Showing papers on "Multipath propagation published in 2021"
TL;DR: In this paper, the authors identify the potential of the spatio-temporal control offered by reconfigurable intelligent surfaces (RISs) to boost wireless communications in rich scattering channels via two case studies.
Abstract: Recent advances in the fabrication and experimentation of reconfigurable intelligent surfaces (RISs) have motivated the concept of the smart radio environment, according to which the propagation of information-bearing waveforms in the wireless medium is amenable to programmability. Although the vast majority of recent experimental research on RIS-empowered wireless communications gravitates around narrowband beamforming in quasi-free space, RISs are foreseen to revolutionize wideband wireless connectivity in dense urban as well as indoor scenarios, which are usually characterized as strongly reverberant environments exhibiting severe multipath conditions. In this article, capitalizing on recent physics-driven experimental explorations of RIS-empowered wave propagation control in complex scattering cavities, we identify the potential of the spatio-temporal control offered by RISs to boost wireless communications in rich scattering channels via two case studies. First, an RIS is deployed to shape the multipath channel impulse response, which is shown to enable higher achievable communication rates. Second, the RIS-tunable propagation environment is leveraged as an analog multiplexer to localize non-cooperative objects using wave fingerprints, even when they are outside the line of sight. Future research challenges and opportunities in the algorithmic design and experimentation of smart rich scattering wireless environments enabled by RISs for sixth generation wireless communications are discussed.
105 citations
TL;DR: In this paper, a comparison of indoor radio propagation measurements and corresponding channel statistics at 28, 73, and 140 GHz, based on extensive measurements from 2014-2020 in an indoor office environment, is provided.
Abstract: This letter provides a comparison of indoor radio propagation measurements and corresponding channel statistics at 28, 73, and 140 GHz, based on extensive measurements from 2014–2020 in an indoor office environment. Side-by-side comparisons of propagation characteristics (e.g., large-scale path loss and multipath time dispersion) across a wide range of frequencies from the low millimeter wave band of 28 GHz to the sub-THz band of 140 GHz illustrate the key similarities and differences in indoor wireless channels. The measurements and models show remarkably similar path loss exponents over frequencies in both line-of-sight (LOS) and non-LOS (NLOS) scenarios, when using a one meter free space reference distance, while the multipath time dispersion becomes smaller at higher frequencies. The 3GPP indoor channel model overestimates the large-scale path loss and has unrealistic large numbers of clusters and multipath components per cluster compared to the measured channel statistics in this letter.
74 citations
TL;DR: Both theoretical analysis and simulation results demonstrate that RIS has the potential to realize accurate positioning with a single access point, due to its ability to mark the channel and replace traditional active positioning anchors.
Abstract: Recently, reconfigurable intelligent surface (RIS), which operates with the aim to manipulate multi-path signals, has been widely considered for wireless communications. In this letter, we extend the employment of RIS to indoor positioning, with the aid of ultra-wideband (UWB) technique. Moreover, the Cramer-Rao lower bound of the developed positioning scheme is quantified. Both theoretical analysis and simulation results demonstrate that RIS has the potential to realize accurate positioning with a single access point, due to its ability to mark the channel and replace traditional active positioning anchors. Moreover, it is also depicted that when the number of receive antennas is limited, time-of-arrival based positioning has a higher accuracy than that based on angle-of-arrival, due to the high multipath resolution of UWB signals.
61 citations
TL;DR: In this paper, the authors derived exact end-to-end SNR expressions for RIS-aided and amplify-and-forward (AF) relay systems, and proposed a novel and simple way to obtain the optimal phase shifts at the RIS elements.
Abstract: Reconfigurable Intelligent Surface (RIS) can create favorable multipath to establish strong links that are useful in millimeter wave (mmWave) communications. While previous works assumed Rayleigh or Rician fading, we use the fluctuating two-ray (FTR) distribution to model the small-scale fading in mmWave frequency. First, we obtain the statistical characterizations of the product of independent FTR random variables (RVs) and the sum of product of FTR RVs. For the RIS-aided and amplify-and-forward (AF) relay systems, we derive exact end-to-end signal-to-noise ratio (SNR) expressions. To maximize the end-to-end SNR, we propose a novel and simple way to obtain the optimal phase shifts at the RIS elements. The optimal power allocation scheme for the AF relay system is also proposed. Furthermore, we evaluate important performance metrics including the outage probability and the average bit-error probability. To validate the accuracy of our analytical results, Monte-Carlo simulations are subsequently conducted to provide interesting insights. It is found that the RIS-aided system can attain the same performance as the AF relay system with low transmit power. More interestingly, as the channel conditions improve, the RIS-aided system can outperform the AF relay system using a smaller number of reflecting elements.
56 citations
TL;DR: A software-defined receiver (SDR) to extract navigation observables from cellular 5G signals that exploits the downlink channel and the statistics of the code phase error in a multipath-free environment and in the presence of multipath are derived.
Abstract: A comprehensive approach for opportunistic navigation with fifth-generation (5G) cellular signals that exploits the downlink channel is developed. The structure of possible 5G reference signals that can be exploited is presented. Then, a software-defined receiver (SDR) to extract navigation observables from cellular 5G signals is proposed. The statistics of the code phase error in a multipath-free environment and in the presence of multipath are derived, which are subsequently used to analyze the statistics of the position estimation error with different simulated channel models. Finally, experimental results are conducted to evaluate the ranging performance of the proposed SDR with real 5G signals. After removing the effect of the clock bias and drift from the estimated pseudorange, the ranging error standard deviation is shown to be 1.19 m.
55 citations
TL;DR: A novel tensor-based method for channel estimation that allows estimation of mmWave channel parameters in a non-parametric form that is able to accurately estimate the channel, even in the absence of a specular component is presented.
Abstract: 5G mmWave communication is useful for positioning due to the geometric connection between the propagation channel and the propagation environment. Channel estimation methods can exploit the resulting sparsity to estimate parameters (delay and angles) of each propagation path, which in turn can be exploited for positioning and mapping. When paths exhibit significant spread in either angle or delay, these methods break down or lead to significant biases. We present a novel tensor-based method for channel estimation that allows estimation of mmWave channel parameters in a non-parametric form. The method is able to accurately estimate the channel, even in the absence of a specular component. This in turn enables positioning and mapping using only diffuse multipath. Simulation results are provided to demonstrate the efficacy of the proposed approach.
54 citations
TL;DR: Simulation results indicate that the proposed schemes outperform the traditional counterparts in terms of accuracy, robustness and complexity, especially for the case of low-complexity IRSs with limited number of active sensing elements.
Abstract: We consider the channel estimation problem in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems assisted by intelligent reconfigurable surfaces (IRSs). To avoid the inherent estimation ambiguities of the two-hop channels from mobile stations (MS) to the base station (BS), we adopt a hybrid IRS architecture composed of passive reflectors and active sensors, and establish two independent subproblems of estimating the MS-to-IRS and BS-to-IRS channels. By leveraging the sparse characteristics of high-frequency propagation, we model the training signals as multi-dimensional canonical polyadic decomposition (CPD) tensors with missing fibers or slices. We develop algebraic algorithms to solve the tensor completion problems and recover channel multipath parameters, i.e., angles of arrival, time delays and path gains. Our methods require neither random initialization nor iterative operations, and for these reasons they can perform robustly with a low computational complexity. Moreover, we investigate the uniqueness condition of CPD tensor completion, which can be utilized to inform both the physical design of hybrid IRSs and the time-frequency resource allocation of training strategies. Simulation results indicate that the proposed schemes outperform the traditional counterparts in terms of accuracy, robustness and complexity, especially for the case of low-complexity IRSs with limited number of active sensing elements.
46 citations
TL;DR: Simulation results indicate that the proposed twin-IRS structure and estimation schemes can recover the channel state information with remarkable accuracy, thereby offering a centimeter-level resolution of user positioning.
Abstract: We consider the channel estimation problem and the channel-based wireless applications in multiple-input multiple-output orthogonal frequency division multiplexing systems assisted by intelligent reconfigurable surfaces (IRSs). To obtain the necessary channel parameters, i.e., angles, delays and gains, for environment mapping and user localization, we propose a novel twin-IRS structure consisting of two IRS planes with a relative spatial rotation. We model the training signal from the user equipment to the base station via IRSs as a third-order canonical polyadic tensor with a maximal tensor rank equal to the number of IRS unit cells. We present four designs of IRS training coefficients, i.e., random, structured, grouping and sparse patterns, and analyze the corresponding uniqueness conditions of channel estimation. We extract the cascaded channel parameters by leveraging array signal processing and atomic norm denoising techniques. Based on the characteristics of the twin-IRS structures, we formulate a nonlinear equation system to exactly recover the multipath parameters by two efficient decoupling modes. We realize environment mapping and user localization based on the estimated channel parameters. Simulation results indicate that the proposed twin-IRS structure and estimation schemes can recover the channel state information with remarkable accuracy, thereby offering a centimeter-level resolution of user positioning.
37 citations
TL;DR: M exploits amultipath-assisted approach that turns the harmful multipath from foe to friend to enable single AP localization: a device can be pinpointed through the combination of azimuths and the relative time of flight of Line-of-Sight signals, eliminating the need for multiple APs along with their absolute ToF measurements.
Abstract: Owing to the ubiquitous penetration of Wi-Fi in our daily lives, Wi-Fi indoor localization has attracted intensive attentions in the last decade or so. Despite some significant progresses, the high accuracy of existing systems is still achieved at the cost of dense access point (AP) deployment. The more practical single AP localization is largely left as an open problem because the hardware-induced time delay “contaminates” the measurement of signal propagation time in the air. In this article, we design and implement $M^3$ M 3 to tackle this challenge with commodity Wi-Fi cards. $M^3$ M 3 exploits a multipath-assisted approach that turns the harmful multipath from foe to friend to enable single AP localization: a device can be pinpointed through the combination of azimuths and the relative time of flight (ToF) of Line-of-Sight (LoS) signal and reflection signals, eliminating the need for multiple APs along with their absolute ToF measurements. $M^3$ M 3 further utilizes frequency hopping to combine multiple channels to form a virtually wider-spectrum channel for higher ToF resolution. As a prominent feature of $M^3$ M 3 , the channels do not need to be adjacent. Comprehensive experiments demonstrate that $M^3$ M 3 outperforms the state-of-the-art systems and achieves a median localization accuracy of 71 cm in three environments with a single AP.
37 citations
TL;DR: The multipath in a cellular-based navigation framework is characterized and it is shown that the proposed RAIM-based measurement exclusion technique reduces the position root mean-squared error (RMSE) by 66%.
Abstract: A receiver autonomous integrity monitoring (RAIM) framework for ground vehicle navigation using ambient cellular signals of opportunity (SOPs) and an inertial measurement unit (IMU) is developed. The proposed framework accounts for two types of errors that compromise the integrity of the navigation solution: (i) multipath and (ii) unmodeled biases in the cellular pseudorange measurements due to line-of-sight (LOS) signal blockage and high signal attenuation. This paper, first, characterizes the multipath in a cellular-based navigation framework. Next, a fault detection and exclusion technique for a cellular-based navigation framework is developed. Simulation and experimental results with real long-term evolution (LTE) signals are presented evaluating the efficacy of the proposed RAIM-based fault detection and exclusion technique on a ground vehicle navigating in a deep urban environment in the absence of global navigation satellite system (GNSS) signals. The experimental results on a ground vehicle traversing 825 m in an urban environment show that the proposed RAIM-based measurement exclusion technique reduces the position root mean-squared error (RMSE) by 66%.
35 citations
TL;DR: This work uses a gradient boosting decision tree (GBDT)-based method to predict the pseudorange errors by considering the signal strength, satellite elevation angle and pseudorange residuals and shows results for a challenging urban environment characterized by high-rise buildings on one side.
Abstract: The accuracy of location information, mainly provided by the global positioning system (GPS) sensor, is critical for Internet-of-Things applications in smart cities. However, built environments attenuate GPS signals by reflecting or blocking them resulting in some cases multipath and non-line-of-sight (NLOS) reception. These effects cause range errors that degrade GPS positioning accuracy. Enhancements in the design of antennae and receivers deliver a level of reduction of multipath. However, NLOS signal reception and residual effects of multipath are still to be mitigated sufficiently for improvements in range errors and positioning accuracy. Recent machine learning-based methods have shown promise in improving pseudorange-based position solutions by considering multiple variables from raw GPS measurements. However, positioning accuracy is limited by low accuracy signal reception classification. Unlike the existing methods, which use machine learning to directly predict the signal reception classification, we use a gradient boosting decision tree (GBDT)-based method to predict the pseudorange errors by considering the signal strength, satellite elevation angle and pseudorange residuals. With the predicted pseudorange errors, two variations of the algorithm are proposed to improve positioning accuracy. The first corrects pseudorange errors and the other either corrects or excludes the signals determined to contain the effects of multipath and NLOS signals. The results for a challenging urban environment characterized by high-rise buildings on one side, show that the 3-D positioning accuracy of the pseudorange error correction-based positioning measured in terms of the root mean square error is 23.3 m, an improvement of more than 70% over the conventional methods.
TL;DR: In this paper, the authors describe a methodology based on accurate models of the indoor multipaths and of the radar signals, that enables separating the undesired multipaths from desired signals of multiple individuals, removing a key obstacle to real-world contactless vital signs monitoring and localization.
Abstract: Objective: Over the last two decades, radar-based contactless monitoring of vital signs (heartbeat and respiration rate) has raised increasing interest as an emerging and added value to health care. However, until now, the flaws caused by indoor multipath propagation formed a fundamental hurdle for the adoption of such technology in practical healthcare applications where reliability and robustness are crucial. Multipath reflections, originated from one person, combine with the direct signals and multipaths of other people and stationary objects, thus jeopardizing individual vital signs extraction and localization. This work focuses on tackling indoor multipath propagation. Methods: We describe a methodology, based on accurate models of the indoor multipaths and of the radar signals, that enables separating the undesired multipaths from desired signals of multiple individuals, removing a key obstacle to real-world contactless vital signs monitoring and localization. Results: We also demonstrated it by accurately measure individual heart rates, respiration rates, and absolute distances (range information) of paired volunteers in a challenging real-world office setting. Conclusion: The approach, validated using a frequency-modulated continuous wave (FMCW) radar, was shown to function in an indoor environment where radar signals are severely affected by multipath reflections. Significance: Practical applications arise for health care, assisted living, geriatric and quarantine medicine, rescue and security purposes.
01 Jun 2021
TL;DR: In this paper, a deep learning based joint source channel coding (JSCC) scheme for wireless image transmission over multipath fading channels with non-linear signal clipping was proposed, where the encoder and decoder use convolutional neural networks (CNN) and directly map the source images to complex-valued baseband samples for OFDM transmission.
Abstract: We present a deep learning based joint source channel coding (JSCC) scheme for wireless image transmission over multipath fading channels with non-linear signal clipping. The proposed encoder and decoder use convolutional neural networks (CNN) and directly map the source images to complex-valued baseband samples for orthogonal frequency division multiplexing (OFDM) transmission. The proposed model-driven machine learning approach eliminates the need for separate source and channel coding while integrating an OFDM datapath to cope with multipath fading channels. The end-to-end JSCC communication system combines trainable CNN layers with non-trainable but differentiable layers representing the multipath channel model and OFDM signal processing blocks. Our results show that injecting domain expert knowledge by incorporating OFDM baseband processing blocks into the machine learning framework significantly enhances the overall performance compared to an unstructured CNN. Our method outperforms conventional schemes that employ state-of-the-art but separate source and channel coding such as BPG and LDPC with OFDM. Moreover, our method is shown to be robust against non-linear signal clipping in OFDM for various channel conditions that do not match the model parameter used during the training.
TL;DR: This article provides a comprehensive analysis of packet scheduling in the MPTCP, not only in theory but in practice as well, and compares single-criterion and multicriteria schedulers under different bottleneck conditions and multipath congestion controls.
Abstract: Multipath transmission control protocol (MPTCP) is a transport protocol that allows the simultaneous use of multiple transmission control protocol subflows across the existing IP addresses between peers. Since each subflow undergoes the bottleneck link condition of its path, selecting the best subflow to schedule an outgoing packet plays a key role in the multipath performance. While good scheduling decisions can significantly improve throughput, wrong decisions prevent users from benefiting the aggregating capacities of available subflows. To deal with this concern, in the last years, several scheduling solutions were proposed to achieve different goals and applications. Different from other surveys and tutorials, in this article, we provide a tutorial of packet scheduling in the MPTCP that brings not only its fundamentals but also a detailed analysis of multipath performance from a consistent experimental methodology. In a multipath network setup, we compare single-criterion and multicriteria schedulers under different bottleneck conditions and multipath congestion controls. From a comprehensive experimental analysis that unveils several performance issues, we discuss the lessons learned and research opportunities regarding the multipath throughput improvement as the central motivation. In this way, this article provides a comprehensive analysis of packet scheduling in the MPTCP, not only in theory but in practice as well.
14 Jun 2021
TL;DR: In this paper, the authors present results from one of the first measurement campaigns for medium-distance (up to 35 m) outdoor channels in urban environments where both the directions and delays of multipath are measured with good resolution.
Abstract: Wireless communications in the THz frequency range can allow data rates of hundreds of Gbit/s, and will thus be an important part of 6G. A first important step for system design is an understanding of the underlying propagation channel. In this paper, we present results from one of the first measurement campaigns for medium-distance (up to 35 m) outdoor channels in urban environments where both the directions and delays of multipath are measured with good resolution. The results show a surprisingly rich multipath environment, leading to significant dispersion in both delay and angular domains. We also find that metallic-covered surfaces lead to a considerable enhancement of multipath, which indicates an important impact of building materials. Overall, the results indicate that relying on pronounced sparsity for THz system design might not always be valid in this type of environment.
TL;DR: A robust RFF extraction scheme based on three corresponding algorithms to enhance the recognition robustness through regularization and channel adaptation and a verification algorithm based on the generative Gaussian probabilistic linear discriminant analysis (GPLDA) model to handle unauthorized devices.
Abstract: Radio-frequency fingerprinting (RFF) exploiting hardware characteristics has been employed for device recognition to enhance the overall security. However, the performance unreliability in long-term experiments, channel fading interference, and unauthorized devices verification are three open problems that restrict the development of RFF recognition. To address these issues, a robust RFF extraction scheme based on three corresponding algorithms is studied. For the first problem, a long-term stacking of repetitive symbols (LSRSs) algorithm is proposed to reduce the acquired signal variance, which contributes to the identification accuracy and long-term stability. For the second issue, we propose an artificial noise adding (ANA) algorithm to enhance the recognition robustness through regularization and channel adaptation. For the third issue, a verification algorithm based on the generative Gaussian probabilistic linear discriminant analysis (GPLDA) model is developed to handle unauthorized devices. Our robust RFF extraction scheme is verified in the experiments with 54 CC2530 ZigBee devices. It enables reliable node identification with the accuracy of 99.50% in the short rang line-of-sight (SLOS) scenarios for signals collected over 18 months, and 95.52% in the extensive multipath fading experiments. The equal error rate (EER) of the verification experiments with six authorized devices versus six unseen unauthorized devices is as low as 0.63%.
TL;DR: In this paper, a passive location parameter estimator using multiple satellites for moving aerial target is proposed, in which the direct wave signals in reference channels are first filtered by a band-pass filter, followed by a sequence cancellation algorithm to suppress the direct-path interference and multihop interference.
Abstract: Estimating the location parameters of moving target is an important part of intelligent surveillance for Internet of Vehicles (IoV). Satellite has the potential to play a key role in many applications of space-air-ground integrated networks (SAGIN). In this paper, a novel passive location parameter estimator using multiple satellites for moving aerial target is proposed. In this estimator, the direct wave signals in reference channels are first filtered by a band-pass filter, followed by a sequence cancellation algorithm to suppress the direct-path interference and multi-path interference. Then, the fourth-order cyclic cumulant cross ambiguity function (FOCCCAF) of the signals in the reference channels and the four-weighted fractional Fourier transform fourth-order cyclic cumulant cross-ambiguity function (FWFRFT-FOCCCAF) of signals in the surveillance channels are derived. Using them, the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) are estimated and the distance between the target and the receiver and the velocity of the moving aerial target are estimated by using multiple satellites. Finally, the Cramer-Rao Lower Bounds of the proposed location parameter estimators are derived to benchmark the estimator. Simulation results show that the proposed method can effectively and precisely estimate the location parameters of the moving aerial target.
TL;DR: TeraMIMO as discussed by the authors is an accurate stochastic MATLAB simulator of statistical terahertz (THz) channels for single-and multi-carrier scenarios.
Abstract: Following recent advances in terahertz (THz) technology, there is a consensus on the crucial role of THz communications in the next generation of wireless systems. Aiming at catalyzing THz communications research, we propose TeraMIMO, an accurate stochastic MATLAB simulator of statistical THz channels. We simulate ultra-massive multiple-input multiple-output antenna configurations as critical infrastructure enablers that overcome the limitation in THz communications distances. We consider both line-of-sight and multipath components and propose frequency- and delay-domain implementations for single- and multi-carrier paradigms in both time-invariant and time-variant scenarios. We implement exhaustive molecular absorption computations based on radiative transfer theory alongside alternative sub-THz approximations. We further model THz-specific constraints, including wideband beam split effects, spherical wave propagation, and misalignment fading. We verify TeraMIMO by analogy with measurement-based channel models in the literature and ergodic capacity analysis. We introduce a graphical user interface and a guide for using TeraMIMO in THz channel generation and analyses.
TL;DR: In this article, a two-step stochastic hybrid estimation (TS-SHE) algorithm was proposed for robust and accurate carrier phase tracking of global navigation satellite system (GNSS) signals in urban environments with significant power degradation and fluctuation.
Abstract: This article presents a two-step stochastic hybrid estimation (TS-SHE) algorithm for robust and accurate carrier phase tracking of global navigation satellite system (GNSS) signals in urban environments with significant power degradation and fluctuation. The proposed algorithm adaptively combines a bank of parallel Kalman filters (KFs) with different dynamic state models to cope with the nonstationarity of GNSS signals. Different from conventional approaches, including the phase lock loop (PLL), the KF, and the interacting multiple-model (IMM) method, which strongly rely on the a priori fixed signal model to ensure good tracking performance, we develop a novel stochastic filter transition strategy utilizing the information from reliable signal condition evaluation to overcome the performance degradation caused by the model mismatch in urban environments. Therein, for the first time, we use the prior information of signal fading conditions for more accurate combined weighting of all component filters. Specifically, to obtain the prior information, GNSS data are sequentially buffered and analyzed in the first step. Then, in the second step, optimized filter weights are generated, and the overall combined carrier phase is estimated. We analyze the theoretical performance of the new algorithm and show the effects of different design parameters on the performance. The primary advantages of the proposed algorithm include: 1) the ability to rapidly recover carrier phase observation when blocked signals are reacquired and 2) high accuracy in carrier phase estimation of GNSS signals corrupted by strong multipath fading. Simulation and real data experiment results show the enhanced robustness and improved accuracy of the proposed TS-SHE algorithm compared with conventional carrier phase tracking methods.
TL;DR: A high-resolution delay estimation algorithm that derives the Cramér-Rao Bound (CRB) for the data model and performs numerical simulations of the algorithm using system parameters of the emerging IEEE 802.11be standard, showing that the algorithm is asymptotically efficient and converges to the CRB.
Abstract: In wireless networks, an essential step for precise range-based localization is the high-resolution estimation of multipath channel delays. The resolution of traditional delay estimation algorithms is inversely proportional to the bandwidth of the training signals used for channel probing. Considering that typical training signals have limited bandwidth, delay estimation using these algorithms often leads to poor localization performance. To mitigate these constraints, we exploit the multiband and carrier frequency switching capabilities of wireless transceivers and propose to acquire channel state information (CSI) in multiple bands spread over a large frequency aperture. The data model of the acquired measurements has a multiple shift-invariance structure, and we use this property to develop a high-resolution delay estimation algorithm. We derive the Cramer-Rao Bound (CRB) for the data model and perform numerical simulations of the algorithm using system parameters of the emerging IEEE 802.11be standard. Simulations show that the algorithm is asymptotically efficient and converges to the CRB. To validate modeling assumptions, we test the algorithm using channel measurements acquired in real indoor scenarios. From these results, it is seen that delays (ranges) estimated from multiband CSI with a total bandwidth of 320 MHz show an average RMSE of less than 0.3 ns (10 cm) in 90% of the cases.
TL;DR: The proposed reduced-complexity algorithm is very effective in (even severe) multipath conditions, outperforming natural competitors also when the number of antennas and samples is kept at the theoretical minimum, and exhibiting robustness to several types of mismatch.
TL;DR: DPI-DCSK is an efficient alternative transmission scheme for chaos-based wireless communication, such as indoor applications of transmitted-reference UWB communications and in practical ultra-wideband (UWB) channels.
Abstract: This letter proposes a differential permutation index differential chaos shift keying (DPI-DCSK) modulation for wireless communications. The conventional PI-DCSK has the disadvantage of wasting transmission energy and the loss of system performance. In $M$ -ary DPI-DCSK, one chaotic sequence to be sent represents current modulated ${M}$ -ary symbol as well as the reference of next modulation symbol in a data frame, which can fully utilize the transmission energy with the same hardware implementation as PI-DCSK. The bit error performance of the proposed DPI-DCSK over the multipath Rayleigh fading channel is theoretically analyzed. The simulation results show that the proposed DPI-DCSK achieves a performance gain of more than 3 dB over PI-DCSK, especially in high-delay multipath fading channels. The performance advantage of the proposed scheme is also illustrated in practical ultra-wideband (UWB) channels. Thus, DPI-DCSK is an efficient alternative transmission scheme for chaos-based wireless communication, such as indoor applications of transmitted-reference UWB communications.
TL;DR: A synthetic aperture navigation system that exploits downlink cellular long-term evolution (LTE) signals and an inertial measurement unit (IMU) is developed, suitable for multipath-rich environments, such as indoors and deep urban canyons.
Abstract: A synthetic aperture navigation (SAN) system that exploits downlink cellular long-term evolution (LTE) signals and an inertial measurement unit (IMU) is developed. The system is suitable for multipath-rich environments, such as indoors and deep urban canyons. The proposed SAN system mitigates multipath via a spatial discriminator, which utilizes the motion of a single antenna element to synthesize a geometrically separated antenna array from time-separated snapshots, alleviating the need for a physical antenna array. Signals from the synthesized antenna array are used to beamform towards the line-of-sight (LOS) LTE direction, while suppressing multipath components. Different stages of the beamforming process are discussed, and the computational complexity of the proposed system is analyzed. To deal with the unknown clock biases of the LTE eNodeBs, two navigation frameworks are developed: (1) base/rover and (2) standalone rover. The proposed SAN system is validated experimentally, and the navigation solutions achieved from the following systems are compared: (i) IMU only, (ii) LTE only, (iii) feedforward LTE-SAN, (iv) feedback LTE-SAN, (v) LTE-IMU, and (vi) feedback LTE-SAN-IMU. In the performed experiment, a pedestrian-mounted receiver navigated indoors for 109 m in 50 seconds, while receiving LTE signals from 5 LTE eNodeBs. The proposed LTE-SAN-IMU system exhibited a two-dimensional position root mean-squared error (RMSE) of 1.44 m with a standard deviation of 1.85 m.
TL;DR: In this paper, the performance and precision of ionospheric observables extracted from different algorithms were investigated using two validation methods, i.e., the co-location experiment by calculating the single difference for each satellite, and the single-frequency PPP (SF-PPP) test by two co-located stations; and use the short arc experiment to demonstrate the advantages of the PPP-fixed method.
Abstract: Precise extraction of ionospheric total electron content (TEC) observations with high precision is the precondition for establishing high-precision ionospheric TEC models. Nowadays, there are several ways to extract TEC observations, e.g., raw-code method (Raw-C), phase-leveled code method (PL-C), and undifferenced and uncombined precise point positioning method (UD-PPP); however, their accuracy is affected by multipath and noise. Considering the limitations of the three traditional methods, we try directly to use the phase observation based on zero-difference integer ambiguity to extract ionospheric observations, namely, PPP-Fixed method. The main goal of this work is to: 1) deduce the expression of ionospheric observables of these four extraction methods in a mathematical formula, especially the satellite and receiver hardware delays; 2) investigate the performance and precision of ionospheric observables extracted from different algorithms using two validation methods, i.e., the co-location experiment by calculating the single difference for each satellite, and the single-frequency PPP (SF-PPP) test by two co-location stations; and 3) use the short arc experiment to demonstrate the advantages of the PPP-Fixed method. The results show that single-difference mean errors of TEC extracted by PL-C, UD-PPP, and PPP-Fixed are 1.81, 0.59, and 0.15 TEC unit (TECU), respectively, and their corresponding maximum single-difference values are 5.12, 1.68, and 0.43 TECU, respectively. Compared with PL-C, the precision of the TEC observations extracted by the PPP-Fixed method is improved by 91.7%, while it is 67.3% for UD-PPP. The SF-PPP experiment shows that PPP-Fixed is the best among these methods in terms of convergence speed, correction accuracy, and reliability of positioning performance. Moreover, the PPP-Fixed method can achieve high accuracy even when the observed arc is short, e.g., within 40 min.
TL;DR: In this paper, 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, is addressed.
Abstract: 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.
TL;DR: A multipath-assisted indoor localization system using commodity WiFi signals which contain phase errors caused by imperfect hardware and non-synchronized clocks and develops a location searching algorithm of the target and scatterers based on Particle Swarm Optimization (PSO).
Abstract: Target localization using a single receiver is highly needed due to the mobility of the target. In indoor environment, multipath signals are rich and usually relate the target location and structure of indoor environment through geometry parameters such as Angle of Arrival (AOA) and Time of Flight (TOF). Based on this, in this article we propose a multipath-assisted indoor localization system using commodity WiFi signals which contain phase errors caused by imperfect hardware and non-synchronized clocks. The proposed system is different from conventional localization algorithms which treat multipath signals as enemies, and only uses a single receiver. To realize accurate localization without interferences of phase errors, we firstly construct a geometry model for jointly estimating the locations of the target and scatterers which can be regarded as objects such as furniture, by using TOF differences between reflection paths and direct path. Considering more constrains on target location, we select the direct path as the reference. Then, with the help of AOAs, we develop a location searching algorithm of the target and scatterers based on Particle Swarm Optimization (PSO). We have implemented the proposed system on the commodity WiFi devices, and the experiment results in actual indoor environment show that the median location error is around 1.5m by using only one receiver. What’s more, the intensive simulation results show that the proposed system is promising in the emerging networks such as 5G, where higher bandwidth of signal and more antennas can be used for providing accurate AOA and TOF.
TL;DR: In this article, an efficient representation learning methodology that exploits the latest advancement in deep learning and graph optimization techniques to achieve effective ranging error mitigation at the edge is proposed, where Channel Impulse Response (CIR) signals are directly exploited to extract high semantic features to estimate corrections in either NLoS or LoS conditions.
TL;DR: A receiver design by the marriage of the OTFS and a large-scale antenna array is proposed, which allows low-complexity detection and low-overhead pilot pattern design and achieves better error performance with lower receiver complexity and lower overhead compared to the existing approaches.
Abstract: Orthogonal time frequency space (OTFS) is a modulation technique that is dedicated to the high-speed mobility scenario. However, its transmission involves a two-dimensional convolution of the symbols of interest and the multipath fading channel, and it complicates the equalization. In addition to the high-complexity issue, the existing pilot pattern to estimate the unknown channel accurately requires large overhead to avoid the pilot being contaminated, which is spectrally inefficient. In this paper, we propose a receiver design by the marriage of the OTFS and a large-scale antenna array, which allows low-complexity detection and low-overhead pilot pattern design. The receiver is briefly summarized as follows. First, the received signal from each path of the multipath fading channel is identified by a high-resolution spatial matched filter beamformer facilitated by a large-scale antenna array. Then, the derivation shows that the received signal from an angle of arrival can be regarded as a flat-faded signal with rotations in both the delay and the Doppler coordinates. In addition, the identified signal from each beamforming branch in the delay-Doppler domain can be simply equalized using the channel information estimated by our pilot pattern. We further provide the estimator of the channel fading and the rotations of delay and Doppler in each identified branch. With these estimates, the delay and the Doppler shift in each identified branch can be compensated, and then, the signals from all angles of arrival are combined as different diversity versions. Furthermore, the equalization complexity is reduced to 5.0% of the message passing detection and our pilot pattern with only around 25% overhead of the existing pilot pattern ensures the same protection of the pilot. Moreover, the carrier frequency offset is considered and be compensated. The significance of the proposed receiver is its practicality, and it achieves better error performance with lower receiver complexity and lower overhead compared to the existing approaches, at the cost of a linear beamforming antenna array. The price is quite affordable, since the linear antenna array has moderate computational complexity, and it is deployed widely in current and future wireless communication systems. Eventually, the efficiency, the reliability, and the low complicacy of the proposed receiver approach are further validated by the numerical results.
TL;DR: The design and implementation of WiNar is presented, a WiFi RTT-based indoor location determination system that combines the advantages of both fingerprint and ranging-based techniques to overcome the different challenges of indoor environments and is also robust to heterogeneous devices.
TL;DR: This letter proposed a 5G positioning and mapping algorithm with unknown orientation and clock bias for single-bounce diffuse multipath channel models, able to accurately localize, calibrate and synchronize the UE, even in the absence of line-of-sight and specular components.
Abstract: 5G mmWave communication systems have the potential to jointly estimate the positions of user equipment (UE) and mapping their propagation environments using a single base station. But such potential depends on the characteristics of the reflecting surfaces, such as a deterministic specular nature, a stochastic diffuse/scattering nature, or a combination of both. In this letter, we proposed a 5G positioning and mapping algorithm with unknown orientation and clock bias for single-bounce diffuse multipath channel models. The method is able to accurately localize, calibrate and synchronize the UE, even in the absence of line-of-sight and specular components. This enables robust positioning and mapping using only diffuse multipath.