Multipath Phenomenon Research Articles

Three-Dimensional End-to-End Modeling and Analysis for Graphene-Enabled Terahertz Band Communications

Terahertz (0.1–10 THz) band communication is envisioned as a key technology to satisfy the increasing demand for ultra-high-speed wireless links. In this paper, a 3-D end-to-end model in the THz band is developed that includes the graphene-based reflectarray antenna response and the 3-D multipath propagation phenomena. In particular, the architecture of a graphene-based reflectarray antenna is investigated, and the 3-D radiation pattern is modeled. Moreover, a 3-D THz channel model based on ray tracing techniques is developed as a superposition of the line-of-sight (LoS), reflected, and scattered paths. By using the developed end-to-end model, an in-depth analysis on the 3-D channel characteristics is carried out. Specifically, the gain at the main beam of the graphene-based reflectarray antenna is 18 dB, and the 3-dB beamwidths in the elevation and the azimuth planes are $7^\circ$ and $10^\circ$ , respectively. The use of the reflectarray leads to a decrease of the delay spread from 1.23 to 0.099 ns, which suggests that the resulting coherence bandwidth reaches 2 GHz. Moreover, the root mean square (rms) angular spread in the elevation plane is less than $0.12^\circ$ , which is one tenth of that without beamforming. Furthermore, the wideband channel capacity at THz frequencies is characterized, which can be enhanced with a larger transmit power, a lower operating frequency, a larger bandwidth, and a higher beamforming gain. Finally, the beamforming gain enabled by the reflectarray antenna is compromised at the cost of the strict beam alignment, and the deviation needs to be smaller than $11^\circ$ . The provided analysis and the channel physical parameters lay out the foundation and are particularly useful for realizing reliable and efficient ultra-high-speed wireless communications in the THz band.

Enhancing BER Performance Limit of BCH and RS Codes Using Multipath Diversity

Modern wireless communication systems suffer from phase shifting and, more importantly, from interference caused by multipath propagation. Multipath propagation results in an antenna receiving two or more copies of the signal sequence sent from the same source but that has been delivered via different paths. Multipath components are treated as redundant copies of the original data sequence and are used to improve the performance of forward error correction (FEC) codes without extra redundancy, in order to improve data transmission reliability and increase the bit rate over the wireless communication channel. For a proof of concept Bose, Ray-Chaudhuri, and Hocquenghem (BCH) and Reed-Solomon (RS) codes have been used for FEC to compare their bit error rate (BER) performances. The results showed that the wireless multipath components significantly improve the performance of FEC. Furthermore, FEC codes with low error correction capability and employing the multipath phenomenon are enhanced to perform better than FEC codes which have a bit higher error correction capability and did not utilise the multipath. Consequently, the bit rate is increased, and communication reliability is improved without extra redundancy.

TRIEDS: Wireless Events Detection Through the Wall

In this paper, we propose a novel wireless indoor events detection system, TRIEDS. By leveraging the time-reversal technique to capture the changes of channel state information (CSI) in the indoor environment, TRIEDS enables low-complexity single-antenna devices that operate in the ISM band to perform through-the-wall indoor multiple events detection. The multipath phenomenon denotes that the electromagnetic signals undergo different reflecting and scattering paths in a rich-scattering environment. In TRIEDS, each indoor event is detected by matching the instantaneous CSI to a multipath profile in a training database. To validate the feasibility of TRIEDS and to evaluate the performance, we build a prototype that works on ISM band with carrier frequency being 5.4 GHz and 125 MHz bandwidth. Experiments are conducted to detect the states of the indoor wooden doors. Experimental results show that with a single receiver access point and transmitter (client), TRIEDS can achieve a detection rate higher than 96.92% and a false alarm rate smaller than 3.08% under either line-of-sight (LOS) or non-LOS transmission.

Altitude measurement of low elevation target in complex terrain based on orthogonal matching pursuit

In this paper, a novel method is proposed to deal with altitude measurement of low-elevation target for very high frequency (VHF) radars in complex terrain. In VHF radars, the altitude measurement of low elevation target is unstable and unfavorable owing to the presence of multipath phenomenon. The multipath signal, reflected by the ground, is highly correlated with the direct signal. Moreover, the direct and multipath signal always fall within the same beamwidth. Especially, in complex terrain, the multipath signal is modulated by the rough and irregular reflecting, which results in performance depravation of the exiting direction-of-arrival (DOA) estimation methods, and makes the altitude measurement of low elevation target difficult. To resolve this problem, a perturbation multipath propagation model for low-angle tracking and localization is developed to simulate the echo of the low elevation target in complex terrain, where the effect of irregular reflecting is considered as random amplitude and phase perturbation of the multipath signal. Then a novel method is proposed to resolve the model and estimate the DOA of the low elevation target. Both computer simulations and real data analysis results illustrate that the proposed method can estimate the DOA with higher accuracy in comparison with the existing methods.

Device-Free RF Human Body Fall Detection and Localization in Industrial Workplaces

Fall detection and localization of human operators inside a workspace are major issues in ensuring a safe working environment. Recent research has shown that the perturbations of the radio-frequency (RF) signals commonly adopted for wireless communications can also be used as sensing tools for device-free human motion detection. Device-free RF-based human sensing applications range from tag-less body localization to detection and monitoring of human well-being (e-Health). In this paper, we propose a real-time system for human body motion sensing with special focus on joint body localization and fall detection. The proposed system continuously monitors and processes the RF signals emitted by industry-compliant radio devices operating in the 2.4 GHz ISM band and supporting machine-to-machine communication functions. Human-induced diffraction and multipath phenomena that affect RF signal propagation are leveraged for body localization while for fall detection a hidden Markov model is applied to discern different postures of the operator and to detect safety-relevant events by tracking the received signal strength indicator footprints. Fall detection performances are corroborated by extensive experimental measurements in different settings. In addition, we propose also a sensor fusion tool that is able to integrate the device-free RF-based sensing system within an industrial image sensors framework. Preliminary results, conducted during field trial measurements, confirm the effectiveness of the proposed approach in terms of localization accuracy, and sensitivity/specificity to correctly detect a fall event from preimpact postures.

RSS-Based Ranging by Leveraging Frequency Diversity to Distinguish the Multiple Radio Paths

Among various ranging techniques, Radio Signal Strength (RSS) based approaches attract intensive research interests because of its low cost and wide applicability. RSS-based ranging is prone to be affected by the multipath phenomenon which allows the radio signals to reach the destination through multiple propagation paths. To address this issue, previous works try to profile the environment and refer this profile during run-time. In a practical dynamic environment, however, the profile frequently changes and the painful retraining is needed. Rather than such static ways of profiling the environments, in this paper, we try to accommodate the environmental dynamics automatically in real-time. The key observation is that given a pair of nodes, the RSS at different spectrum channels will be different. This difference carries the valuable phase information of the radio signals. By analyzing these RSS values, we are able to identify the amplitude of signals solely from the Line-of-Sight (LOS) path. This LOS amplitude is a simple function of the path length (the physical distance). We find that the analysis is a typical non-linear curvature fitting problem that has no general routing algorithms. We prove that, this problem format is ill-conditioned which has no stable and trustable solutions. To deal with this issue, we further explore the practical considerations for the problem and modify it to a greatly improved conditioning shape. We solve the problem by numerical iterations and implement these ideas in a real-time indoor tracking system called MuD. MuD employs only three TelosB nodes as anchors. The experiment results show that in a dynamic environment where five people move around, the averaged localization error is about $1$ meter. Compared with the traditional RSS-based approaches in dynamic environments, the accuracy improves up to $10$ times.

Analysis of outage probability for in‐band device‐to‐device communications underlaying cellular network

SummaryOne of the key strategies for jointly increasing throughput and optimum resource allocation in 5G is device‐to‐device (D2D) communications, which can be obtained by minimizing the outage probability considered as an objective function of optimization problem. To minimize this objective function, we found that outage probability should be modeled by jointly considering the effect of interference, noise, and multipath phenomena. In this paper, the exact formulas for outage probability of in‐band D2D communications underlying cellular network are proposed. In the proposed model, additive white Gaussian noise and Rayleigh multipath fading are considered into 2 radio resource reuse scenarios. In the first scenario, each D2D pair is allowed to reuse radio resource block of one cellular user, whereas in the second scenario, 2 resources of 2 cellular users can be reused. The proposed formulas are compared to the approximate (nonexact) ones, which models additive white Gaussian noise by a constant variance. The numerical analysis for the first and second scenarios show that the approximate formulas and respected exact ones are in accordance with simulation results in MATLAB. Moreover, based on nonorthogonal multiple access approach, 2 approximations for the nonexact and the proposed formulas are extracted, which are acceptable for multiple resource reuse scenario. As a remarkable result, simulation results show that when the distance of the D2D pair from the respected cellular user is more than 71 m (2 times greater than average distance between the D2D nodes), multiple‐reuse scenarios offer higher throughput compared to 1‐reuse scenario in an acceptable outage probability.

A Localization Algorithm Based on V2I Communications and AOA Estimation

Motivated by safety applications in urban vehicular scenarios, where GPS does not typically provide the required positioning accuracy, a GPS-free localization technique that exploits vehicle-to-infrastructure communications is proposed. In particular, it provides for a vehicle to opportunistically use the beacon packets received from a roadside unit (RSU) in order to obtain estimates of their angle of arrival. Such estimates, together with the RSU's position information within beacon packets, are fed to a weighted least squares algorithm that aims at localizing the vehicle. The algorithm tries to take advantage of reliable measurements typically collected closer to the RSU—where a very high signal-to-noise ratio yields an accurate angular resolution—while keeping robustness against multipath phenomena. Simulation results show the effectiveness of the proposed technique.

IEEE <italic>Access</italic> Special Section Editorial: Resource Management in Vehicular Adhoc Networks: Energy Management, Communication Protocol and Future Applications

The advancements in technology have transformed the vehicles moving around us into intelligent machines. These vehicles now have the capabilities to communicate and share useful information with each other under a communication network known as Vehicular Ad hoc Network (VANETs). The aim of this vehicular digitization was to enhance the standard, ease, leisure and safety of passengers, drivers and pedestrians on the roads. Using the power of intelligent decision making and the capability of interconnectivity, VANETs has enabled vehicles to communicate and operate in different modes, such as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Sensor (V2S), Vehicle-to-broadband cloud (V2B) and Vehicle-to-Human (V2H). Vehicular communications in all these modes rely on vehicular characteristics, diverse road structures and resource management during the operations of VANETs. To meet the quality requirements, the vehicular characteristics and network constrains are always focused to improve VANETs performance. Vehicular communication is established by using infrastructure and devices based on the operational policies defined in the Dedicated Short Range Communications (DSRC), IEEE 802.11p and Wireless Access in Vehicular Environments (WAVE) standards. The standards addresses the VANETs key issues regarding extension of coverage areas, impact of high vehicular velocities and dynamic road conditions on communications. The standards also outline the management of a multi-path phenomena whilst cooperating with multiple ad-hoc networks and providing a suitable environment for the merger of future technologies and applications with VANETs [item 1) in the Appendix].

Two-dimensional direction-of-arrival estimation of coherently distributed noncircular signals via symmetric shift invariance

In practical applications such as mobile communication, radar and sonar, the effect of angular spread on the source energy can no longer be ignored due to multipath phenomena. Therefore, a spatially distributed source model is more realistic than the point source mode in these complex cases. A lot of direction-of-arrival (DOA) estimation methods for distributed sources have been published. Whereas researches concentrated on the complex circular signal case, the noncircular property of signal can be employed to further improve the estimation performance, which has received extensive attention recently. To date, several low-complexity DOA estimation algorithms for two-dimensional (2D) coherently distributed (CD) noncircular sources have been proposed. However, all these algorithms need obtain the approximate shift invariance relationship between the sub-arrays by applying the one-order Taylor series approximation to the generalized steering vectors, which may introduce additional errors and affect the estimation accuracy. In this paper, a novel 2D DOA estimation algorithm based on the symmetric shift invariance relationship is proposed using the centro-symmetric three-dimensional (3D) linear arrays. Firstly, the extended array model is established by exploiting the noncircularity of the signal. Then, it is proved that the deterministic angular distribution function vector of the CD source has a symmetrical property for arbitrary centro-symmetric array, based on which the symmetric shift invariance relationships of extended generalized steering vectors are established in the three sub-arrays of 3D linear arrays. On the premise of such relationships, the center azimuth and elevation DOAs are obtained by the polynomial rooting method without spectral peak searching. Finally, the cost function implementing the parameter matching is constructed by the symmetric shift invariance relationship of the generalized steering vector of the whole array. Theoretical analysis and simulation experiment show that compared with the existing low-complexity algorithms, the proposed algorithm avoids the additional errors introduced by the Taylor series approximation, which allows it to achieve higher estimation accuracy with the small complexity cost. Moreover, the proposed algorithm can achieve omnidirectional angle estimation in the three-dimensional space.

Novel DV-hop Method Based on Krill Swarm Algorithm Used for Wireless Sensor Network Localization

Wireless sensor network (WSN) is self-organizing network; it consists of a large number of sensor nodes with perception, calculation ability and communication ability. As we all know, the floor, walls or people moving has an effect on indoor localization, so it will result in multi-path phenomena and decrease signal strength, also the received signal strength indicator (RSSI) is unable to gain higher accuracy of positioning. When using multilateral measurement method to calculate the unknown node coordinates, it will generate big error in range-free distance vector-hop (DV-hop) localization algorithm of WSN. In order to improve the WSN positioning accuracy in indoor condition, more reasonable distribute network resources, in this paper, we firstly propose krill swarm algorithm used for WSN localization. First, we detailed analyze the multilateral measurement method in DV-hop localization algorithm. The position problem can be transformed into a global optimization problem. Then, we adequately utilize the advantage of calculating optimization problem. We apply the krill swarm algorithm into the stage of estimating unknown node coordinates in DV-hop algorithm to realize localization. Finally, the simulation experience results show that the localization with krill swarm algorithm has an obviously higher positioning precision and accuracy stability with different anchor node proportion and nodes. We also make comparison with DV-hop algorithm and the newest localization algorithm.

Direction of arrival estimation using array of antennas for low‐altitude targets in multi‐path environment

The multi-path phenomenon can degrade the direction estimation of arrival in radar systems. Without considering this phenomenon, it is possible to consider the multi-path signal in place of the true signal. In this study, a practical model is developed for radar signals received in flat areas such as deserts. After the modelling process including the presentation of received signals and their formulation, the authors suggest the maximum likelihood (ML) decision to estimate the elevation angle of the targets. Afterwards, by considering the relevant issues, they promote the ML method over the maximum a posteriori decision and the windowing decision. This model and its estimation are very useful in practical applications, particularly in low peak power and low probability of intercept radars.

Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo.

We propose a computational model for shape, illumination and albedo inference in a pulsed time-of-flight (TOF) camera. In contrast to TOF cameras based on phase modulation, our camera enables general exposure profiles. This results in added flexibility and requires novel computational approaches. To address this challenge we propose a generative probabilistic model that accurately relates latent imaging conditions to observed camera responses. While principled, realtime inference in the model turns out to be infeasible, and we propose to employ efficient non-parametric regression trees to approximate the model outputs. As a result we are able to provide, for each pixel, at video frame rate, estimates and uncertainty for depth, effective albedo, and ambient light intensity . These results we present are state-of-the-art in depth imaging. The flexibility of our approach allows us to easily enrich our generative model. We demonstrate this by extending the original single-path model to a two-path model, capable of describing some multipath effects. The new model is seamlessly integrated in the system at no additional computational cost. Our work also addresses the important question of optimal exposure design in pulsed TOF systems. Finally, for benchmark purposes and to obtain realistic empirical priors of multipath and insights into this phenomena, we propose a physically accurate simulation of multipath phenomena.

Exploitation of Ubiquitous Wi-Fi Devices as Building Blocks for Improvised Motion Detection Systems.

This article deals with a feasibility study on the detection of human movements in indoor scenarios based on radio signal strength variations. The sensing principle exploits the fact that the human body interacts with wireless signals, introducing variations of the radiowave fields due to shadowing and multipath phenomena. As a result, human motion can be inferred from fluctuations of radiowave power collected by a receiving terminal. In this paper, we investigate the potentialities of widely available wireless communication devices in order to develop an improvised motion detection system (IMDS). Experimental tests are performed in an indoor environment by using a smartphone as a Wi-Fi access point and a laptop with dedicated software as a receiver. Simple detection strategies tailored for real-time operation are implemented to process the received signal strength measurements. The achieved results confirm the potentialities of the simple system here proposed to reliably detect human motion in operational conditions.

Virtual 3D city model as a priori information source for vehicle localization system

This paper aims at demonstrating the usefulness of integrating virtual 3D models in vehicle localization systems. Usually, vehicle localization algorithms are based on multi-sensor data fusion. Global Navigation Satellite Systems GNSS, as Global Positioning System GPS, are used to provide measurements of the geographic location. Nevertheless, GNSS solutions suffer from signal attenuation and masking, multipath phenomena and lack of visibility, especially in urban areas. That leads to degradation or even a total loss of the positioning information and then unsatisfactory performances. Dead-reckoning and inertial sensors are then often added to back up GPS in case of inaccurate or unavailable measurements or if high frequency location estimation is required. However, the dead-reckoning localization may drift in the long term due to error accumulation. To back up GPS and compensate the drift of the dead reckoning sensors based localization, two approaches integrating a virtual 3D model are proposed in registered with respect to the scene perceived by an on-board sensor. From the real/virtual scenes matching, the transformation (rotation and translation) between the real sensor and the virtual sensor (whose position and orientation are known) can be computed. These two approaches lead to determine the pose of the real sensor embedded on the vehicle. In the first approach, the considered perception sensor is a camera and in the second approach, it is a laser scanner. The first approach is based on image matching between the virtual image extracted from the 3D city model and the real image acquired by the camera. The two major parts are: 1. Detection and matching of feature points in real and virtual images (three features points are compared: Harris corner detector, SIFT and SURF). 2. Pose computation using POSIT algorithm. The second approach is based on the on–board horizontal laser scanner that provides a set of distances between it and the environment. This set of distances is matched with depth information (virtual laser scan data), provided by the virtual 3D city model. The pose estimation provided by these two approaches can be integrated in data fusion formalism. In this paper the result of the first approach is integrated in IMM UKF data fusion formalism. Experimental results obtained using real data illustrate the feasibility and the performances of the proposed approaches.

Multiple Extended Target Tracking for Through-Wall Radars

Tracking moving targets hidden behind visually opaque structures as building walls is a crucial issue in many surveillance, rescue, and security applications. The electromagnetic waves at the low microwave frequency range penetrate into common building materials and thereby enable the radar to expose behind the wall scene. However, due to complexity of the scattering scenario, the radar signal undergoes multipath propagation phenomena. These typically manifest themselves as environmental clutter which may impair detection and tracking of true targets. In this paper, a signal processing strategy is proposed to track multiple extended targets in a scene by means of a wide-band monostatic through-wall radar. The system collects data sets at regular time steps which are first processed by a microwave tomographic technique. Then, a detection/tracking stage is implemented in order to track the position and dynamics of targets in real time. An extended target-tracking approach is applied to properly exploit at the tracking stage the information related to extended nature of targets. The effectiveness of the proposed signal processing chain is assessed by numerical tests based on full-wave data pertaining to an indoor scenario.

Altitude measurement of low‐angle target in complex terrain for very high‐frequency radar

For low-angle targets, the performance of altitude measurement is affected by multipath phenomenon. Especially, for complex terrain case, the main problem is due to the fact that the multipath signal is perturbed by irregular reflector, which leads to the mismatch of the steering vector, and thus degrades the performance of or even fails the existing methods. To deal with this problem, the authors propose a new perturbational multipath signal model, where perturbation caused by complex terrain is considered as the gain and phase errors of the steering vector of the multipath signal. With the spatial sparsity of the incident signals, the sparse Bayesian learning technique is adopted to estimate the perturbation and direction of arrival (DOA) iteratively. The computer simulation results show that their algorithm is able to estimate the effect of perturbation with high precision and to enhance the DOA accuracy compared with existing algorithms. Furthermore, real data analysis validates the efficiency of altitude measurement in practice. Finally, the proposed perturbational multipath signal model is also applicable to other situations where complex multipath phenomenon is not negligible.

Duality of quantum coherence and path distinguishability

We derive a generalized wave-particle duality relation for arbitrary multipath quantum interference phenomena. Beyond the conventional notion of the wave nature of a quantum system, i.e., the interference fringe visibility, we introduce a quantifier as the normalized quantum coherence, recently defined in the framework of quantum information theory. To witness the particle nature, we quantify the path distinguishability or the which-path information based on unambiguous quantum state discrimination. Then, the Bohr complementarity principle for multipath quantum interference can be stated as a duality relation between the quantum coherence and the path distinguishability. For two-path interference, the quantum coherence is identical to the interference fringe visibility, and the relation reduces to the well-known complementarity relation. The duality relation continues to hold in the case where mixedness is introduced due to possible decoherence effects.

Modelling and Simulation of IDMA-OFDM for Underwater Acoustic Communication

<p>The Ocean exhibits phenomenon of changing acoustic signal transmission due to its non-stationary nature. Water columns in between transmitter and receiver are not fixed at any point of time. Thus, designing of a wireless communication systems for underwater applications becomes significantly challenging. The speed of sound in water is 1500 m/s which introduces large delay spread in acoustic signal due to multipath phenomenon. The large time delay causes Inter-symbol Interference; this ISI degrades the performance of many receivers. However orthogonal frequency division multiplexing is multi-tone modulation reduces long time delay spreads of acoustic channels. The interleave division multiple access distinguishes each user with the unique random interleaver code. The IDMA-OFDM is superior design for reducing error bursts in multi-access underwater applications. A Simulink based simulation modell of IDMA-OFDM system has been discussed in this paper. Satisfactory performance of the implementation was observed through analysis of BER with respect to SNR. The results have been concluded by comparing simulated data in BER tool of Simulink.</p><p><strong>Defence Science Journal, Vol. 65, No. 4, July 2015, pp. 307-311, DOI: http://dx.doi.org/10.14429/dsj.65.8606</strong></p>

A precise RFID indoor localization system with sensor network assistance

Indoor localization is very critical for medical care applications, e.g., the patient localization or tracking inside the building of the hospital. Traditional Radio Frequency Identification (RFID) technologies are very popular in this area since their cost is very low. In such technologies, each tag acts as the transmitter and the Radio Signal Strength Indicator (RSSI) information is measured from the readers. However, RSSI information suffers severely from the mult i-path phenomenon. As a result, if in a very large area, the localization accuracy will be affected seriously. In order to solve this problem, we introduce Wireless Sensor Networks (WSNs) with only a few nodes, each of which acts as both transmitter and receiver. In such networks, the change of signal strength (referred as dynamic of RSSI) is leveraged to select a cluster of reference tags as candidates. Then the final target location is estimated by using the RSSI relationships between the target tag and candidate reference tags. Thus, the localization accuracy and scalability are able to be improved. We proposed two algorithms, SA-LANDMARC, and COCKTAIL. Experiments show that the localization accuracy of the two algorithms can reach 0.7m and 0.45m, respectively. Compared to most traditional Radio Frequency (RF)-based approaches, the localization accuracy is improved at least 50%.