Multipath Propagation Effects Research Articles

Indoor Wireless Network Coverage Area Optimization

The presence of interference has a significant impact on wireless network connections indoors. Because of the effects of multipath propagation, such as reflection, refraction, and scattering of radio waves by the structure of the building, the sent signal can usually be received in free space or via more than one pathway, and the consequence might be a phenomenon known as multipath fading. For different results, propagation models have been identified, which provide the propagation features of the initial evaluation. There are two types of wireless propagation models: empirical and theoretical models that deal with coverage, overlapping channels, and wireless network performance. The planning system was set out using a practical and theoretical approach. Furthermore, this comparison may aid in determining the accuracy of survey measures in the context of indoor wireless monitoring and provide an estimate of the breadth and performance of wireless networks in the form of a new topology with contour presentation. Layout plan optimization The typical RSSI of a Wi-Fi system ranges from -40 dBm to -55 Dbm, with a power of 17-18 Dbm, and is applied to channels 1 through 11 non-overlapping in the design of networks with multiple APs that are nearby. The new topology represents the optimization outcomes, accompanied by a contour display and dispersed over the region.

Power losses and efficiency analysis of non-uniform time modulated arrays over a ground plane

In this paper, the analysis of non-uniform time modulated arrays (NTMA) on the ground plane, in which antenna elements are modulated with different modulation frequencies, is presented theoretically by evaluating the horizontal and vertical polarization conditions. The constructive and disruptive effects of multipath propagation consisting of direct radiation using the line of sight and the most basic reflected radiation are investigated at the observation point. Furthermore, the closed-form expression of the total harmonic power losses of the NTMA placed at a certain height from the ground plane is derived. Depending on the height of the array elements from the ground plane, it causes positive and negative effects on the power consumption rate of NTMA’s harmonics and thus on the radiation efficiency. Also, the representative examples of NTMA on the ground plane, unlike the isolated environment are comprehensively presented.

Effect of the polarization of satellite navigation antennas on multipath error envelopes

The paper estimates the envelope of error introduced by the effect of multipath propagation in global navigation satellite systems. Based on the mathematical model of the radio path of the direct and reflected signals, the results of the multipath error envelopes for various types of navigation antennas are obtained. Keywords: antenna, GNSS, multipath interference, interference, radiation pattern, multipath error envelope.

Оценка пространственно-поляризационной селекции антенн спутниковой навигации на основе анализа ошибки огибающей многолучевой интерференции

The paper estimates error envelopes introduced by the effect of multipath propagation in global navigation satellile systems. Based on the mathematical model of the radio path of the direct and reflected signals, the results of the multipath error envelopes for various types of navigation antennas are obtained.

Deep Neural Network Correlators for GNSS Multipath Mitigation

Machine learning and, more precisely, data-driven models are providing solutions where physics-based models are intractable. This article discusses the use of deep learning models to characterize the intricate effects of multipath propagation on GNSS correlation outputs. Particularly, we aim at substituting standard correlation schemes, optimal under single-ray Gaussian noise assumptions, with Neural Network (NN)-based correlation schemes, that are able to learn the otherwise challenging to model multipath channels. The paper shows that Deep Neural Networks (DNNs), as applied to tracking loops, can provide enhanced performance as compared to standard correlation schemes in <inline-formula><tex-math notation="LaTeX">$i)$</tex-math></inline-formula> Line-of-Sight (LOS) scenarios, by filtering out more noise thanks to strong prior regularization through knowledge of correlation characteristics and Gaussian noise during training process; and <inline-formula><tex-math notation="LaTeX">$ii)$</tex-math></inline-formula> at the same time, the DNN can adjust its behavior to better disentangle multipath signals from LOS signals. This article provides results showing the superiority of the proposed DNN trained models, with focus on time-delay tracking in a variety of realistic scenarios.

Deconvolving Pulsar Signals with Cyclic Spectroscopy: A Systematic Evaluation

Abstract Radio pulsar signals are significantly perturbed by their propagation through the ionized interstellar medium. In addition to the frequency-dependent pulse times of arrival due to dispersion, pulse shapes are also distorted and shifted, having been scattered by the inhomogeneous interstellar plasma, affecting pulse arrival times. Understanding the degree to which scattering affects pulsar timing is important for gravitational-wave detection with pulsar timing arrays (PTAs), which depend on the reliability of pulsars as stable clocks with an uncertainty of ∼100 ns or less over ∼10 yr or more. Scattering can be described as a convolution of the intrinsic pulse shape with an impulse response function representing the effects of multipath propagation. In previous studies, the technique of cyclic spectroscopy has been applied to pulsar signals to deconvolve the effects of scattering from the original emitted signals, increasing the overall timing precision. We present an analysis of simulated data to test the quality of deconvolution using cyclic spectroscopy over a range of parameters characterizing interstellar scattering and pulsar signal-to-noise ratio (S/N). We show that cyclic spectroscopy is most effective for high S/N and/or highly scattered pulsars. We conclude that cyclic spectroscopy could play an important role in scattering correction to distant populations of highly scattered pulsars not currently included in PTAs. For future telescopes and for current instruments such as the Green Bank Telescope upgraded with the ultrawide bandwidth receiver, cyclic spectroscopy could potentially double the number of PTA-quality pulsars.

Non-Line-of-Sight Localization of Passive UHF RFID Tags in Smart Storage Systems

The UHF radio-frequency identification (RFID) has gained growing attention for tagged object localization in smart storage systems. Due to Non-Line-Of-Sight (NLOS) condition, it is challenging to accurately locate the position of tags inside closed spaces. In this paper, we propose a precise and cost-effective solution for tagged object localization in closed spaces, using only received signal strength (RSS) information. We establish a RSS profile for each tag and discover some important features of RSS profiles including uniqueness, time-variation, column-dependence and waveform-similarity. Based on these features, we propose a reference-free RSS-profile (RFRP) localization scheme. The advantage of our propose scheme is to accurately localize multiple tags in closed spaces by overcoming the challenges including the lack of pre-deployed reference tags, NLOS propagation, multi-path propagation and coupling effect. The RFRP scheme first roughly estimates tags’ coordinates based on Peak Asymmetry Factor, then acquires reference-tag substitutes through the similarity of RSS sequences. Subsequently, our scheme refines the relative positions of all tags by these substitutes. Finally all tags’ absolute positions are estimated through a RSS-ranging model. Extensive experiment results demonstrate that our approach can achieve high ordering accuracy and localization accuracy for the tags inside closed spaces.

Exchanging Bandwidth With Aperture Size in Wireless Indoor Localization - Or Why 5G/6G Systems With Antenna Arrays Can Outperform UWB Solutions

The localization of wireless devices in indoor scenarios presents a major challenge because of multipath propagation. Hence, the majority of the research community has focused on increasing the available bandwidth of localization systems, leading to the emergence of the ultra wide band (UWB) radar. However, the hardware implementation of UWB transceivers is challenging itself and, hence, their utilization in commercial low-cost wireless devices is not to be expected in the near future. Hence, instead of evaluating frequency dependent phases via UWB, the measurement of spatially distributed phases represents a valuable alternative. Therefore, this article presents a comparison of phase-difference-of-arrival (PDOA) and time-of-arrival (TOA) systems. For this purpose, we compare the measurement sensitivity, the effects of multipath propagation, and the hardware complexity. Based on the results, the applicability of typical position estimators is discussed. Thereby, we argue that PDOA-based localization with large receiver arrays appears to be the better choice to localize wireless devices, because it enables highly accurate positioning using narrow band signals without elaborated transmitter–receiver synchronization. To validate this, indoor localization measurements are presented and compared with UWB results in extant literature.

Development of a Far-Field Noise Estimation Model for an Aircraft Auxiliary Power Unit

Aircraft Auxiliary Power Unit (APU) is one of the major aircraft systems and is reported to be a key driver of unscheduled maintenance. So far, the research has been focused on the implementation of the APU thermodynamic state data to isolate and diagnose faults. To advance the available diagnostic techniques, research work has been initiated to explore the potential of employing far-field microphone data for the identification and isolation of APU faults. This paper aims to address the first step required in the overall effort and proposes a novel methodology for the development of a noise model that can be used for evaluating noise as a source of fault diagnostics. The methodology integrates experimentally acquired full-scale aircraft state and noise data, a physics-based APU thermodynamic model, and semi-empirical noise models to estimate the noise produced by an aircraft APU based on a limited parameter-set. The methodology leads to a model which works by estimating the unknown thermodynamic parameters from the limited dataset and then passes on the relevant parameters to noise estimation models (combustion/jet noise models). An inherent part of the model is the effect of multipath propagation and ground reflections for which a relationship has been analytically derived that considers all the necessary parameters. The developed model has been validated against experimental noise and thermodynamic data acquired from a Boeing 737-400 aircraft APU under several different operating conditions. The acquired noise estimates suggest that the proposed approach provides an accurate estimation of the far-field noise under a wide range of APU operating conditions, both at the sub-system and APU level. The model would act as an enabler to simulate APU noise data under degraded functional states and subsequently developing fault diagnostic schemes based on the far-field noise data.

Low-level signals radiomonitoring using periodic spectrum samples analysis

The article presents practical implementation of energy detection method based upon non-parametric statistics computed using periodic spectrum samples provided by measuring equipment. The method enables efficient detection and monitoring for signals with low or negative signal-to-noise ratio. The method's sensitivity is limited by measuring equipment inherent noise fluctuations and can be a priory experimentally established for certain experimental hardware settings and desirable spectrum samples lengths. Sensitivity thresholds (in terms of signal-to-noise ratio) for reliable (with probability &gt; 0,98) signal detection for a typical spectrum analyzer used in experiment varied from –11 dB to + 0,6 dB for spectrum samples lengths ranged between 30 000 and 470 spectrums respectively. The suggested energy detection method can be used for unstable and intermittent signals detection, which are active (or above sensitivity threshold) only for a fraction of spectrum sample recording time. The method is independent of signal's modulation (if any is used), amplitude variability profile and signal's probability distribution features. Experimentally determined sensitivity threshold levels for real radio frequency signals coincided within 1,9 dB tolerances with corresponding levels estimated from spectrum analyzer inherent noise fluctuations for all implemented spectrum samples lengths. The data recording time for abovementioned spectrum samples lengths ranged between 207 and 3,2 seconds respectively and was entirely hardware-dependent parameter. Experiment proved equal efficiency and reliability of the suggested method for reliable detection for both white noise signal (generated by analog generator) and broadcasted LTE signal (generated by cellular base stations), which were affected by multi-path propagation effects and average signal level instability due to subscribers time-varying activity. The experiment showed the proposed energy detection method besides detection of low-level radio frequency signals (down to –11 dB SNR) provides highly reliable assessment of the detected signal's signal-to-noise ratio with 0,6 dB tolerance and 0,95 probability. The energy detection method demonstrated zero level of false detections when there was no signal at the spectrum analyzer input (the input port of the instrument was terminated by a matched load), which is essential for method applicability in tasks of highly reliable detection of low-level signals from various types of sources. Taking into account specifications of available hardware, required sensitivity level and limits for data recording time it is possible to choose optimal length of spectrum sample for the energy detection method, which would be the most reasonable for any task in question. The energy detection method based upon non-parametric statistics computed using periodic spectrum samples can be effectively used in detection and radiomonitoring of low-level signals, in radio frequency electromagnetic compatibility research tasks and radio propagation path properties analysis in high loss environment.

Image‐Based Indoor Localization Using Smartphone Camera

With the increasing demand for location‐based services such as railway stations, airports, and shopping malls, indoor positioning technology has become one of the most attractive research areas. Due to the effects of multipath propagation, wireless‐based indoor localization methods such as WiFi, bluetooth, and pseudolite have difficulty achieving high precision position. In this work, we present an image‐based localization approach which can get the position just by taking a picture of the surrounding environment. This paper proposes a novel approach which classifies different scenes based on deep belief networks and solves the camera position with several spatial reference points extracted from depth images by the perspective‐n‐point algorithm. To evaluate the performance, experiments are conducted on public data and real scenes; the result demonstrates that our approach can achieve submeter positioning accuracy. Compared with other methods, image‐based indoor localization methods do not require infrastructure and have a wide range of applications that include self‐driving, robot navigation, and augmented reality.

Adaptive Radar Detection and Classification Algorithms for Multiple Coherent Signals

In this paper, we address the problem of target detection in the presence of coherent (or fully correlated) signals, which can be due to multipath propagation effects or electronic attacks by smart jammers. To this end, we formulate the problem at hand as a multiple-hypothesis test that, besides the conventional radar alternative hypothesis, contains additional hypotheses accounting for the presence of an unknown number of interfering signals. In this context and leveraging the classification capabilities of the Model Order Selection rules, we devise penalized likelihood-ratio-based detection architectures that can establish, as a byproduct, which hypothesis is in force. Moreover, we propose a suboptimum procedure to estimate the angles of arrival of multiple coherent signals ensuring (at least for the considered parameters) almost the same performance as the exhaustive search. Finally, the performance assessment, conducted over simulated data and in comparison with conventional radar detectors, highlights that the proposed architectures can provide satisfactory performance in terms of probability of detection and correct classification.

Application of Diversity Combining with RLS Adaptive Filtering in Data Transmission in a Hydroacoustic Channel.

When transmitting data in a hydroacoustic channel under difficult propagation conditions, one of the problems is intersymbol interference (ISI) caused mainly by the effect of multipath propagation. This phenomenon leads to a decrease in transmission parameters, and sometimes completely prevents it. Therefore, we have made an attempt to use diversity combining with Recursive Least Squares (RLS) adaptive filtering to improve the quality of data transmission in a hydroacoustic channel with strong reflections. The method was tested in simulation and during measurements in a real environment. The influence of the method on data transmission in the hydroacoustic channel was examined in detail. The obtained results allow us to draw conclusions regarding the purposefulness of use of diversity combining and RLS adaptive filtering in order to improve the quality of data transmission by reducing the effect of ISI.

Precision indoor three‐dimensional visible light positioning using receiver diversity and multi‐layer perceptron neural network

In recent times, several applications requiring highly accurate indoor positioning systems have been developed. Since the global positioning system is unavailable/less accurate in the indoor environment, alternative techniques such as visible light positioning (VLP) are considered. The VLP system benefits from the wide availability of illumination infrastructure, energy efficiency and the absence of electromagnetic interference. However, there is a limited number of studies on three-dimensional (3D) VLP and the effect of multipath propagation on the accuracy of the 3D VLP. This study proposes a supervised artificial neural network to provide accurate 3D VLP whilst considering multipath propagation using receiver diversity. The results show that the proposed system can accurately estimate the 3D position with an average root mean square (RMS) error of 0.0198 and 0.021 m for line-of-sight (LOS) and non-LOS link, respectively. For 2D localisation, the average RMS errors are 0.0103 and 0.0133 m, respectively.

Performance comparison of time‐of‐arrival estimation techniques for LTE signals in realistic multipath propagation channels

<h3>Abstract</h3> Long term evolution (LTE) signals have the potential for use in positioning, especially in challenging environments. The time-of-arrival (TOA)-based technique supported by LTE is attractive due to its high positioning accuracy. However, it is vulnerable to multipath propagation effects in typical LTE channels. This paper will summarize several existing advanced TOA estimators for LTE signals, i.e., first peak detection, information theoretic criteria, super-resolution algorithm, and delay-lock loop (DLL). Later, the paper will evaluate the TOA estimation performances of these techniques with multipath propagation effects and varying signal conditions using simulations. For the DLL, the multipath error envelope metric is assessed for different signal bandwidths. The root mean square errors of the TOA estimations are compared to evaluate suitable TOA estimators under various conditions. Finally, some other performance characteristics of these techniques are also discussed.

Adaptive Selection Combiner over Log-normal Fading Channel in Wireless Communication System

Wireless communication system has found worldwide acceptability in providing acceptable services due to its portability, flexibility and ease of usage. However, the system is characterized by severe multipath propagation effects that degrade its performance. Selection Combiner (SC) as one of the techniques being used to address this problem is associated with poor performance due to fixed constellation of the modulation scheme. Hence, in this paper, an Adaptive Selection Combiner (ASC) using Constellation Adaptation Algorithm (CAA) with an expression over Log-normal fading channel is proposed. ASC is developed using conventional SC (CSC) and CAA. Ten thousand randomly generated bits are modulated using each of Phase Shift Keying (PSK) and Quadrature Amplitude Modulation (QAM) schemes. The modulated signals at varying paths are scanned by CSC to choose a path with the highest Signal to Noise Ratio (SNR). The output of SC which is a function of Channel State Information (CSI) acquired is used to determine the channel gain. The constellation size is then adjusted based on the value of channel gain through CAA. The proposed adaptive technique is simulated using MATLAB software and evaluated using Bit Error Rate (BER) to determine its performance. The results obtained show that ASC gives lower BER values than CSC due to the self-adjustment of the constellation size. Therefore, the proposed ASC can be deployed to improve the performance of wireless communication systems.

Survey of CSI fingerprinting‐based indoor positioning and mobility tracking systems

Techniques for indoor positioning systems (IPSs) can be categorised as range-based or range-free. Range-based methods rely on geometric mappings to approximate a location given the calculated distances or angles from multiple reference points. In contrast, range-free strategies utilise fingerprinting, wherein an acquired fingerprint data is compared to a pre-collected dataset to identify the best position estimate. Among these, fingerprinting of channel state information (CSI) is preferred over other information such as received signal strength indicator as the former can exploit the effect of multipath propagation and is robust against non-line-of-sight channels. CSI has the potential to achieve cm-level positioning accuracy with a single reference point only. In this study, the authors survey CSI fingerprinting-based indoor positioning and mobility tracking systems. The process of fingerprinting that includes site surveying and signal preprocessing is discussed in detail. They determine the potential challenges of such systems and propose remedies to improve positioning accuracy. In general, spatial diversity, such as multiple-input multiple-output antennas and wireless sensor networks, or frequency diversity (e.g. high subcarrier count, frequency hopping mechanism) are exploited to achieve high positioning resolution. Such IPS can also be enhanced via additional sensors or spatial graphs for motion detection and tracking.

A Dataset to Evaluate IEEE 802.15.4g SUN for Dependable Low-Power Wireless Communications in Industrial Scenarios

This article presents a dataset obtained from the deployment of an IEEE 802.15.4g SUN (Smart Utility Network) single-hop network (11 nodes) in a large industrial scenario (110,044 m 2 ) for a long period of time (99 days). The dataset contains ∼11 M entries with RSSI (Received Signal Strength Indicator), CCA (Clear Channel Assessment), and PDR (Packet Delivery Ratio) values. The analyzed results show a high variability in the average RSSI (i.e., between −82.1 dBm and −101.7 dBm) and CCA (i.e., between −111.2 dBm and −119.9 dBm) values, which is caused by the effects of multi-path propagation and external interference. Despite being above the sensitivity limit for each modulation, these values result in poor average PDR values (i.e., from 65.9% to 87.4%), indicating that additional schemes are needed to meet the link reliability requirements of industrial applications. Hence, the presented dataset will allow researchers and practitioners to propose novel mechanisms and evaluate their performance using realistic conditions, enabling the dependability vision of the RAW (Reliable and Available Wireless) WG (Working Group) at the IETF (Internet Engineering Task Force).

Multipath Estimating Delay Lock Loop for LTE Signal TOA Estimation in Indoor and Urban Environments

Long-term evolution (LTE) signals are potential signals-of-opportunity for position and navigation, especially in challenging urban and indoor environments. A major challenge is that the LTE signal time-of-arrival (TOA) estimations are susceptible to the multipath propagation effects. In this paper, the multipath estimating delay lock loop (MEDLL), which is originally designed for global positioning system receivers, is applied to LTE signal TOA estimation in multipath environments. We derive the analytical expression of the correlation function for LTE signals and present the procedure for estimating parameters of the detected multipath components. Two initialization methods without and with super-resolution algorithm (SRA) are developed for the MEDLL. Our analyses show that the MEDLL with SRA-based initialization can achieve better multipath resolution, while the one without SRA has less complexity. Extensive simulations involving static multipath scenarios are conducted to examine the statistical TOA estimation performance of the proposed MEDLL with LTE cell-specific reference signal. The simulation results and computational complexity analysis indicate that the proposed MEDLL outperforms the conventional delay lock loop and SRA in term of multipath mitigation performance and computational complexity. Experimental results using real collected LTE signals in urban environments are also provided to demonstrate the effectiveness of the proposed technique for realistic scenarios.

Particle Filtering-Based Low-Elevation Target Tracking With Multipath Interference Over the Ocean Surface

As radar signals propagate above the ocean surface to determine the trajectory of a target, the signals that are reflected directly from the target arrive at the receiver along with indirect signals reflected from the ocean surface. These unwanted signals must be properly filtered; otherwise, their interference may mislead the signal receiver and significantly degrade the tracking performance of the radar. To this end, we propose a low-elevation target tracking mechanism considering the specular and diffuse reflection effects of multipath propagation over the ocean surface simultaneously. The proposed mechanism consists of a state-space model and a particle filtering algorithm and promises considerable improvements in the capacity and accuracy of the radar tracking systems. The efficiency and accuracy of the developed target tracking method are tested and compared with an unscented Kalman filtering method in two- and three-dimensional space using a series of simulation experiments.