The earth’s magnetic field along railway tracks is strongly distorted by magnetic material in the vicinity, e.g., steel in rails and reinforced concrete. The resulting magnetic distortions are persistent in time and characteristic for a certain part of the track. Thus, these distortions can be seen as fingerprints that enable localization when a map of the magnetic field is available. This is particularity interesting for areas where global navigation satellite system (GNSS) signals are not available, such as tunnels. Unfortunately, creating the magnetic map in a GNSS-denied area requires a position reference system that is most likely not available. This paper addresses this problem with a graph-based simultaneous localization and mapping (SLAM) algorithm that uses only odometer and magnetometer measurements. The key idea of the proposed algorithm is to use the magnetic field to detect loop closures and to calculate the relative transformation between different nodes in the pose-graph. The algorithm is evaluated based on a data set recorded with the advanced TrainLab of the Deutsche Bahn traveling on a track in Berlin. Results show that the graph SLAM algorithm together with the magnetic loop closure detection reduces and bounds the position error of the odometry.

Multipath-enhanced device-free localization (MDFL) systems determine presence and location of objects and users not necessarily equipped with localization devices. For localization, MDFL systems exploit user-induced changes in the power of all received signal components, including both line-of-sight and multipath components (MPCs). In this work, we therefore provide a statistical fading model that describes user-induced changes in received power specifically for MPCs. The model is derived and validated empirically using an extensive set of wideband and ultra-wideband measurement data. Since the localization performance of MDFL systems strongly depends on the information about the propagation paths within the wireless network, we further propose a Bayesian calibration approach that estimates the location of the reflection points of MPCs caused by single-bounce reflections. For MPCs caused by single-bounce reflections, the solution space of possible locations of reflection points is constrained to the delay ellipse, which allows the formulation of a computationally efficient one-dimensional estimation problem. Eventually, the problem is solved by sequential Bayesian estimation. The applicability of the proposed approach is demonstrated and evaluated using measurement data. Independent of the underlying measurement system, the Bayesian calibration approach is shown to robustly estimate the locations of the reflection points in different environments. Finally, the localization results of MDFL for an indoor scenario confirm the applicability of the Bayesian calibration approach.

Magnetic-field based indoor localization uses distortions of the Earth magnetic field caused by magnetic material in the surrounding of the platform that is to be localized. Existing magnetic localization methods compare measurements from a magnetometer with a magnetic field map to estimate the position of the platform, on which the sensor is mounted. For the comparison it is typically assumed that the magnetometer is calibrated. Unfortunately, for some platforms calibration is not straightforward, particularly when they are large or heavy. In this paper we therefore propose a simultaneous localization and calibration approach. The approach is based on a Rao-Blackwellized particle filter that makes use of the conditional linearity of magnetometer measurements and calibration parameters. The feasibility of the proposed particle filter is evaluated with three data sets recorded with a differential drive robot in an indoor environment.

Joint Communication & Sensing is a concept where communication signals are reused for wireless sensing, i.e. detecting and locating moving objects. With increasing wireless connectivity and the proliferation of fixed wideband wireless devices, wireless sensing is becoming practical in urban road traffic. Thus, wireless sensing can support or replace infrastructure-based perception systems such as cameras or radar. In this paper, we propose an architecture in which passive coherent localization is supported by spatial information of static multipath components induced by moving objects. Based on wideband measurements, we show that incorporating spatial information from static MPCs improves the performance of state-of-the-art perception algorithms in terms of robustness and accuracy.

The trend towards urbanization increases the need for highly available public transportation. Nevertheless, the majority of society demands for individual transport too. To address these demands in urban areas, intelligent transportation systems (ITS) aim at increasing capacity and safety while reducing costs, accidents, and environmental impact. To that end, both the railway industry and the automotive industry focus on automation, digitization, and wireless communications to cope with increasing numbers of vehicles and passengers. These two industries may rely on cooperative ITS (C-ITS) communicating in the same frequency band. Without appropriate measures, interference between the different radio technologies must be assumed and reliable communication for safety-critical applications cannot be guaranteed. To develop accurate and realistic interference models for current and future radio technologies, we conducted a four-day measurement campaign with the Deutsche Bahn (DB) advanced TrainLab on the Berlin “Süd-Ring” tracks. In this paper, we present an overview on C-ITS radio technologies, the measurement campaign, first results, and conclusions. An initial data analysis shows that adjacent channel interference can cause severe performance degradation on urban rail C-ITS if generated in line-of-sight (LOS) to the train with a significant number of interfering signals.

In this paper a particle filter for absolute train localization based on magnetic field measurements is proposed. The filter utilizes distortions of the earth magnetic field introduced by ferromagnetic infrastructure components along the railway track. The distortions are characteristic for a certain part of the track network and therefore are a source of position information. The particle filter introduced in this paper incorporates a prior created map of these distortions to estimate the train position. This only requires low-cost passive magnetometers and a simple movement model that accounts for the limited dynamics of a train. The feasibility of the approach is demonstrated in an evaluation with measurements collected on a train driving in a rural area. Overall a position root mean square error below four meters could be achieved, proving that the magnetic field is a viable source of position information that is independent from other localization systems like GNSS.

In this paper, we introduce a millimeter wave (mmWave) channel sounder design to enable dynamic vehicle-to-vehicle propagation measurements such as short range train-to-train and different car platooning scenarios. The RUSK-DLR channel sounder has been extended with mmWave frontends to up- and down-convert intermediate signals. This combination is enabling us to perform accurate mmWave propagation measurements in a dynamic manner. The design of the frontends and the evaluation through lab tests and outdoor measurements are described. Outdoor measurements covered static and dynamic scenarios. Measurements at ranges up to 110 m were performed. We could accurately record channel impulse responses, phase measurements and delay information in the different scenarios. Furthermore, we could show a good match between the theoretical free space path loss and empirical received power and evaluate the influence of multipath propagation on the distance estimate.

Vehicle-to-Vehicle (V2V) communication can significantly enhance the performance of collision avoidance systems through periodically exchanging information between vehicles. At urban intersections, the effect of shadowing caused by buildings has a severe influence on the communication performance. In addition, an increased traffic density near an intersection creates a high level of interference, which can lead to a communication performance degradation. In this paper, we evaluate the performance of IEEE 802.11p based V2V communication for cooperative collision avoidance at urban intersections. We focus on the impact of shadowing from buildings and of traffic density on the communication performance. For this purpose, we investigate the effect of both the data rate and transmission power in different scenarios. The simulation results show that transmission power and data rate can be tailored to increase the reliability of the communication for the collision avoidance system. Simulation results also show that buildings at intersections reduce the interference from vehicles in other road sections. In addition, our simulation results reveal more insight on the main cause of packet loss near intersections. They show that loss due to packet collisions is the dominant reason, which can be reduced by increasing the data rate.

Vehicle platooning, where vehicles drive in a convoy with small relative distance, is a promising technology for road transportation. In particular, for Electric Vehicles (EVs), platoon of EVs (e-platoon) can help EVs not only reduce energy consumption but also allow them to exchange energy and charge their battery from a moving electric source on the fly. E-platooning requires Vehicle-to-Vehicle (V2V) communication with very frequent information exchange and very high reliability between vehicles. This poses a big challenge on the communication system especially when e-platooning coexists with other safety-related applications in a mixed traffic scenario. In this work, we consider communication protocols based on ITS-G5 in the Control Channel (CCH) for e-platoon. We propose to use an additional with antennas for communication between e-platoon members and a deterministic distributed scheduling scheme together with dynamic switching of antenna beams. Simulations show that the our protocol can improve the performance of the communication between e-platoon members by reducing the Update Delay (UD) significantly.

Electric Vehicles (EVs) nowadays suffer from range anxiety due to their short driving range and long battery recharging time. One solution to mitigate range anxiety problem is by reducing the unnecessary stop-and-go events to reduce energy consumption. Cooperative Adaptive Cruise Control (CACC) is one of the Vehicular Ad-hoc NETworks (VANETs) applications that can provide safe and smooth driving. In VANETs, all vehicles share their state information periodically by transmitting Cooperative Awareness Messages (CAMs) to their neighbors using Vehicle-to-Vehicle (V2V) communication. CACC exploits the state information of the neighboring vehicles to operate adaptive and energy efficient maneuver. To satisfy the CACC requirements, vehicles need to provide sufficient and accurate information. The precision of this information is highly dependent on the update rate of CAMs and on their communication range. High update rate and communication range can cause channel congestion and increase packet collisions. The focus in this paper is to determine the required update rate and communication range that satisfy the requirements of the CACC to reduce the energy consumption for EVs. Simulation results also show the benefit of using CACC on the energy consumption.

Dynamic route planning is one of the ITS efficiency applications that reduce travel time and energy consumption. To perform efficient route planning, real-time traffic information should be collected and disseminated to vehicles on other road segments. vehicular ad-hoc networks (VANETs) are a promising technology that can provide the communication means to deliver the required traffic information. In this paper, we study and evaluate three state-of-the-art dissemination protocols in realistic vehicular scenarios. The evaluated protocols are geocast-based beacon piggybacking protocol, a low-overhead adaptation of it and greedy perimeter stateless routing (GPSR). As performance metrics we measure the average end-to-end delay, average routing overhead and the packet delivery ratio as a function of traffic density. The goal is to find a reliable dissemination protocol that fulfills the application requirements and requires low routing overhead. Simulation results show that all three protocols achieve the requirements of dynamic route planning in end-to-end delay and packet delivery ratio. However, a significant reduction in the routing overhead can be achieved by the modified piggybacking protocol.

Cooperative safety applications require dedicated short-range communications (DSRC) to provide position-awareness of neighboring vehicles at a specific level of reliability, i.e., awareness-quality, up to a given distance, i.e., awareness-range. However, heavy communication loads negatively impact such awareness requirements due to communication impairments, ranging from strict capacity limitations of DSRC channels to correlated packet collisions due to periodic communication patterns. Transmission control strategies may adapt power or rate to control such impairments but risk missing the requirements of cooperative safety applications. In this paper, we design a new awareness control strategy by implementing a spatial awareness framework. Specifically, we adapt the distribution of the awareness-quality as a function of the awareness-range. Therefore, we first propose random transmit power control (RTPC), which manages to provide different levels of awareness-quality at different ranges, while mitigating correlated packet collisions by randomizing them in space. As RTPC is able to reduce the channel load, we secondly propose to combine RTPC with transmit rate control (TRC) and to benefit from the gained channel resources by subsequently increasing the update-rate and by implication, the quality of position-awareness. The spatial awareness control capability of RTPC+TRC has been evaluated through simulations. We discuss the influence of RTPC+TRC on cooperative safety applications exemplarily for the forward collision warning (FCW) application.

Cooperative positioning (CP) can be used either to calibrate the accumulated error from inertial navigation or as a stand-alone navigation system. Though intensive research has been conducted on CP, there is a need to further investigate the joint impact from the system level on the accuracy. We derive a posterior Cramér-Rao bound (PCRB) considering both the physical layer (PHY) signal structure and the asynchronous latency from the multiple access control layer (MAC). The PCRB shows an immediate relationship between the theoretical accuracy limit and the effective factors, e.g. geometry, node dynamic, latency, signal structure, power, etc. which is useful to assess a cooperative system. However, for a large-scale decentralized cooperation network, calculating the PCRB becomes difficult due to the high state dimension and the absence of global information. We propose an equivalent ranging variance (ERV) scheme which projects the neighbor's positioning uncertainty to the distance measurement inaccuracy. With this scheme, the interaction among the mobile terminals (MTs), e.g. measurement and communication can be decoupled. We use the ERV to derive a local PCRB (L-PCRB) which approximates the PCRB locally at each MT with low complexity. We further propose combining the ERV and L-PCRB together to improve the precision of the Bayesian localization algorithms. Simulation with an L-PCRB-aided distributed particle filter (DPF) in two typical cooperative positioning scenarios show a significant improvement comparing with the non-cooperative or standard DPF.

Orthogonal frequency division multiplexing (OFDM) became a popular transmission approach since it suppresses intersymbol interference (ISI) due to large channel delays. However, intercarrier interference (ICI) for high mobility receivers yields corrupted channel estimates for pilot-aided OFDM channel estimation. Thus, this paper presents a novel time-variant channel estimation approach to mitigate this system impairment. We linearize the time-variant channel and determine the expansion point by channel estimates corresponding to the current OFDM symbol, and we get the unknown channel slopes by an iterative data and channel estimation. Our algorithm combines a least squares or a minimum norm channel slope estimation and the detection of the channel paths. It sets any channel path power equal zero once the corresponding power estimate is smaller than a given threshold. Our algorithm exploits the large channel correlations between the cyclic prefix and the successive OFDM symbol optimally. These maximum correlations reason the superiority of ICI mitigation with cyclic prefixes compared to channel slope estimation with adjacent OFDM symbols, which needs at least twice the number of pilot symbols. Additionally, our iterative ICI mitigation reduces noise impairments.

This paper presents Cram'er-Rao Lower Bounds (CRLBs) for hybrid distance estimation algorithms, i.e., for received signal strength (RSS) and time of arrival (TOA) as well as for RSS and time difference of arrival (TDOA) measurements. The CRLBs are first evaluated with a simple channel model that consists of additive white Gaussian noise (AWGN), path loss (PL), and shadow fading (SF) in contrast to previous works. Several different CRLBs are dereived as the SF can be treated as nuisance parameter. Further, we apply the CRLBs to the WINNER II channel model and to current communication systems. Results show that the hybrid schemes always perform equal or better than RSS, TOA or TDOA only schemes, that the fourth type CRLB is the tightest bound, and that the distance when RSS performs better than TOA or TDOA increases with decreasing bandwidth.

This paper presents a novel low complexity high resolution timing and Doppler acquisition algorithm for direct-sequence code division multiple access (DS-CDMA) systems. We apply the two-dimensional unitary estimation of signal parameters via rotational invariant techniques (ESPRIT) algorithm to the joint delay and Doppler acquisition task in multipath environments. Additionally, we use the advantageous beam space (BS) implementation of unitary ESPRIT, which reduces the computational complexity once again remarkably. The exploitation of a-priori knowledge according to the BS version of unitary ESPRIT also reduces the root mean square error (RMSE) at low signal to noise ratios (SNRs). Furthermore, we enhance the delay and Doppler multipath acquisition by the two-dimensional structured least squares (SLS) algorithm. Thus, our delay and Doppler RMSE reaches the Cramer-Rao bound (CRB) almost as good as maximum likelihood (ML) delay and Doppler acquisition. However, our algorithms save computational complexity compared to the ML algorithm by several orders of magnitude.

This paper presents a novel time-variant ML channel estimator for mobile radio navigation receivers. Our novel ML channel estimator enables the coherent noise averaging over several hundred codewords for time-variant channel phasors. Compared to the conventional incoherent summation of log-likelihood functions or compared to the conventional time-invariant log-likelihood functions, we avoid the squaring loss (SL) completely. The novel time-variant log-likelihood function compared to the conventional time-invariant log-likelihood function yields an SNR gain of up to 15 dB for an observation interval of 200ms. Additionally, since the novel time-invariant log-likelihood functions only require a Slepian subspace of a small dimension, the computational complexity of our novel time-variant ML channel estimation does not exceed the computational complexity of the conventional time-invariant ML channel estimation.

Traditionally, the low complexity maximum likelihood (ML) estimation of several closely-spaced superimposed signal replicas in a communications or navigation receiver is performed using expectation maximization (EM) or space alternating generalized expectation maximization (SAGE) methods. However, the componentwise decomposition of a multidimensional log-likelihood function according to the EM or SAGE algorithm fails for realistic time-variant channels. Thus, we consider the minimization of a multidimensional log likelihood function for the closely-spaced channel delays. To get this log-likelihood function, we use a decomposition of the highly time-variant channel path phasors based on Slepian's subspaces. The obtained log likelihood function enables a coherent averaging over several hundred codewords even for high-mobility receivers. Therefore, we avoid the squaring loss (SL) that appears for the conventional incoherent averaging and incoherent log-likelihood functions. Our simulations show that the minimization of coherent log-likelihood functions based on Slepian's functions yields a root mean square error (RMSE) of the line of sight (LOS) delay estimation that approximates the Cramer-Rao bound (CRB) rather closely.

Statistical models for handover scenarios are necessary in order to identify key parameters and to allow proper benchmarking for handover techniques. This is quite a difficult task, since many assumptions have to be made, resulting in a loss of generality. Efforts towards the generation of such models were already made. Although these models show insights into trade offs, they leave some open questions. It is our purpose to extend previous work by presenting a new model for the macro cellular environment which is based on relative received signal strength technique employing a hysteresis margin and exponential smoothing. This model also incorporates implicitly the effect of the velocity of the mobile terminal on the handover system performance as well as the statistical dependency of consecutive signal strength measurements. We evaluate handover performance parameters such as handover probability, handover delay, expected number of handovers and outage probability. The outcome of this analysis is presented through numerical evaluations of the proposed model.

In this paper, location estimation algorithms for cellular communications systems employing orthogonal frequency division multiplexing (OFDM) are investigated. To determine the location of a mobile station, usually time difference of arrival (TDOA) measurements are performed with at least three base stations using pilot symbols included in the signal streams. With these TDOAs then the location is estimated. However, the timing estimation process is faced by different effects that restrict the performance. For instance, limited number of pilot symbols, inter-cell interference, and multipath propagation decrease the accuracy of the timing estimates, and hence, position estimates. Therefore, we propose to feed back decided data symbols where particularly data-aided timing estimation and interference cancellation are considered in the location estimation context. Simulation results for a 3GPP-LTE system show the ability of these algorithms to increase the overall accuracy and reliability of location estimates.

In this paper, we consider location estimation algorithms using cellular orthogonal frequency division multiplexing (OFDM) communications systems. In the classical approach the mobile station (MS) determines time difference of arrival (TDOA) information from the received signals of at least three base stations (BSs). With these TDOAs the location of the MS is estimated. However, only at the cell edge a good reception of several BSs is ensured. Especially for future systems targeting a frequency re-use of one, interference is a limiting factor. Hence, in the inner cell it is difficult to detect out-of-cell BSs with sufficient quality. Therefore, we propose an interference cancellation scheme to improve the performance in these critical situations for TDOA positioning. Simulation results for a 3GPP- LTE system show the ability of this approach to increase the accuracy and to extend the coverage of the overall location estimation.

We consider a transmission system employing orthogonal frequency-division multiplexing (OFDM) with bit- interleaved coded modulation (BICM) and perfect channel state information (CSI) at both transmitter and receiver. For exploiting the transmit CSI, we present adaptive bit loading (ABL) schemes that minimize the bit-error rate at the decoder output. A simple low complexity and a generalized ABL procedure for square lattice quadrature amplitude modulation signal constellations and Gray encoding scheme are proposed. Simulation results show that at low and medium signal-to-noise ratios (SNRs) the simple low complexity ABL scheme performs as well as the general ABL scheme. The benefits of using the generalized procedure are only visible at high SNR.

We recall the principles of cyclic delay diversity (CDD) and discuss the properties and impact of this transmit antenna diversity technology. Different applications motivate variations of the CDD principle. This variants are briefly introduced and discussed. We show the application to several types of wireless systems, in particular terrestrial digital video broadcasting, cellular mobile radio communications systems and a wireless communications system using adaptive bit loading. Simulation results show the benefits of CDD for these kind of systems.

The wireless hybrid enhanced mobile radio estimators (WHERE) consortium researches radio positioning techniques to improve various aspects of communications systems. In order to provide the benefits of position information available to communications systems, hybrid data fusion (HDF) techniques estimate reliable position information. Within this paper, we first present the scenarios and radio technologies evaluated by the WHERE consortium for wireless positioning. We compare conventional HDF approaches with two novel approaches developed within the framework of WHERE. Yet, HDF may still provide insufficient localization accuracy and reliability. Hence, we will research and develop new cooperative positioning algorithms, which exploit the available communications links among mobile terminals of heterogeneous wireless networks, to further enhance the positioning accuracy and reliability.

In this paper, we analyze an orthogonal frequency division multiplexing (OFDM) system employing combined space- time-frequency (STF) codes and an iterative receiver with extrinsic information transfer (EXIT) charts and bit-error rate plots. As STF codes, we examine orthogonal space-time block codes (OSTBCs), cyclic delay diversity, and discontinuous Doppler diversity. We present a combined STF code for eight transmit antennas with rate one. Another contribution of this work is the EXIT chart analysis demonstrating that EXIT charts are not always a suitable tool to analyze the performance of iterative receivers for STF coded OFDM systems. Further, error floors due to the mapping can be removed even for correlated channels by differential encoding and code doping the bits before the modulator.

This paper considers a downlink multiple-input multiple-output (MIMO) multi-carrier code division multiple access (MC-CDMA) system with pilot aided channel estimation (PACE) and iterative channel estimation (ICE) in the receiver. Exploiting orthogonal space-time block coding (STBC), we investigate ICE schemes as a simple extension of PACE using estimated data chips as additional pilots. Due to the superposition of different users' spread data signals, zero-valued chips can occur after spreading, which can cause noise enhancement when using data estimates as reference signals in ICE. Hence, we propose MIMO channel estimation methods to overcome the above problem. Simulations with a realistic outdoor MIMO channel model, over a wide range of data rates and speeds, show that ICE can outperform PACE at the cost of increased complexity

This paper investigates a downlink multiple-input multiple-output (MIMO) multi-carrier code division multiple access (MC-CDMA) system with iterative channel estimation (ICE) in the receiver. In MC-CDMA, each user's data symbols are spread with a Walsh-Hadamard code. Due to the superposition of different users' spread data signals, zero-valued subcarriers can occur, which cannot be used to compute least-squares (LS) estimates in ICE. For multiple transmit antennas, the data signals coincide non-orthogonal at each receive antenna. Hence, we propose two MIMO channel estimation methods to overcome these problems and avoid noise enhancement in ICE.

In this paper we investigate the performance of a downlink multi-carrier code division multiple access (MC-CDMA) system employing a receiver with iterative channel estimation (ICE). The MC-CDMA system transmits M data symbols per user and per orthogonal frequency division multiplexing symbol in parallel, with each data symbol being spread with a Walsh-Hadamard code of length L. Due to the superposition of spread data signals (chips), zero-valued subcarriers can occur, which cannot be used to compute least-squares (LS) channel estimates in ICE. Therefore, we propose the modified LS channel estimation method to overcome this problem and to avoid noise enhancement in ICE. We further investigate the robustness of ICE with the modified LS method if the channel statistics are not known perfectly. Simulation results demonstrate that a signal-to-noise ratio gain of 1 dB can be achieved when employing robust ICE with the modified LS method.

The concept of a multi-cell environment for an MC-CDMA downlink scenario is presented; it is based on a propagation model taking into account path loss and shadowing. The interference of adjacent cells is studied and the Gaussian approximation (GA) is investigated for simplifying the interference simulation. The major influence of the interfering cells is restricted to the peripheral area of the desired cell. In particular, it is shown, by simulations, that the GA for the impinged interference signals is an appropriate assumption. This drastically decreases the simulation complexity for a high number of interfering cells, e.g., one or two tiers around the desired cell.

We addresses channel estimation based on the discrete Fourier transform (DFT) applied to OFDM-based MIMO systems. By exploiting the properties of the DFT, channel estimation schemes for MIMO-OFDM system can be simplified. The Fourier transform translates phase shifts in the frequency domain to delays in the time domain. In order to exploit this feature, phase shifted pilot sequences are a perfect match to the Fourier transform in terms of separating the N/sub T/ superimposed signals, corresponding to N/sub T/ transmit antennas, without any further processing. The proposed estimator is shown to be optimum for the sample spaced channel, i.e. the channel tap delays are multiples of the sampling duration. A sub-optimum approximation for the non-sample spaced channel is also suggested.

A discrete-time matrix transmission model is introduced for wideband multiuser multiple-input multiple-output (MIMO) transmission systems. The model is more general than existing ones in the sense that it incorporates correlated interference and noise by means of prewhitening. It allows a flexible treatment of users that may or may not be included in a joint detection process. On the basis of the matrix transmission model, conventional multiuser detection techniques are easily adapted for the multiuser MIMO case. It is shown that for certain types of interference, a linear matrix transmission model exists only for a real-valued notation. Moreover, the real-valued notation provides the possibility to include space-time block codes in the linear transmission model. In order to endorse the introduction of this particular matrix model, we show that the discrete-time transmission matrix immediately determines the capacity of the MIMO transmission system. Some simulation results based on optimum and suboptimum detection techniques indicate that the loss of performance due to the exclusion of users from joint detection is quite small.