to a given error probability for given aperture sizes of the transmitting and receiving arrays.

AI-Based Channel Prediction in D2D Links: An Empirical Validation

Device-to-Device (D2D) communication propelled by artificial intelligence (AI) will be an allied technology that will improve system performance and support new services in advanced wireless networks (5G, 6G and beyond). In this paper, AI-based deep learning techniques are applied to D2D links operating at 5.8 GHz with the aim at providing potential answers to the following questions concerning the prediction of the received signal strength variations: i) how effective is the prediction as a function of the coherence time of the channel? and ii) what is the minimum number of input samples required for a target prediction performance? To this end, a variety of measurement environments and scenarios are considered, including an indoor open-office area, an outdoor open-space, line of sight (LOS), non-LOS (NLOS), and mobile scenarios. Four deep learning models are explored, namely long short-term memory networks (LSTMs), gated recurrent units (GRUs), convolutional neural networks (CNNs), and dense or feedforward networks (FFNs). Linear regression is used as a baseline model. It is observed that GRUs and LSTMs present equivalent performance, and both are superior when compared to CNNs, FFNs and linear regression. This indicates that GRUs and LSTMs are able to better account for temporal dependencies in the D2D data sets. We also provide recommendations on the minimum input lengths that yield the required performance given the channel coherence time. For instance, to predict 17 and 23 ms into the future, in indoor and outdoor LOS environments, respectively, an input length of 25 ms is recommended. This indicates that the bulk of the learning is done within the coherence time of the channel, and that large input lengths may not always be beneficial.

An Indoor Positioning and Tracking Algorithm Based on Angle-of-Arrival Using a Dual-Channel Array Antenna

The angular position measurement of an array antenna based on a wireless signal has high accuracy in an indoor no-occlusion environment. However, due to the high complexity of indoor environments, signal occlusion, multipath, and other interfering factors are inevitable when users move randomly, which can greatly reduce the positioning accuracy. In addition, different directions of the positioning source signal can also affect the positioning result. The switching wheels of the dual-polarization antenna array are collected in channel 1, the fast Fourier transform (FFT) is applied to the data of channel 2 to estimate the frequency offset, and the phase of the data is compensated. Using the FFT frequency offset estimation, the high-precision positioning of a single base station is realized using the dual-channel switch and dual-polarization antenna array in turn. Aiming at analyzing the affecting factors of the positioning system accuracy, the strong tracking kalman filter algorithm is studied. At the same time, the singular value decomposition of the covariance matrix is performed to improve the robustness of the strong tracking kalman filter, and the adaptive factor is introduced to improve the filtering accuracy. The proposed positioning algorithm can achieve the positioning accuracy within 1 m in the coverage area in a line-of-sight (LOS) environment, while the dynamic positioning accuracy within 1 m cannot be guaranteed in the coverage area in a non-line-of-sight (NLOS) environment. On this basis, the analysis of the static, rotational, and dynamic positioning accuracies of the source in the LOS and NLOS environments shows that the proposed singular value decomposition strong tracking kalman filter (SVD-STKF) algorithm can improve the overall positioning accuracy of the system by 0.03 m, and the maximum error in the LOS environment can be reduced by 0.08 m. The proposed SVD-STKF algorithm can correct the Hausdorff distance of dynamic positioning by up to 0.513 m in the NLOS environment where the system’s positioning accuracy decreases sharply due to the signal shielding. Also, it can make the positioning results smoother and achieve a good correction effect for the points far away from the true trajectory.

Exploiting User Mobility for WiFi RTT Positioning: A Geometric Approach

Recently, round-trip time (RTT) measured by a fine-timing measurement protocol has received great attention in the area of WiFi positioning. It provides an acceptable ranging accuracy in favorable environments when a line-of-sight (LOS) path exists. Otherwise, a signal is detoured along with non-LOS (NLOS) paths, making the resultant ranging results different from the ground truth, called an RTT bias, which is the main reason for poor positioning performance. To address it, we aim at leveraging the user mobility trajectory detected by a smartphone’s inertial measurement units, called pedestrian dead reckoning (PDR). Specifically, PDR provides the geographic relation among adjacent locations, guiding the resultant positioning estimates’ sequence not to deviate from the user trajectory. To this end, we describe their relations as multiple geometric equations, enabling us to render a novel positioning algorithm with acceptable accuracy. Depending on the mobility pattern being linear or arbitrary, we develop different algorithms divided into two phases. First, we can jointly estimate an RTT bias of each access point (AP) and the user’s step length by leveraging the geometric relation mentioned above. It enables us to construct a user’s relative trajectory defined on the concerned AP’s local coordinate system. Second, we align every AP’s relative trajectory into a single one, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">trajectory alignment</i> , equivalent to transformation to the global coordinate system. As a result, we can estimate the sequence of the user’s absolute locations from the aligned trajectory. Various field experiments extensively verify the proposed algorithm’s effectiveness that the average positioning error is approximately 0.369 (m) and 1.705 (m) in LOS and NLOS environments, respectively.

Side-Channel Propagation Measurements and Modeling for Hardware Security in IoT Devices

The ubiquitous interconnectivity of electronic devices offered by Internet-of-Things (IoT) networks has been increasingly embraced in a wide range of applications. In IoT networks, threats to hardware security are often not perceived as serious, with the assumption that an attack could only be carried out at close proximity. However, in this article, we show that through electromagnetic (EM) side-channel signal leakage, operational information and program activities of IoT devices and field-programmable gate array (FPGA) modules can be garnered from approximately 200 m away in an outdoor line-of-sight (LOS) environment. We describe an extensive measurement campaign conducted to investigate the aforementioned leakage and provide propagation models that can be used to predict the power (and corresponding variation i.e., shadowing gain) of the EM side-channel signal emanation at various distances, scenarios, and environments. With a circularly polarized receiver antenna, our results show that the received power of the emanated EM side-channel (carrier) signal varies from about −61 dBm at 1 m to about −112 dBm at 200 m in the outdoor LOS environment. Furthermore, a received signal power of about −73 dBm was observed at 1 m and −88 dBm was recorded at 10 m in an indoor LOS environment. Power variation (shadowing gain) of about 3.6 and 2.0 dB was observed in the outdoor and indoor environments, respectively. This work is relevant for EM side-channel leakage countermeasure development and provides pertinent information to embedded systems and wireless network security engineers.

WiGId: Indoor Group Identification with CSI-Based Random Forest.

Human identity recognition has a wide range of application scenarios and a large number of application requirements. In recent years, the technology of collecting human biometrics through sensors for identification has become mature, but this kind of method needs additional equipment as assistance, which cannot be well applied to some scenarios. Using Wi-Fi for identity recognition has many advantages, such as no additional equipment as assistance, not affected by temperature, humidity, weather, light, and so on, so it has become a hot topic of research. The methods of individual identity recognition have been more mature; for example, gait information can be extracted as features. However, it is difficult to identify small-scale (2–5) group personnel at one time, and the tasks of fingerprint storage and classification are complex. In order to solve this problem, this paper proposed a method of using the random forest as a fingerprint database classifier. The method is divided into two stages: the offline stage trains the random forest classifier through the collected training data set. In the online phase, the real-time data collected are input into the classifier to get the results. When extracting channel state information (CSI) features, multiple people are regarded as a whole to reduce the difficulty of feature selection. The use of random forest classifier in classification can give full play to the advantages of random forest, which can deal with a large number of multi-dimensional data and is easy to generalize. Experiments showed that WiGId has good recognition performance in both LOS (line of sight) and N LOS (None line of sight) environments.

Performance Research of LoRa at High Transmission Rate

In recent years, most of Lora’s research in the field of wireless technology has focused on the experimental exploration of its stability and communication range, without much attention to the impact of the actual transmission rate. However, as the length of Lora transmission load increases, the growth rate of low-speed transmission time is faster than that of high-speed transmission time. Therefore, it means that for the transmission of large capacity packets, the lower the transmission rate, the more time it takes; the longer the time, the more power it consumes. In this paper, the SNR, RSSI and packet loss rate of Lora over 3 Kbps in urban area and mountainous area were tested and explored respectively, and the RSSI curve with distance in different environment was obtained. The test results show that the actual communication distance of Lora with high transmission rate of 21875 bps in line of sight environment can reach 3 Km, packet loss rate is less than 1%. In addition, the influence of Lora on communication quality under different occlusion degrees in a certain range was tested. In the range of 500 m to 3 km, when the occlusion ratio was 0-5%, the highest speed reference value was 21875 bps, 5-10% reference value was 6250 bps, and more than 10% reference value was 3125 bps. In this paper, Lora SX1278 transceiver was used, in the same transmission of 100 KB data, the actual transmission rate is further improved by using the maximum duty cycle and improving the request response mechanism. Compared with a Lora system for image transmission, the transmission time is reduced by 81.3% and the transmission speed is increased by 4.3 times, which greatly improves the transmission efficiency of large capacity data such as image.

Toward Elderly Care: A Phase-Difference-of-Arrival Assisted Ultra-Wideband Positioning Method in Smart Home

Location-based services (LBSs) for elderly care is a trending topic in smart homes. The key issue is the high accurate positioning for the elderly. Ultra-wideband (UWB) is a centimeter-level positioning accuracy in line-of-sight (LOS) environments. However, most of existing UWB positioning methods need non-line-of-sight (NLOS) identification and compensation, and thus leading to severe deterioration in positioning accuracy in presence of complex indoor environments where the elderly lived. This article proposes a phase-difference-of-arrival (PDOA) assisted UWB positioning method (PDOA-UWB) for elderly care. In our positioning framework, we first calculate the phase difference of arrival from PDOA chip integrated in the UWB base stations (BSs) to obtain a coarse location of the elderly, which is further used to distinguish which nearby BSs are in LOS environments and which are in NLOS environments. Then, a PDOA-UWB positioning solution is derived to improve the positioning accuracy of the elderly. Compared with some existing methods, our method achieves higher accuracy with less NLOS compensation, and is easier to be implemented in complex indoor environments for simple practical engineering applications. Experimental results show the efficacy of our proposed method.

Ultrasound TDoA Positioning Using the Best Linear Unbiased Estimator and Efficient Anchor Placement

In this article, positioning based on ultrasound time-difference-of-arrival (TDoA) measurements is investigated. The best linear unbiased estimator (BLUE) algorithm is proposed, compared to a least-squares estimator (LSE), and validated using numerical simulations. Optimal placement of anchor nodes is also investigated by evaluating and minimizing the geometrical dilution of precision (GDOP) with some geometrical constraints. The comparison was carried out by assuming the multipath channel affecting line-of-sight (LoS) propagation. It is shown that the accuracy and the processing time of the proposed BLUE approach are similar to that of the LSE while providing a simpler implementation. It is also shown that anchors' constellations that minimize the GDOP can lead to increased robustness to multipath and that the BLUE provides a more robust performance than LSE when multipath is introduced in a LoS environment.

Impact of an Interfering Node on Unmanned Aerial Vehicle Communications

Unlike terrestrial communications, unmanned aerial vehicle (UAV) communications have some advantages such as the line-of-sight (LoS) environment and flexible mobility. However, the interference will be still inevitable. In this paper, we analyze the effect of an interfering node on the UAV communications by considering the LoS probability and different channel fading for LoS and non-line-of-sight (NLoS) links, which are affected by horizontal and vertical distances of the communication link. We then derive a closed-form outage probability in the presence of an interfering node for all the possible scenarios and environments of main and interference links. After discussing the impacts of transmitting and interfering node parameters on the outage probability, we show the existence of the optimal height of the UAV that minimizes the outage probability. We also show the NLoS environment can be better than the LoS environment if the average received power of the interference is more dominant than that of the transmitting signal on UAV communications. Finally, we analyze the network outage probability for the case of multiple interfering nodes using stochastic geometry and the outage probability of the single interfering node case, and show the effect of the interfering node density on the optimal height of the UAV.