Multipath-based simultaneous localization and mapping (MP-SLAM) is a well established approach to obtain position information of transmitters and receivers as well as information regarding the propagation environments in future multiple input multiple output (MIMO) communication systems. Conventional methods for MP-SLAM consider specular reflections of the radio signals occurring at smooth, flat surfaces, which are modeled by virtual anchors (VAs) that are mirror images of the physical anchors (PAs), with each VA generating a single multipath component (MPC). However, non-ideal reflective surfaces (such as walls covered by shelves or cupboards) cause dispersion effects that violate the VA model and lead to multiple MPCs that are associated to a single VA. In this paper, we introduce a Bayesian particle-based sum-product algorithm (SPA) for MP-SLAM in MIMO communications systems. Our method considers non-ideal reflective surfaces by jointly estimating the parameters of individual dispersion models for each detected surface in delay and angle domain leveraging multiple-measurement-to-feature data association. We demonstrate that the proposed SLAM method can robustly and jointly estimate the positions and dispersion extents of ideal and non-ideal reflective surfaces using numerical simulation.

Fingerprint-based localization methods are regarded as a promising solution in sixth-generation (6G) wireless communication because of their ubiquitous infrastructure and high precision in lab-level experiments. However, received signal strength (RSS) instability and fingerprint spatial ambiguity (FSA) significantly undermine the precision of localization methods under real-world applications. Signal spatial difference (SSD) is one of the approaches which can effectively address RSS instability and FSA in fingerprint localization. However, few of these approaches are effectively incorporated into localization methods. In this paper, we propose an SSD-based advantage actor-critic (A2C) method, SSD-A2C, for 3D indoor localization. This method is the first to combine SSD and reinforcement learning (RL), achieving a highly efficient method that can address FSA and RSS instability. A 3D indoor localization simulation environment is developed based on ray-tracing (RT) simulation results of two real-world indoor scenarios, and the proposed method is trained and evaluated by it. Experiment results proved the excellent performances of SSD-A2C in minor localization errors and a high success rate of predicting the desired location. SSD is found to be more appropriate than RSS for RL. The strengths, shortcomings, and future research directions of the proposed method are also discussed in this paper.

In this paper, we present an iterative algorithm that detects and estimates the specular components (SCs) and estimates the dense component (DC) of single-input—multiple-output (SIMO) ultra-wide-band (UWB) multipath channels. Specifically, the algorithm super-resolves the SCs in the delay–angle-of-arrival domain and estimates the parameters of a parametric model of the delay-angle power spectrum characterizing the DC. Channel noise is also estimated. In essence, the algorithm solves the problem of estimating spectral lines (the SCs) in colored noise (generated by the DC and channel noise). Its design is inspired by the sparse Bayesian learning (SBL) framework. As a result the iteration process contains a threshold condition that determines whether a candidate SC shall be retained or pruned. By relying to results from extreme-value analysis the threshold of this condition is suitably adapted to ensure a prescribed probability of detecting spurious SCs. Studies using synthetic and real channel measurement data demonstrate the virtues of the algorithm: it is able to still detect and accurately estimate SCs, even when their separation in delay and angle is down to half the Rayleigh resolution limit (RRL) of the equipment; it is robust in the sense that it tends to return no more SCs than the actual ones. Finally, the algorithm is demonstrated to outperform a state-of-the-art super-resolution channel estimator in terms of robustness in the estimation of the amplitudes of specular components closely spaced in the dispersion domain.

We present a factor graph formulation and particle-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The proposed sequential algorithm jointly estimates the mobile agent’s position together with a time-varying number of multipath components (MPCs). The MPCs are represented by “delay biases” corresponding to the offset between line-of-sight (LOS) component delay and the respective delays of all detectable MPCs. The delay biases of the MPCs capture the geometric features of the propagation environment with respect to the mobile agent. Therefore, they can provide position-related information contained in the MPCs without explicitly building a map of the environment. We demonstrate that the position-related information enables the algorithm to provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations. Using simulated and real measurements in different scenarios we demonstrate that the proposed algorithm significantly outperforms state-of-the-art multipath-aided tracking algorithms and show that the performance of our algorithm constantly attains the posterior Cramér-Rao lower bound (P-CRLB). Furthermore, we demonstrate the implicit capability of the proposed method to identify unreliable measurements and, thus, to mitigate lost tracks.

This paper presents a factor graph formulation and particle-based sum-product algorithm (SPA) for robust sequential localization in multipath-prone environments. The proposed algorithm jointly performs data association, sequential estimation of a mobile agent position, and adapts all relevant model parameters. We derive a novel non-uniform false alarm (FA) model that captures the delay and amplitude statistics of the multipath radio channel. This model enables the algorithm to indirectly exploit position-related information contained in the multipath components (MPCs) for the estimation of the agent position without using any prior information such as floorplan information or training data. Using simulated and real measurements in different channel conditions, we demonstrate that the algorithm can provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations and show that the performance of our algorithm constantly attains the posterior Cramér-Rao lower bound (P-CRLB), facilitating the additional information contained in the presented FA model. The algorithm is shown to provide robust estimates in both, dense multipath channels as well as channels showing specular, resolved MPCs, significantly outperforming state-of-the-art radio-based localization methods.

We present a variational message passing (VMP)-based approach to detect the presence of a person based on their respiratory chest motion using multistatic ultra-wideband (UWB) radar. In the process, the respiratory motion is estimated for contact-free vital sign monitoring. The received signal is modeled as a backscatter channel and the respiratory motion and propagation channels are estimated using VMP. We use the evidence lower bound (ELBO) to approximate the model evidence for the detection. Numerical analyses and measurements demonstrate that the proposed method leads to a significant improvement in the detection performance compared to a fast Fourier transform (FFT)-based detector or an estimator-correlator in low-signal-to-noise ratio (SNR) conditions, since the multipath components (MPCs) are better incorporated into the detection procedure. Specifically, the proposed method has a detection probability of 0.95 at −20dB SNR, while the estimator-correlator and FFT-based detector have 0.32 and 0.05, respectively.

Cooperative localization is an enabling technology for the IoT that will introduce innovative services for modern convenience and public safety. Particle-based belief propagation (BP) is a state-of-the-art method for cooperative localization. However, in large and dense cooperative localization networks, particle-based BP suffers from particle degeneracy. In this paper, we propose a new method that combines particle-based BP and particle flow (PF) and can avoid this detrimental effect. To perform operations on the graph effectively, particles are moved towards regions of high likelihood based on the solution of a partial differential equation. We show that the proposed PF-BP algorithm can significantly outperform conventional particle-based BP in accuracy and runtime.

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.

We present an approach for indoor localization of radio nodes using commercial off-the-shelf (COTS), low-cost, low-power, transceiver chips, found in many internet of things (IoT) applications. As such chips are typically narrowband by design, suitable localization schemes are narrowband as well, e.g., based on received signal strength indication (RSSI) or angle of arrival (AoA) measurements. Naturally, these techniques are prone to multipath fading in indoor scenarios. To circumvent this, we show that for a typical transceiver chip it is possible to generate reproducible frequency-chirped wideband signals with deterministic behavior, suitable for time of flight (ToF) localization. Despite the simplicity of the deployed radio, it is possible to predict the phase of this waveform in order to use a signal model for filtering at the receiver side. In an exemplary implementation based on a Texas Instruments CC2510 chip, we achieve an overall signal bandwidth of up to 80 MHz, centered at 2.44 GHz. With a synchronized receiver infrastructure in a simple scenario, an absolute localization error below 1 m in 90% of the cases can be reached even with a fast, sub-optimal time difference of arrival (TDoA) multilateration algorithm.

This paper proposes a channel model for ultra-wideband (UWB) off-body radio signals considering the human body in close proximity to a radio transceiver as an extended object (EO). The EO representing the human body adds scattered signal paths to the multipath components commonly encountered in indoor environments and accounts for attenuation of specular components by the human body. The purpose of this work is to describe the impact of such multipath effects, in order to develop robust algorithms for ranging and positioning in these environments. For a qualitative validation, we compare synthetically generated channel realizations based on our proposed off-body channel model with off-body measurements acquired in an indoor environment. To evaluate the human body effects on UWB range estimation, we compare the results obtained with a maximum likelihood (ML) multipath parameter estimator applied to channel measurements with synthetically generated channel realization, showing good correspondence.

We present a simultaneous localization and mapping (SLAM) algorithm that is based on radio signals and the association of specular multipath components (MPCs) with geometric features. Especially in indoor scenarios, robust localization from radio signals is challenging due to diffuse multipath propagation, unknown MPC-feature association, and limited visibility of features. In our approach, specular reflections at flat surfaces are described in terms of virtual anchors (VAs) that are mirror images of the physical anchors (PAs). The positions of these VAs and possibly also of the PAs are unknown. We develop a Bayesian model of the SLAM problem and represent it by a factor graph, which enables the use of belief propagation (BP) for efficient marginalization of the joint posterior distribution. The resulting BP-based SLAM algorithm detects the VAs associated with the PAs and estimates jointly the time-varying position of the mobile agent and the positions of the VAs and possibly also of the PAs, thereby leveraging the MPCs in the radio signal for improved accuracy and robustness of agent localization. The algorithm has a low computational complexity and scales well in all relevant system parameters. Experimental results using both synthetic measurements and real ultra-wideband radio signals demonstrate the excellent performance of the algorithm in challenging indoor environments.

We propose a search-free beamspace tensor-ESPRIT algorithm for millimeter wave MIMO channel estimation. It is a multidimensional generalization of beamspace-ESPRIT method by exploiting the multiple invariance structure of the measurements. Geometry-based channel model is considered to contain the channel sparsity feature. In our framework, an alternating least squares problem is solved for low rank tensor decomposition and the multidimensional parameters are automatically associated. The performance of the proposed algorithm is evaluated by considering different transformation schemes.

Multipath-assisted positioning makes use of specular multipath components (MPCs), whose parameters are geometrically related to the positions of the transceiver nodes. Diffuse scattering from rough surfaces affects the observed specular reflections in the angular and delay domains. Based on the effective roughness approach, the angular delay power spectrum can be calculated as a function of location parameters, which-in a next step-could be useful to accurately characterize the position-related information of MPCs. The calculated power spectra follow reported characteristics of stochastic multipath models, i.e. Gaussian shape in the angular domain and an exponential shape in the delay domain. The resulting angular and delay spreads are in an equivalent range to values reported in literature.

Setting up indoor localization systems is often excessively time-consuming and labor-intensive, because of the high amount of anchors to be carefully deployed or the burdensome collection of fingerprints. In this paper, we present SALMA, a novel low-cost UWB-based indoor localization system that makes use of only one anchor and that does neither require prior calibration nor training. By using only a crude floor plan and by exploiting multipath reflections, SALMA can accurately determine the position of a mobile tag using a single anchor, hence minimizing the infrastructure costs, as well as the setup time. We implement SALMA on off-the-shelf UWB devices based on the Decawave DW1000 transceiver and show that, by making use of multiple directional antennas, SALMA can also resolve ambiguities due to overlapping multipath components. An experimental evaluation in an office environment with clear line-of-sight has shown that 90% of the position estimates obtained using SALMA exhibit less than 20 cm error, with a median below 8 cm. We further study the performance of SALMA in the presence of obstructed line-of-sight conditions, moving objects and furniture, as well as in highly dynamic environments with several people moving around, showing that the system can sustain decimeter-level accuracy with a worst-case average error below 34 cm.

Accurate indoor radio positioning requires high-resolution measurements to either utilize or mitigate the impact of multipath propagation. This high resolution can be achieved using large signal-bandwidth, leading to superior time resolution and / or multiple antennas, leading to additional angle resolution. To facilitate multiple antennas, phase-coherent measurements are typically necessary. In this work we propose to employ non-phase-coherent measurements obtained from directional antennas for accurate single-anchor indoor positioning. The derived algorithm exploits beam patterns to jointly estimate multipath amplitudes to be used in maximum likelihood position estimation. Our evaluations based on measured and computer generated data demonstrate only a minor degradation in comparison to a phase-coherent processing scheme.

Highly accurate location information is a key facilitator to stimulate future services for the commercial and public sectors. Positioning and tracking of absolute positions of wireless nodes usually requires information provided from technical infrastructure, e.g., satellites or fixed anchor nodes, whose maintenance is costly and whose limited operating coverage narrows the positioning service. In this paper, we present an algorithm aimed at the tracking of absolute positions without using information from fixed anchors, odometers, or inertial measurement units. We perform radio channel measurements, in order to exploit position-related information contained in multipath components (MPCs). Tracking of the absolute node positions is enabled by the estimation of MPC parameters followed by the association of these parameters to a floorplan. To account for uncertainties in the floorplan and for propagation effects like diffraction and penetration, we recursively update the provided floorplan using the measured MPC parameters. We demonstrate the ability to localize two agent nodes without the employment of further infrastructure, using data from ultra-wideband channel measurements. Furthermore, we show the potential performance gain if also one fixed anchor is available, and we validate the algorithm for a range of different signal bandwidths and a number of nodes.

In this demo, we present a low-cost indoor localization system based on the off-the-shelf ultra-wideband transceiver Decawave DW1000. To obtain an accurate position information, the system makes use of a single anchor and of multipath reflections from walls, hence removing the need of installing a network of anchors or any other additional infrastructure. The procedure of determining the position of a tag can be divided in four consecutive stages. First, the location of virtual anchors is computed by mirroring the anchor position at reflective surfaces. Using two-way ranging, the distance and channel impulse response (CIR) between anchor and tag is obtained. This actual CIR is compared with expected CIRs from possible tag locations using a maximum likelihood approach to estimate the tag's position. Finally, a switchable directional antenna can be exploited to improve the robustness of the system by suppressing undesired, interfering multipath components. By following this procedure, the proposed system can achieve a decimeter accuracy and react to position updates in real-time.

Location awareness is a key factor for a wealth of wireless indoor applications. Its provision requires the careful fusion of diverse information sources. For agents that use radio signals for localization, this information may either come from signal transmissions with respect to fixed anchors, from cooperative transmissions between agents, or from radar-like monostatic transmissions. Using a priori knowledge of a floor plan of the environment, specular multipath components can be exploited, based on a geometric-stochastic channel model. In this paper, a unified framework is presented for the quantification of this type of position-related information, using the concept of equivalent Fisher information. We derive analytical results for the Cramér-Rao lower bound of multipath-assisted positioning, considering bistatic transmissions between agents and fixed anchors, monostatic transmissions from agents, cooperative measurements between agents, and combinations thereof, including the effect of clock offsets. Awareness of this information enables highly accurate and robust indoor positioning. Computational results show the applicability of the framework for the characterization of the localization capabilities of a given environment, quantifying the influence of different system setups, signal parameters, and the impact of path overlap.

Sub-band divided ray tracing (RT) has been widely used to reproduce as reliably as possible the ultra-wideband (UWB) radio wave propagation channel in realistic indoor environments. However, its accuracy is strictly limited by the available description of the environment. Moreover, its computational complexity scales with the number of selected subbands and the number of propagation paths. In the present work, our RT tool considers not only deterministic propagation paths but also diffuse scattering components. Based on a low-complexity sub-band divided RT implementation, we propose a calibration method for indoor UWB sub-band divided RT. The method estimates the optimal material parameters, including the dielectric parameters and the scattering parameters, using channel measurements and multiobjective simulated annealing (MOSA). This calibration can improve the accuracy of sub-band divided RT in terms of the power delay profile (PDP) and the root mean square (RMS) delay spread for all test locations including those not considered by the calibration. A measurement campaign is used to verify the calibration technique.

Ray tracing has been extensively used to simulate indoor channel characteristics. For an ultra-wideband system, the channel characteristics vary significantly over the entire bandwidth. To cope with this, sub-band divided RT has been proposed by dividing the frequency of interest into multiple subbands and superposing the RT results at the individual center frequency of each subband. Thus, the computational complexity is directly proportional to the number of subbands. In this paper, we propose a mathematical method to significantly reduce the computational complexity of the sub-band divided RT, making it almost independent of the number of subbands. It is important to note that, based on our approach, not only the determination of the rays reaching a give location is made only once, but also the electromagnetic calculation of the received signal is not needed to perform repeatedly. The accuracy of low-complexity subband divided RT algorithm is verified through a measurement campaign.

The MINT (multipath-assisted indoor navigation and tracking) problem exploits the geometry of deterministic multipath components (MPCs) for robust indoor positioning in line-of-sight (LOS) and non-LOS situations. It assumes a known room layout and can thus easily make use of signals reflected by the walls, for instance. In this paper, the Cramer-Rao lower bound of the positioning error is derived for this problem. This requires a novel channel model, where diffuse multipath is modeled as a colored Gaussian process that influences the effective SNR of deterministic MPCs. The adverse effect of path overlap is demonstrated and discussed. Computational results show the three-fold importance of a large signal bandwidth. The bandwidth reciprocal (i.e. the pulse duration) multiplies the error standard deviation - a fundamental result well-known from AWGN channels. But it also multiplies the effective power of the interfering diffuse multipath and gives rise to additional path overlap. A minimum bandwidth of 1 GHz seems appropriate and sufficient.

Noncoherent UWB receivers promise low power consumption and low processing complexity but their peak data rate is limited by the delay spread of the multipath radio channel. A recently proposed multichannel autocorrelation receiver (AcR) can break this rate limit because it can demodulate multicarrier signals. The hardware implementation of this receiver architecture is addressed in this paper. We focus on the integration device, which is part of the AcR and essential to suppress nonlinear cross-products between data carriers. Passive filter structures are analyzed for this purpose. It is shown that the optimized filter outperforms the reference system using an ideal sliding window integrator because it is able to mitigate more system noise.

To obtain good position accuracy with state-of-the-art indoor localization algorithms, multiple anchors must be within radio range of the user. However, anchor placement and maintenance is expensive. By reducing the number of anchors per room, overall costs are reduced. In [1], an algorithm that can estimate the position of a user using only one anchor and a priori floor plan information. However, performance for static localization was poor due to the presence of ambiguities. This paper describes a cooperative positioning algorithm that utilizes a single anchor and provides both the position and the position uncertainty for multiple users, by letting the users exchange their position information. The problem is represented with a factor graph, and belief propagation (BP) is used to extract the positions and their accuracy. The proposed cooperative algorithm leads to a significant improvement of the positioning accuracy compared to the non-cooperative method from [1].

We propose a Monte Carlo method for determining the parameters of multipath components (MPCs) for ultra-wideband channels. A partially collapsed Gibbs sampler is used for jointly estimating the number, times-of-arrival, angles-of-arrival, and amplitudes of the MPCs as well as the sounding pulse from signals received by a 2D antenna array. Our system model accounts for propagation delays between the receive antennas. Temporal-angular sparsity of the detected MPCs is ensured by a 2D minimum distance constraint. Numerical results for synthetic and real signals demonstrate the excel lent performance and fast convergence of our method.

Realistic simulation of Ultra-Wideband (UWB) positioning and tracking is a tough and challenging task. Lots of parameters have a significant impact on the final performance of the positioning system, such as parameters of the transmitted waveform, radio regulations, channel, receiver, ranging-, positioning-, and tracking-algorithm, and finally the geometric setup. A realistic simulation framework is needed to develop and optimize UWB methods and algorithms. This work proposes a novel framework for realistic UWB positioning simulations. All stages of influence have been modelled carefully. While ray-tracing simulators focus on a given user defined scenario, our approach uses statistically defined environments. Random processes are used to select channel impulse responses from a measurement database, according to an algorithm that introduces realistic large and small-scale variability with space. In particular, line-of-sight (LOS) and non-LOS (NLOS) channels are used at a defined ratio. In this work the application of the simulation framework is demonstrated to compare the performance of a least-squares (LS) positioning algorithm to a standard and an extended Kalman filter (SKF and EKF) tracking algorithm in environments with a large fraction of NLOS range measurements. The IEEE 802.15.4a standard is analyzed with respect to its positioning performance. It is shown that the localization accuracy and reliability can be improved significantly with the tracking algorithms, but their performance drop significantly with increasing probability of NLOS links.

We present a novel UWB indoor localization concept that performs the position estimation with a set of virtual anchor nodes, generated from a single physical anchor and floor plan information. Using range estimates to the virtual anchors, we perform multilateration to estimate the position of an agent. Previous work has shown the general applicability of this concept. In this contribution, we use a moving agent to exploit the correlation in successive positions using state-space concepts. A motion model for the agent and the measurement likelihood function allow for the use of the powerful framework of Bayesian state estimation. With this concept, we can propagate prior information on the agent position from one time step to the next. The statistical model for the ranging to the virtual anchors accounts for several imperfections, which lead to multimodal and heavy-tailed measurement distributions. We show how modified versions of the Kalman filter as well as a particle filter can account for these imperfections and yield accurate and robust position estimates. In a typical indoor pedestrian motion scenario, we can achieve an accuracy of about 45 cm for 90% of the estimates.

Impulse-radio ultra-wideband (IR-UWB) radios are slated to be the next generation of transceivers that can support power-constrained applications such as wireless sensor and body area networks. However, despite inherent advantages of UWB transmission due its large signal bandwidth, adoption of IR-UWB radios is hampered by relatively complex receiver structures. In this overview paper, we will first provide a brief description of the challenges for IR-UWB receiver design. Then, we will discuss two recently presented promising UWB receiver concepts, which are based on compressed sensing and multichannel autocorrelation, respectively, and which are apt to overcome current limitations.

We present a detailed analysis of a previously introduced multichannel autocorrelation receiver (MC-AcR) for multichannel ultra-wideband (UWB) signals. Performance measures are introduced and discussed to provide insight in the problem invertibility for this receiver, the capability to separate the multichannel UWB signal. The results can be used for understanding the impact of hardware imperfections and for the optimization of signal parameters.

Transmitted reference Ultra-wideband (TR-UWB) system in conjunction with autocorrelation receivers (AcR) is a popular low cost and low complexity radio system for low and mid-data rate applications. The scheme is in general robust against multipath fading. At the receiver front-end, however, both the desired UWB signal and narrowband interference (NBI) can enter to the radio receiver in which case several co-terms and cross-terms are produced after correlation. Previous studies provided an approximate data model of TR-UWB under NBI. However, analytically computed covariance matrix based on the approximate data model showed inferior performance to that of the estimated covariance matrix when used to compute bit error rate (BER) by using linear minimum mean squared error (MMSE) method. In this paper we provide an improved data model taking into account some of the neglected factors in the previous data model derivation. Finally, we show that the analytic covariance matrix derived from the improved data model achieves a closely matching BER with that achieved by the estimated covariance matrix.

Real time locating systems (RTLS) and wireless sensor networks are currently a hot and challenging research topic. Ultra wideband (UWB) shows promising properties for these systems, such as low-power transmitter designs and highly accurate ranging, where cm-level accuracy is achievable even in multipath intensive environments. A high-performance reference receiver architecture based on coherent processing is presented in this paper. It is used to define quality metrics of the received signals, as for example peak signal to noise ratio (PSNR) and line-of-sight signal to noise ratio (LSNR) for ranging, and Eb/N0 for communication. These metrics are used to evaluate the experimental data in their quality and their ranging performance. Furthermore, performance trade-offs w.r.t. system parameter and receiver architecture choices, in particular for non-coherent receivers, can be analyzed. In this work, the reference receiver is verified experimentally using IEEE 802.15.4a compliant signals. The ranging performance is correlated to the signal-to-noise ratios for an indoor line-of-sight measurement campaign.

In this contribution we design and analyze pulse shaping filters for use in IEEE 802.15.4a transmitters. They are developed and evaluated for channels 1-4 in the low band. We analyze the minimum requirements for IEEE 802.15.4a standard compliance and design passive filters which shape a given input pulse stream at baseband. Further, we simulate these filters by using S-parameter models of available components and optimize the resulting designs for standard compliance of the baseband transmit pulse with an iterative method. Finally, we compare the simulations with measurement results and evaluate our design by means of an IEEE 802.15.4a transmitter application.

Non-coherent UWB receivers provide robustness against multi path fading at low complexity. The analog front-end of these receivers, however, is susceptible to narrowband interference (NBI), thus requiring NBI mitigation capability. NBI identification and NBI parameter estimation is the first step towards implementing effective NBI mitigation. We propose an NBI detection and its center frequency identification method for multichannel autocorrelation receivers (AcR) as used by transmitted-reference (TR) UWB systems. We evaluate the performance of the method by computer simulations.

One of the unique characteristics of ultra-wideband channels is the clustering phenomenon resolved by the ultra-wide signal bandwidth. Channel structures extended from the Saleh-Valenzuela model, e.g., the IEEE 802.15.3a and IEEE 802.15.4a models, have been proposed to describe such phenomenon. It is, however, noticed that a clear and systematic procedure to estimate the model parameters is still missing. Based on the recently developed analytical results, a new estimation approach is proposed in the paper to resolve the inconvenience and ambiguity in previous methods. The key step is to match the statistical profiles of interests by formulating an optimization problem with an explicit analytical objective function that can be systematically solved. Numerical tests have also been conducted to demonstrate the effectiveness of the proposed method.

This paper shows the application of a recently proposed statistical characterization of the ultra-wideband (UWB) radio channel to the standardized IEEE 802.15.4a UWB physical layer (PHY). The statistical channel characterization yields the first and second-order moments of the autocorrelation function of the received UWB pulses, which can accurately express the intra-burst interference due to multipath propagation. The presented mathematical framework directly shows the influence of channel and system parameters, like bandwidth and delay spread, on the performance of a coherent receiver. The results show for a non-line-of-sight indoor channel, e.g. a reduction of the fading standard deviation from 1.3 to 0.8 dB when the bandwidth is increased from 0.5 to 1.3 GHz and a power loss of about 3 dB when the energy-accumlation interval is reduced from 50 to 10 ns.

Transmitted Reference (TR) UWB using an autocorrelation receiver (AcR) provides a simple architecture and yet robust functionality against multipath propagation. Narrowband interference (NBI), however, can easily jam and saturate the AcR front-end and degrade the performance severely. To cope with this problem, we propose a front-end NBI cancellation technique followed by a linear combining stage. The two-stage mitigation scheme allows the receiver to cope with low SIR scenarios. It provides superior performance than what can be achieved with either of the techniques alone. Computer simulation is conducted in the presence of an IEEE802.11a WLAN service with a random center frequency in the ranges of 5.15-5.875 GHz.

The hybrid matched-filter (HMF) receiver for transmitted reference (TR) ultra wideband (UWB) systems suffers substantial performance degradation in presence of noise and multi-access interference (MAI). In this paper, we present multiuser detection (MUD) techniques for the HMF-TR UWB receiver, derived by introducing a multiuser equivalent system model. We also show a modified pre-combining minimum mean-square error (MMSE) solution for the HMF TR UWB receiver that is novel, due to the non-linear behavior of the autocorrelation operation. The proposed detectors are shown to yield improved performance over multi-channel autocorrelation receiver (MC- AcR) TR UWB receivers.

This paper describes the application of an ultra wideband (UWB) communication and positioning system for constructing a 3D map of the environment. Basis for the mapping functionality are the device's ability to measure the impulse response and the system's ability to determine the position of the device. A systematic analysis of the problem structure is provided and key issues are identified. The proposed solution is partly implemented, and as far as possible verified in a simple experimental setup. Experiments show on the one hand the feasibility of such a system, i.e. that walls and various objects can be detected. On the other hand, the experiments also clarify which issues have to be examined in more detail

Timing acquisition constitutes a major challenge in realizing highly efficient ultra-wideband (UWB) systems for applications such as indoor wireless communications. To bypass channel estimation the transmitted reference (TR) signaling is a promising candidate. In this paper, we suggest a novel data-aided timing acquisition technique for frame-level synchronization in differential transmitted reference (DTR) UWB systems. Being based on parallel integrate-and-dump (I&D) operations within different pulse-pair correlator branches, the proposed timing acquisition technique performs particularly well at higher signal-to-noise ratio (SNR). The good performance of it is confirmed by simulation results

In this work, effects of the anchor nodes' geometry on the positioning error are evaluated for a least squares estimation (LSE) of the linearized multilateration problem. It is shown that the linearized solution can produce an unbiased average position estimate, if all involved range estimates are biased (NLOS). A theoretical discussion contains the requirements on the reference topology and range error biases in order to obtain an unbiased solution. Several positioning scenarios are simulated in which such a behaviour occurs to verify the results of the theoretical discussion. In the final part of the work the properties of the range bias in the NLOS scenario are analyzed for measured UWB channels.

When operated at high symbol rates, non-coherent UWB systems will experience inter-symbol-interference, which will be non-linear by nature and can be modeled using a FIR Volterra system. Despite the non-linearity of the ISI, it can still be equalized using linear weighting. In this paper, modulation and its statistical properties are found to influence the amount of ISI that can be suppressed. An intuitive explanation will be provided for the impact of modulation on the performance of linear weighting for non-linear TR channels. The theory is validated by means of simulation

The autocorrelation receiver (AcR) is a suboptimum, low-complexity receiver architecture particularly suited to ultra-wideband (UWB) communication systems. As the bit rate increases, interference among pulses due to multipath propagation causes serious impairments of the AcR's performance. To mitigate this effect, we propose an appropriate design of the chip code and of the delay hopping (DH) code. We provide conditions to be satisfied by the DH code in order to reduce the nonlinear intersymbol interference (ISI) and the bias term, which are peculiar nuisance parameters of autocorrelation receivers. By extending the length of the chip code, we show that N/sub p/ transmitted pulses per symbol can be employed to suppress the average linear ISI of N/sub p/-1 previous symbols. Simulated results confirm the performance improvement in terms of bit-error rate. However, in previous work it has been shown that the noise power linearly increases with N/sub p/. Although a large number of pulses per symbol is favorable for ISI mitigation, we show that the transmission of a single pulse minimizes the probability of error, for bit rates lower than an upper bound depending on the channel root mean-square delay spread and on the noise power.

The performance of a conventional threshold detector for a frame differential autocorrelation receiver in IR UWB systems in a dense multipath channel are improved by means of appropriate chip code and delay hopping code design. We point out which conditions must be satisfied by the delay hopping code in order to reduce the nonlinear ISI and the bias term, which are peculiar nuisance parameters of autocorrelation receivers. Moreover, by extending the periodicity of the chip code, we show how to suppress linear ISI. Simulated results confirm the performance improvement in terms of BER. An analytical expression relating the noise power to the system parameters is also presented. These results can be extended to the well known transmitted reference systems.

A discrete-time equivalent system model is derived for differential and transmitted reference (TR) ultra-wideband (UWB) impulse radio (IR) systems, operating under heavy intersymbol-interference (ISI) caused by multipath propagation. In the systems discussed, data is transmitted using differential modulation on a frame-level, i.e., among UWB pulses. Multiple pulses (frames) are used to convey a single bit. Time hopping and amplitude codes are applied for multi user communications, employing a receiver front-end that consists of a bank of pulse-pair correlators. It is shown that these UWB systems are accurately modeled by second-order discrete-time Volterra systems. This proposed nonlinear equivalent system model is the basis for developing optimal and suboptimal receivers for differential UWB communications systems under ISI. As an example, we describe a maximum likelihood sequence detector with decision feedback, to be applied at the output of the receiver front-end sampled at symbol rate, and an adaptive inverse modeling equalizer. Both methods significantly increase the robustness in presence of multipath interference at tractable complexity.

A discrete-time equivalent system model is derived for the intersymbol interference in a differential ultra wideband (UWB) impulse radio (IR) system operating in a multipath propagation environment. Data is transmitted using differential binary modulation on a frame-level (i.e., among UWB pulses), which is a variant of the well-known transmitted reference UWB schemes, proposed for higher data rates. It is shown that the UWB system is accurately modeled as a second-order discrete time Volterra system. Equations are presented to determine the coefficients of this non-linear model. The impact of noise is characterized. The proposed model can be used to develop signal processing algorithms for UWB-receivers in order to enhance the data rate or multiuser capabilities, for instance.

This paper investigates the possibility of using pre-equalization on the up-link of a time division duplex (TDD) OFDM system. The application of this principle is suggested to enhance the spectral efficiency by omitting the usage of training sequences on the up-link. This is desired in order to enable the efficient transmission of very short data packets, like single ATM cells. The principle proposed also keeps the complexity of synchronization low at the base station, which is accessed by many users. We describe a simple scheme to limit the transmission power on the up-link. The power is kept constant by deleting subcarriers that are severely attenuated due to fading. Parameter estimation steps for retrieving the data at the base-station are explained and evaluated. Those include the estimation of the timing-offset, the carrier phase offset, and the received magnitude. Coherent demodulation is considered.

This paper describes a method of deriving channel parameters for a time-dispersive (=frequency-selective) radio channel from simple wide-band power measurements. A novel relationship is found allowing the estimation of the root-mean-square (RMS) delay spread from such data. Furthermore, we describe a set of equations that can be used for fitting the (measured) channel parameters to a mathematical model, the so-called frequency-domain (FD) model. We also show a simulation procedure, which directly implements the mathematical description of the channel. The output of this procedure-realizations of frequency-selective channel transfer functions-may be used for instance in the investigation of OFDM systems. The study is restricted to the small-scale modeling; Rayleigh and Ricean fading channels are considered. The estimation of the RMS delay spread is based on the frequency-domain level crossing rate (LCR/sub f/), which is derived from the FD-channel model. It is shown that the RMS delay spread is proportional to the LCR/sub f/. Because of the simple hardware required for finding the LCR/sub f/, the suggested measurement method is particularly interesting for the millimeter-wave frequency band (>30 GHz). However, it can be used at other frequencies as well, where standard laboratory equipment is sufficient for conducting the measurements. The accuracy of this technique depends on the bandwidth observed and can be increased further by combining multiple measurements performed within a small local area.

This paper describes the influence of noise on a previously proposed measurement method for wide-band, Rayleigh- and Ricean-fading radio channels. As the original procedure exploits the proportionality between the level crossing rate in the frequency domain (LCR/sub f/) and the RMS delay spread of the channel, the influence of noise on the LCR/sub f/ is investigated and evaluated here. A compact solution is given for the noise-influenced case, which requires only one parameter, additional to those from the original procedure. However, we observe that the continuous-time (and -frequency) model used to obtain this solution yields a bias of the theoretical result against the ones obtained from the measurements, since they are discrete in frequency. Therefore, for the Rayleigh fading case, the discrete-time LCR is derived to identify the source of this bias. It is seen that the analytical result is not very suitable for being applied to a measurement procedure. It can be used however, to quantify the influence of noise in a particular situation and to assess noise-reduction techniques that might be required.

This paper presents a hardware platform that enables the realistic emulation of wide-band air interfaces at low cost. Real-time base-band algorithms for the modulator and demodulator are implemented on separate-and thus independent-DSP evaluation boards. A channel simulation sub-system is suggested, consisting of another DSP board for multipath channel simulation and of analog I/Q-modulators and -demodulators for emulating carrier frequency offsets. The latter allows assessment of frequency synchronization techniques. Reduced speed (bandwidth -40 kHz) tremendously reduces implementation difficulties. The advantages and limitations compared to full-speed hardware and pure computer simulation are elaborated on. The system is used by the authors to assess real-time algorithms for OFDM air-interfaces, but it can be used for other modulation schemes as well. Another possible application is in the education of electrical engineers.

In this paper, a novel frequency-domain model (FD-model) for the frequency selective Ricean fading radio channel is presented. Frequency domain filtering and the Hilbert transform are used to generate samples of the channel's transfer function with appropriate frequency correlation properties (WSSUS model). One of the main advantages of this approach is the availability of analytical expressions relating channel parameters to model parameters. This can be used to conveniently fit the proposed channel model to any of the (measured) channel parameters. Based on this new model, level crossing statistics of the channel's transfer function were investigated. Results have shown that the level crossing rate (LCR/sub f/) is proportional to the RMS delay spread (RDS) of the channel. This implies that from the LCR/sub f/, which can be derived from wide-band power measurements, an accurate estimation of the RDS is possible. Previously, expensive and cumbersome equipment like network analyzers was required to measure this parameter. Computational results have been evaluated with measurements and a time-domain model for the 60 GHz frequency band.

The dual-signal receiver (DSR), proposed for simultaneous reception of two narrowband modulated co-channel signals, shows large performance degradation when both signals have about equal strength. The performance enhancement of the DSR using binary phase shift keying (BPSK) modulation is obtained with forward error correction (FEC) coding. Rather short linear block codes were evaluated using hard and soft decision decoding techniques. With soft decision decoding, large coding gains are obtained in the channel with co-channel interference and additive white Gaussian noise (AWGN). The gain for the channel with interference is larger than for the AWGN-channel, but the absolute performance is worse. Hard decision decoding gives no significant enhancement. The coding gain for the small signal is about the same as for the large signal due to the occurrence of correlated errors.