We report on the proximity detection of a moving object tagged with a passive Ultra High Frequency (UHF) Radio Frequency Identification (RFID) tag compared to a stationary tag and the recognition of predefined movement trajectories of tagged objects. Both methods are based on sequentially received tag responses with time and reader information. The recording system consists of a commercially available UHF RFID reader and a switched antenna system with eight antennas. We use supervised machine learning classification methods comprising convolutional layers, Decision Tree (DT), Random Forest (RF), and Ridge classifiers. The proximity detection is compared with two decision thresholds and shows accuracies of more than 83 percent. Evaluation of the trajectory recognition shows an accuracy of 87.5 percent.

Next-generation wireless communication systems will incorporate millimeter wave (mmWave) as one of the key technologies to increase data rates. These forthcoming mmWave systems will integrate multiple-input multiple-output (MIMO) technology to ensure sufficient link margin and are expected to be deployed in conjunction with sub-6 GHz systems. Configuring a MIMO communication link usually relies on estimating channel state information (CSI), which is difficult to acquire at mmWave frequencies due to the low pre-beamforming signal-to-noise ratio (SNR). In this paper, we propose a novel approach to estimate mmWave MIMO channels by leveraging out-of-band information from a sub-6GHz band. Utilizing this approach, we develop three channel estimation methods and compare these methods with one using only in-band information. We investigate the influence of theK-factor, the SNR and the number of antennas on the performance of the proposed channel estimation methods through simulations. Additionally, we compare these methods in terms of their computational complexity. Finally, we validate the performance of the proposed methods through channel measurements conducted in an outdoor environment. The results demonstrate that the proposed methods outperform in-band mmWave channel estimation in terms of spectral efficiency, particularly in scenarios of low SNR and high K-factor.

Since the design of wireless MIMO systems requires knowledge of the double-directional (i.e., directionally resolved at both link ends) channel characteristics, and 5G/6G use higher frequency bands, there is the need for double-directional measurements in the mmWave spectrum, along with channel sounders that can accurately perform such measurements. This paper introduces a novel channel sounding approach based on a redirecting rotating mirror arrangement (ReRoMA). The method is low-cost and flexible as it requires only a single radio frequency chain at each link end and performs mechanical beamsteering. However, in contrast to existing rotating-horn systems, it physically separates the signal generation/transmission and the beam steering components, resulting in orders-of-magnitude faster measurements. The paper outlines the fundamental concept, describes details of the implementation, and demonstrates its application and accuracy using a 60GHz prototype for measurements in static reference scenarios, as well as dynamic measurements. We illustrate the detected propagation paths using dynamic angular and delay power spectra and correlate these findings with the surrounding environmental structure. Locations of environmental objects are detected within the Fourier resolution determined by bandwidth and horn width, with no noticeable degradation due to the faster measurements.

With the move towards 6G and associated technology deployment in higher frequency bands, measurements of directionally-resolved channels and sounders capable of performing such measurements are a necessity. In this paper, we present a new concept of channel sounding based on a Redirecting Rotating Mirror Arrangement (ReRoMA), capable of performing double-directional channel measurements at millimeter wave frequencies by mechanical beam steering orders of magnitude faster than existing rotating-horn arrangements. We present this new concept, describe a prototype operating at 60 GHz, and use it to perform, as proof-of-principle, a dynamic cart-to-cart channel measurements at a T-intersection scenario. We show that this sounding principle works and allows the directional evaluation of the channel. We visualize the different resolvable propagation paths in terms of dynamic angular and delay power spectrum, and relate them to the environmental geometry.

In this study, the modulation of symbols on OFDM subcarriers is classified for transmissions following Wi-Fi 6 and 5G downlink specifications. First, our approach estimates the OFDM symbol duration and cyclic prefix length based on the cyclic autocorrelation function. We propose a feature extraction algorithm characterizing the modulation of OFDM signals, which includes removing the effects of a synchronization error. The obtained feature is converted into a 2D histogram of phase and amplitude and this histogram is taken as input to a convolutional neural network (CNN)-based classifier. The classifier does not require prior knowledge of protocol-specific information such as Wi-Fi preamble or resource allocation of 5G physical channels. The classifier’s performance, evaluated using synthetic and real-world measured over-the-air (OTA) datasets, achieves a minimum accuracy of 97% accuracy with OTA data when SNR is above the value required for data transmission.

Cooperative connected automated mobility depends on sensing and wireless communication functions. With increasing carrier frequency both functions can be realized with the same hardware, however, the attenuation of radio signals increases quadratically with the carrier frequency. Hence, link setup becomes challenging in vehicular scenarios due to the required beam finding process. In this paper we investigate the multipath components of the vehicle-to-infrastructure (V2I) radio channel in three frequency bands with center frequencies of 3.2 GHz, 34.3 GHz and 62.35 GHz using measurement data with 155.5 MHz bandwidth and a sounding repetition rate of $31.25 \mu \mathrm{~s}$. The channel impulse responses are collected simultaneously at all three carrier frequencies. Using the high temporal sampling rate we apply the CLEAN algorithm, enabling the estimation of the weight, delay and Doppler frequency of multipath components. By analyzing the collinearity of the Doppler normalized scattering function between the frequency bands we found that the collinearity between the 3.2 GHz and 34.3 GHz band as well as between the 3.2 GHz and 62.35 GHz is smaller in the non-line of sight (NLOS) region but increases for the line-of-sight (LOS).

A robust and sparse Direction of Arrival (DOA) estimator is derived for array data that follows a Complex Elliptically Symmetric (CES) distribution with zero-mean and finite second-order moments. The derivation allows to choose the loss function and four loss functions are discussed in detail: the Gauss loss which is the Maximum-Likelihood (ML) loss for the circularly symmetric complex Gaussian distribution, the ML-loss for the complex multivariate t-distribution (MVT) with ν degrees of freedom, as well as Huber and Tyler loss functions. For Gauss loss, the method reduces to Sparse Bayesian Learning (SBL). The root mean square DOA error of the derived estimators is discussed for Gaussian, MVT, and ϵ-contaminated data. The robust SBL estimators perform well for all cases and nearly identical with classical SBL for Gaussian array data.

Gaussian processes (GP’s) have been used to predict acoustic fields by interpolating under-sampled field observations. Using GP interpolation to predict fields is advantageous because of its ability to denoise measurements and for its prediction of likely field outcomes given a certain field coherence, or in GP terminology, a kernel. While there are many design options for a coherence function, in this study we focus on the radial basis function kernel for estimating the direction-of-arrival (DOA) of a plane wave impinging on a uniform linear array. We demonstrate that an array sampled with spacing larger than a half wavelength can benefit from GP interpolation, providing a smaller root mean squared error in comparison to the error of conventional beamforming for DOA estimation.

Classification of modulation of Wi-Fi~6 and 5G downlink (DL) user data signals for spectrum sensing is studied. First, the orthogonal frequency division multiplexing (OFDM) symbol duration and cyclic prefix (CP) length are estimated based on the cyclic autocorrelation function (CAF). We propose a feature extraction algorithm characterizing the modulation of OFDM signals based on the estimated parameters. The algorithm includes removing the effects of a synchronization error and converting the obtained feature into a 2D histogram of phase and amplitude. This histogram is input to a convolutional neural network (CNN)-based classifier. Our system works without knowledge of a carrier frequency, Wi-Fi preamble, or resource allocation of 5G physical channels. We evaluate the classifier's performance with data with various protocol-compliant configurations. Our classifier achieves at least 98% accuracy when SNR is above the value required for data transmission.

Future vehicular communication systems will extend deployed frequency bands from sub-6 GHz to millimeter wave (mmWave). To investigate different propagation effects between sub-6 GHz and mmWave bands in high-mobility scenarios, we proposed a suitable testbed setup to compare these two bands in a fair manner. Experiments conducted using the proposed testbed provide realistic results, but they are only usable if they can be faithfully reproduced. To quantify the reproducibility of the proposed testbed, we perform channel measurements at center frequencies of 2.55 GHz and 25.5 GHz at a velocity of 50 km/h. We investigate the influence of antenna pattern, time between measurements, signal-to-interference-and-noise ratio (SINR) and signal bandwidth on the reproducibility in terms of the channel correlation.

One of the key research directions to increase the capacity of new radio (NR) vehicle-to-everything (V2X) communication systems is extension of employed frequency bands from sub-6 GHz to millimeter wave (mmWave) range. To investigate different propagation effects between sub-6 GHz and mmWave bands in high-mobility scenarios, one needs to conduct channel measurements in both frequency bands. Using a suitable testbed setup to compare these two bands in a fair manner, we perform channel measurements at center frequencies of 2.55 GHz and 25.5 GHz, velocities of 50 km/h and 100 km/h, and at 126 different spatial positions. Furthermore, we conduct a comparative study of the multi-band propagation based on measurement results. We estimate the power delay profile (PDP) and the Doppler power spectral density (DSD) from a large set of measurements collected in a measurement campaign. Finally, we compare measured wireless channels at the two employed frequency bands in terms of root-mean-square (RMS) delay spread and RMS Doppler spread.

Recent investigations indicate that Sparse Bayesian Learning (SBL) is lacking in robustness. We derive a robust and sparse Direction of Arrival (DOA) estimation framework based on the assumption that the array data has a centered (zero-mean) complex elliptically symmetric (ES) distribution with finite second-order moments. In the derivation, the loss function can be quite general. We consider three specific choices: the ML-loss for the circularly symmetric complex Gaussian distribution, the ML-loss for the complex multivariate t-distribution (MVT) with ν degrees of freedom, and the loss for Huber’s M-estimator. For Gaussian loss, the method reduces to the classic SBL method. The root mean square DOA performance of the derived estimators is discussed for Gaussian, MVT, and ϵ- contaminated noise. The robust SBL estimators perform well for all cases and nearly identical with classical SBL for Gaussian noise.

In this paper we present a software defined radio (SDR) based measurement framework that allows for simultaneous measurements of wireless communication channels over multiple bands. We present and discuss results of a wireless vehicular-to-infrastructure multiband channel measurement campaign at center frequencies of 3.2 GHz, 34.3 GHz and 62.35 GHz in a street crossing scenario. The measurement is conducted using a bandwidth of 155.5 MHz and a sounding repetition rate of 31.25 μs. We compare the measurements using the time-variant power delay profile (PDP) and the Doppler spectral density (DSD).

We consider direction of arrival (DOA) estimation for a plane wave hidden in additive circularly symmetric noise at low signal to noise ratio. Starting point is the maximum-likelihood DOA estimator for a deterministic signal carried by a plane wave in noise with a Laplace-like distribution. This leads to the formulation of a DOA estimator based on the Least Absolute Deviation (LAD) criterion. The phase-only beamformer (which ignores the magnitude of the observed array data) turns out to be an approximation to the LAD-based DOA estimator. We show that the phase-only beamformer is a well performing DOA estimator at low SNR for additive homoscedastic and heteroscedastic Gaussian noise, as well as Laplace-like noise. We compare the root mean squared error of several different DOA estimators versus SNR in a simulation study: the conventional beamformer, the phase-only beamformer, and the weighted phase-only beamformer. The simulations indicate that the phase-only DOA estimator has desirable properties when the additive noise deviates from the Laplace-like assumption. The qualitative robustness of these DOA estimators is investigated by comparing the empirical influence functions. Finally, the estimators are applied to passive sonar measurements acquired with a horizontal array in the Baltic Sea.

This paper presents results obtained from a vehicle- to-vehicle channel measurement campaign carried out in the millimeter-wave band around a 60 GHz center frequency and with 8 GHz of bandwidth. We characterize a situation of two oncoming cars on a two-lane road in the campus of the Brno University of Technology. For several vehicle passes we evaluate: (1) observed root mean square (RMS) delay spreads as a function of the received power, (2) temporal decorrelation of the channel impulse response and (3) a dependency of the Pearson correlation coefficient on the received power. For the measurement campaign, a correlative time-domain channel sounder was used.

Safety applications for cooperative intelligent transport systems are limited in their performance by the latency of the communication more than by the achieved throughput. However, there exist few models at packet level that are able to capture the burstiness of the communication. We therefore introduce a packet error model that considers burst lengths through second-order statistics and mean packet errors. The foundation of our approach is the Gilbert-Elliot model, which is able to model not only the packet error rate, but also the burst durations of the packet errors, which we interpret in a nonstationary fashion. Based on this, we formulate maximum likelihood expressions for the time variant model fits, and then proceed to fit the parameters to extensive recorded measurements. We consider the fading statistics of the measured channel and the signal-to-noise ratio and present how they influence the channel burstiness. Our analysis demonstrates that the communication shows strong bursts at packet level, proving the demand for such models. The approach we demonstrate here remains of low computational complexity, allowing future employment in large-scale simulations.

The paper considers direction of arrival (DOA) estimation from long-term observations in a noisy environment. In such an environment the noise source might evolve, causing the stationary models to fail. Therefore a heteroscedastic Gaussian noise model is introduced where the variance can vary across observations and sensors. The source amplitudes are assumed independent zero-mean complex Gaussian distributed with unknown variances (i.e. the source powers), leading to stochastic maximum likelihood (ML) DOA estimation. The DOAs of plane waves are estimated from multi-snapshot sensor array data using sparse Bayesian learning (SBL) where the noise is estimated across both sensors and snapshots. Simulations demonstrate that taking the heteroscedastic noise into account improves DOA estimation.

For analysis of vehicle-to-vehicle (V2V) communications, a firm understanding of the underlying channel is crucial. Through spatial filtering of narrow beams millimeter wave (mmWave) channels become sparse. As accurate channel models are required, without overfitting channel measurements, we investigate the optimal model order given V2V mmWave channel measurements. We use the complex Least Absolute Shrinkage and Selection Operator (LASSO) to find a sparse tapped-delay line representation of the measured channel, and apply the Akaike Information Criterion to find the optimal model order. Beyond this optimal order we start to fit measurement noise. Our results show, that for the given measurements, 4 to 8 taps prove to be an ideal model order.

Understanding millimeter wave (mmWave) vehicular channels is crucial for the application of mmWave technologies in vehicle-to-anything settings. However, as of yet, few attempts of low-complexity approximation of such channels exist. Prior results have shown that such channels are often composed of clustered multipath components, and this work builds on those results. We present an approach to project a measured mmWave channel into subbands. Sufficiently narrow subbands do not resolve the cluster structures and are efficiently approximated as sparse channels. We thereby render sparse tapped delay line model fits possible. In this contribution, we optimize sparse fits in subbands, and then combine all fits to approximate the full band. We evaluate this approach using vehicular mmWave channel measurements, and demonstrate that subband fitting results in efficient leveraging of sparse structures of mmWave channel data.

This paper reports on a power-delay profile measurement campaign emulating a mobile vehicle-to-infrastructure urban-highway environment. The measured location is in the city of Brno, Czech Republic. Utilizing a correlative 50 GS/s time-domain channel sounder with a center frequency of 59.6 GHz and 8 GHz bandwidth and with open-ended WR15 waveguide antennas, we characterize the representative millimeter wave radio channels in terms of the RMS delay spread and its variability caused by mobile scatterers (i.e., vehicles). The RMS delay spread exhibits notable heteroscedasticity as its standard deviation can decrease by 40%. The measured high resolution power-delay profiles exhibit clustering behavior, where a typical number of reflected multipath components is four to five.

The cellular users on-board operational trains heavily suffer from relatively poor performance of current deployment of cellular networks. The improvements in this context are very limited due to the fact that trains mainly operate on rural and slightly sub-urban environments characterized with numerous deep-fade events occurring frequently. Another major concern comes from quasi-metallic train structure, where vehicle penetration loss (VPL) becomes severe and thus seriously affects voice and data applications. However, this impact can be significantly minimized by equipping the train with amplify-and-forward (AF) relay with attached radiating cable. Therefore, to benchmark and understand the variations due to various propagation conditions we conduct real-world measurements on operational trains. In this paper, we address the issue of identifying nominal environments based on statistics of coverage metrics by using agglomerative information bottleneck (AIB). Based on partitions of AIB, we formulate the problem of change detection with nonparametric hypotheses and show that this approach significantly matches to regressions obtained from special case of logistic function (LF) approach.

Network simulators are a crucial tool for evaluating the performance of Vehicular Ad-Hoc Network (VANET) protocols. They allow the assessment of the scalability and the influence of geometric topologies. However, typical network simulators such as NS-3 or OMNET++ often resort to overly simplified packet error models, limiting the validity of the results. Measurements have shown that burst-error patterns are characteristic to VANETs, which are not modeled using the common approaches. In this contribution, we develop a low-complexity burst error model that is parameterized by the Signal-to-Noise Ratio (SNR). We adapt an approach based on the Gilbert-Elliot Markov model, which allows to model first order burst properties while retaining low complexity. Furthermore, we define three performance indicators, overall error probability as well as burst error and burst success probability, and use a multi-feature information bottleneck to find the optimal SNR quantization in the mutual information sense. Based on this, we present Gilbert-Elliot model fits that are easily implementable and demonstrate that low-level SNR quantizations of 4-9 intervals are sufficient to capture the statistics in the MSE sense.

In this paper, we present the enhanced human driver model (EHDM), a time-continuous microscopic car-following model, which considers numerous characteristics attributable to the human driver. It is based on the popular intelligent driver model and the human driver model (HDM), a meta-model which enables the integration of human driving behavior into classical follow-the-leader models. We extend the HDM by adding variable, situation-dependent reaction times, different types of driver distraction, and driving errors to the model. Furthermore, we show that the EHDM provides a more thorough representation of human driving behavior compared with the HDM by performing dynamic traffic simulations and by validating both models with the aid of real-word vehicle data captured in an extensive field test.

This paper proposes and discusses the architecture for a real‐time vehicular channel emulator capable of reproducing the input/output behavior of nonstationary time‐variant radio propagation channels in safety‐relevant vehicular scenarios. The vehicular channel emulator architecture aims at a hardware implementation which requires minimal hardware complexity for emulating channels with the varying delay‐Doppler characteristics of safety‐relevant vehicular scenarios. The varying delay‐Doppler characteristics require real‐time updates to the multipath propagation model for each local stationarity region. The vehicular channel emulator is used for benchmarking the packet error performance of commercial off‐the‐shelf (COTS) vehicular IEEE 802.11p modems and a fully software‐defined radio‐based IEEE 802.11p modem stack. The packet error ratio (PER) estimated from temporal averaging over a single virtual drive and the packet error probability (PEP) estimated from ensemble averaging over repeated virtual drives are evaluated and compared for the same vehicular scenario. The proposed architecture is realized as a virtual instrument on National Instruments™ LabVIEW. The National Instrument universal software radio peripheral with reconfigurable input‐output (USRP‐Rio) 2953R is used as the software‐defined radio platform for implementation; however, the results and considerations reported are of general purpose and can be applied to other platforms. Finally, we discuss the PER performance of the modem for two categories of vehicular channel models: a vehicular nonstationary channel model derived for urban single lane street crossing scenario of the DRIVEWAY’09 measurement campaign and the stationary ETSI models.

Two solutions are presented to overcome the coverage and low bit rate problems for cellular network services in high mobility: amplify-and-forward Moving Relay Nodes (MRNs) and prototype windows on-board Wi-Fi enabled High Speed Trains (HSTs). This paper focuses on 3G/4G User Equipments (UEs) located on-board the high Vehicular Penetration Loss (VPL) vehicles travelling throughout long-range geographical routes. This work is supported by extensive real-world measurements conducted along Austrian railways such as from Vienna to Salzburg and from Vienna to Graz. We propose a novelistic approach and we call SegHyPer. The approach is established by multi-level non-parametric hypotheses tests based on route segmentation link quality parameters to enable micro-analysis in current and future cellular networks for mobile users on-board railjet HST with- and without treatment.

We study the multi-user and multi-carrier power allocation (spectrum balancing) problem in digital subscriber line (DSL) networks under inter-carrier and inter-user interference as well as self-interference. The key assumption of this work is that we do not have knowledge of the interference coefficients, but only have access to the total per-line interference noise power. Furthermore, lines may support different technologies or be part of linear crosstalk cancellation (Vectoring) groups, which implies coupling among lines through per-transceiver sum-power and spectral mask constraints. We propose a novel generalized Nash equilibrium formulation for spectrum balancing in a virtual network with modified selfinterference coefficients and an associated variational inequality problem. The latter allows for intuitive analytical criteria for algorithms' convergence to a unique solution in the virtual network as well as in the real network, related to the sum of interference coefficients. Exemplarily a fully autonomous projected gradient algorithm is proposed which requires no information exchange among Vectoring groups and obeys transmit power limitations throughout its run-time. The applicability of the proposed framework is demonstrated by testing this algorithm on two previously studied power allocation problems in Vectoring G.fast networks.

Vehicle-to-Vehicle channels pose large modeling challenges due to their time-varying nature and their large Doppler shifts. While new descriptions for the channel are being researched, many standardization bodies and hardware manufacturers still rely on the concept of a tapped delay line as channel description. In this paper, we rephrase the problem of estimating a tapped delay line with given number of taps as sparse array processing problem. We formulate the problem via the complex LASSO algorithm, and modify the cost function to only consider frequency regions of interest. We thus end up at a description that estimates a tapped delay line impulse response from channel sounder data in such a way that a given subspace of frequencies is optimally fitted. Our results show that this combination of subspace projection and LASSO optimization allow to estimate the desired channel of interest with high precision and provides large gains over conventional delay line estimation methods.

Beyond question, the improvement of traffic flow is a hot topic in current research, and numerous proposals how to optimize traffic on the roads exist. Distributed communication systems have come up recently as a very important mechanism in modern road networks in order to deal with this issue. Also, traffic delay problems due to jams are manifesting in many of the urban centers worldwide. With the objective of decreasing and in the best case avoiding congestion, V2X communication is exploited in order to counteract traffic jams by intelligent rerouting. Notwithstanding, the transition from very low penetration rates of vehicles that are equipped with communication functionality to a situation where basically all vehicles have the capability to send and receive information will not be made overnight. To a greater degree, the penetration of connected vehicles will increase more and more, which further will result in a very long period of mixed composition. This paper focuses on the analysis of a variable ratio of vehicles with routing and communication capabilities to those who are not. It analyzes the performance of a predictive rerouting algorithm when a distinct percentage of vehicles is unable to communicate for distinct traffic densities, and proves by simulations that even penetration rates far from a hundred percent lead to an improvement of the average time and fuel consumption as well as CO2 emissions per vehicle.

Providing a reliable connectivity for passengers on trains requires to identify and optimize No Service (NoS) areas along a railway track in live networks. We perform a spatial analysis of heterogeneity effect for a mobile user capable to connect on different Radio Access Technologies (RATs). The analysis is supported by real-world measurements, conducted on new regional trains along Austrian railways. Based on established RF similarities and by spatially clustering approximately same size grid-blocks along railway tracks, we give insights in terms of coverage implications to handovers and outages. The handover rate is increased up to four times on presence of insignificant NoS areas. To mitigate the vehicle chassis effect, we equip the train under test with a Moving Relay Node (MRN) and investigate the coverage improvements and outage rate for mobile users under different scenarios. On-board the train with MRN, the outage rate is decreased up to 3% along a railway equipped with dedicated Base Stations (BSs).

The mobile cellular service coverage of nomadic users in the context of high speed vehicular scenarios, e.g., railroad trains, is challenging. Energy saving efforts of the past years have lead to substantially increased vehicle penetration losses, e.g., by metal shielded windows. As a result providing coverage into railroad cars is an emerging topic for optimization efforts, either based on network deployment or on railroad car materials. The quality of these efforts rely heavily on the understanding of the vehicle penetration loss and its angular dependency. In this paper we present a distributed measurement methodology and setup allowing for measuring the penetration loss of railroad cars. We validate the feasibility of the methodology in a real world measurement campaign in Austria. There, we compare two different car configurations, standard and prototypical, at 800-2600MHz for azimuthal angles of arrival of 0-60 degree. The comparison of the two configurations shows a substantial lower penetration loss of the prototypical setup due to windows with alternative material treatment.

Transport delays due to traffic jams are manifested in many urban areas worldwide. To make road traffic networks more efficient, intelligent transport services are currently being developed and deployed. In order to mitigate (or even avoid) congestion, vehicle-to-vehicle and vehicle-to-infrastructure communications provide a means for cooperation and intelligent route management in transport networks. This paper introduces the novel predictive congestion minimization in combination with an A*-based router (PCMA*) algorithm, which provides a comprehensive framework for detection, prediction, and avoidance of traffic congestion. It assumes utilization of vehicle-to-X communication for transmission of contemporary vehicle data such as route source and destination or current position, as well as for provision of the routing advice for vehicles. PCMA*further contains a component for intelligent selection of vehicles to be rerouted in case of a congestion, as well as an A*-based routing algorithm, taking into consideration the current road conditions and predicted future congestion. We prove the performance by dynamic microscopic traffic simulations in a real-world and an artificial road network scenario. Due to the well-performing prediction, the results reveal substantial advantages in terms of time and fuel consumption compared not only with situations with no active rerouting system but also with simple rerouting algorithms and more sophisticated approaches from literature.

The directions of arrival (DOA) of plane waves are estimated from multisnapshot sensor array data using sparse Bayesian learning (SBL). The prior for the source amplitudes is assumed independent zero-mean complex Gaussian distributed with hyperparameters, the unknown variances (i.e., the source powers). For a complex Gaussian likelihood with hyperparameter, the unknown noise variance, the corresponding Gaussian posterior distribution is derived. The hyperparameters are automatically selected by maximizing the evidence and promoting sparse DOA estimates. The SBL scheme for DOA estimation is discussed and evaluated competitively against LASSO (ℓ1-regularization), conventional beamforming, and MUSIC.

Path loss is a major consideration in the link budget of vehicular communication systems. Accurate path loss models enable the prediction of the median received power level of radio waves. In this work, we discuss the empirical path loss models for a Vehicle-to-Infrastructure (V2I) scenario in the 5.9 GHz band based on real-world measurements carried out with IEEE 802.11p in the urban areas of Munich, Germany. Our data analysis is compared to widely popular empirical models such as Original Hata, COST-Hata, Extended Hata and Extended Hata-SRD.

Vehicular communication channels are characterized by a non-stationary time- and frequency-selective fading process due to fast changes in the environment. We characterize the distribution of the envelope of the first delay bin in vehicle-to-vehicle channels by means of its Rician$K$-factor. We analyze the time–frequency variability of this channel parameter using vehicular channel measurements at 5.6 GHz with a bandwidth of 240 MHz for safety-relevant scenarios in intelligent transportation systems (ITS) . This data enables a frequency-variability analysis from an IEEE 802.11p system point of view, which uses 10 MHz channels. We show that the small-scale fading of the envelope of the first delay bin is Rician distributed with a varying$K$-factor. The later delay bins are Rayleigh distributed. We demonstrate that the$K$-factor cannot be assumed to be constant in time and frequency. The causes of these variations are the frequency-varying antenna radiation patterns, as well as the time-varying number of active scatterers, and the effects of vegetation. We also present a simple but accurate bimodal Gaussian mixture model, which allows to capture the$K$-factor variability in time for safety-relevant ITS scenarios.

This contribution documents and discusses recent wideband radio channel measurements carried out in the intra-vehicle environment. Channels in the millimeter-wave (MMW) frequency band have been measured in 55-65 GHz using open-ended rectangular waveguides. We present a channel modeling approach based on a decomposition of spatially specific Channel Impulse Responses (CIRs) into the large and small scale fading. The decomposition is done by a Hodrick-Prescott filter. We parametrize the small scale fading utilizing Maximum-likelihood estimates for the parameters of a generalized extreme value (GEV) distribution. The large scale fading is described by a two dimensional polynomial curve. We also compare simulated results with our measurement exploiting two-sample Kolmogorov-Smirnov test.

In this contribution, we present the results of a measurement campaign targeting at system level evaluation of vehicular multiple-input-multiple-out (MIMO) antennas. Our measurements are performed on a test track served by a Long Term Evolution (LTE) base station operating in the 800 MHz band. We show system level evaluation results obtained by using two reference monopole antennas and two automotive qualified, integrated prototype antenna systems. The presented results are gathered in line-of-sight (LOS) as well as non-LOS conditions. As vehicular antenna systems are limited by the available integration space, we analyze the specific impact of antenna decoupling on system level performance. System level performance metrics include parameters such as ergodic capacity and condition number of the MIMO channel matrix. We achieve antenna decoupling for the reference monopoles by increasing their separation. In case of the integrated antenna system we use a decoupling branch to decrease antenna coupling and correlation at the measurement frequency. We discuss a system level evaluation process for ranking antenna systems based on channel specific parameters. The applicability of the process for MIMO system performance evaluation is shown with a comparison of an integrated antenna system with the described reference antenna system.

Future vehicles are expected to integrate multiple radio access technologies to offer traffic information services for Intelligent Transportation Systems (ITS). Dedicated short range communication (DSRC) being designed exclusively for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication is one of the major candidates to enable such services. The focus of this paper is to shed light on the integration aspects of this technology, especially in regards to the antenna design. A methodology is presented to overcome the difficulties occurring from the integration of the antenna in the radome on the vehicle rooftop. With help of simulations and measurements it is illustrated how the conventionally unwanted dielectric lens effect of the antenna radome resulting in nulls in the radiation pattern can be used to deliberately increase the antenna performance. The performance enhancement by using the antenna radome as a dielectric lens is validated with four prototypes in a semi-anechoic chamber. It is moreover shown that simplified geometrical shapes such as cuboids can be used to describe efficiently the behavior of geometrical more complex radome designs. The greatest advantage of our proposed methodology to enhance antenna performance is that it can be employed without changes of any electronic control units. Thus it can easily be included in a running production cycle without even altering any design elements of the vehicle. To demonstrate to which degree the achieved enhancement on component level translates into system level we also present simulation results of a test scenario including a vehicle and a road-side unit. The outcome of the system level simulations shows substantial improvements in the received signal strength (RSS).

Dependable vehicular communications systems are a key requirement for the implementation of intelligent transportation systems (ITS). Multiple antenna receivers enable the utilization of spatial diversity to increase the reliability as well as improve the availability. The cost of a multiple antenna receiver is the increased hardware complexity due to multiple receive chains. In this paper we demonstrate that antenna selection for IEEE 802.11p allows to connect the antenna with the best instantaneous signal to noise ratio (SNR) to a single receive chain while maintaining the diversity gain. We extend the IEEE 802.11p frame structure by two short preambles to estimate the SNR for each receive branch. The numerical simulation results from our standard compliant link level simulation show a bit error ratio reduction from 8.159 · 10-3to 6.681 · 10-3at an SNR of 8 dB and from 4.688 · 10-5to 1.063 · 10-5at an SNR of 12 dB with antenna selection.

This paper presents the results of an accuracy assessment of a deterministic channel model for vehicle-to-vehicle (V2V) communications. Channel simulations obtained from the ray-tracing model developed by TU Braunschweig are compared to data gathered during the DRIVEWAY V2V channel measurement campaign at 5.6 GHz in the city of Lund in summer 2009. The analysis focuses on PDP and channel gains in an urban four-way intersection scenario. Despite some implementation-based limitations of the ray-tracing model, a very good agreement between simulation and measurement results is achieved. Most relevant power contributions arising from multiple-bounce specular reflections as well as from single-bounce non-specular reflections are captured by the deterministic model. We also discuss the question to what extent roadside obstacles like traffic signs, parked cars or lamp posts have to be considered when characterizing the V2V channel.

In this paper we propose and analyze a novel, computationally inexpensive packet-error model for infrastructure- to-vehicle (I2V) communications. Based on real-world measurements using IEEE 802.11p devices, the proposed model incorporates the physical layer characteristics and propagation effects in an authentic highway environment with real vehicular traffic. With only a small set of parameters, the model allows to simulate time series of packet error events with a Hidden Markov Model (HMM). We propose this packet error model for the simulation of vehicular connectivity in the context of intelligent transport systems.

In this paper we analyze measurements of short range ultra-wideband (UWB) multiple input multiple output (MIMO) transmission. 2 × 2 near field UWB measurements were undertaken in an anechoic chamber using a Medav UWB MIMO channel sounder. Channel characteristics were recorded for transmitter to receiver separations of 0.1 m to 0.7 m. Two independent channels were achieved, and channel capacity is twice as high as capacity of a single channel.

Decentralized physics-based field estimation in clustered sensor networks requires the exchange of state vectors between neighboring clusters. We reduce the communication overhead between clusters by using a differential encoding of state vectors that exploits the spatio-temporal field dependencies. This encoding involves a Kalman prediction step that builds on the state-space equations governing the field's spatio-temporal evolution. The Kalman step keeps the computational complexity low. Simulation results for an acoustic field demonstrate the approach.

In this paper, we discuss and analyze results from real-world performance measurements of a two-hop mobile scenario in an IEEE 802.11p-based vehicular ad hoc network. We have used a roadside unit (RSU) mounted on a highway gantry as information source, and two vehicles equipped with on-board units (OBUs), acting as relay and destination node. We analyze performance gains in terms of coverage range extension and throughput increase, achievable by combining the information received via the direct and the relay link. Our results show that a simple decode-and-forward (DF) relaying yields substantial performance improvements in IEEE 802.11p-based networks.

In this contribution, we present performance evaluation results for a multi-antenna receiver for Wireless Access in Vehicular Environments (WAVE) according to IEEE 802.11p. The analysis was performed by employing the Selection Combining (SC), the Equal Gain Combining (EGC) and the Maximum Ratio Combining (MRC) algorithms on recently carried out real-world measurement data. We show that a well-engineered diversity combining algorithm, applied to multi-antenna systems, significantly improves the reliability and robustness, even under poor receive conditions. The resulting performance enhancement compared to the single antenna regime in case of the frame success ratio (FSR) is increased up to 25%. This is due to the fact that all available information from both antenna branches is utilized. Furthermore, we investigate the influence of different driving directions on the system performance.

The assumption of wide-sense-stationarity (WSS) and uncorrelated scattering (US) of wireless propagation channels is widespread for the design and analysis of wireless propagation channels. However, the assumption is only valid within a limited range. In this paper, we investigate the extension of this range not only in time (WSS), but also in frequency (US), for measured vehicular propagation channels. We do this by using the collinearity of the local scattering function, which is a bounded spectral distance metric, and thus allows us to set an indicative threshold, which in turn enables a WSS and a US test. We prove that the fading process in vehicular communications is strongly non-WSS, which agrees with results previously reported in the literature. Furthermore, for the first time (to the authors' knowledge), we show their non-US behavior as well, mainly in scenarios with rich scattering. The dimensions of the minimum stationarity region are on the order of 40 ms in time and 40MHz in frequency.

In this contribution we design, implement and characterise a novel low-profile dual-band loop antenna suitable for advanced tyre monitoring systems by measurements. The antenna is omnidirectional in azimuth, vertically polarised and suited for the European UHF and microwave RFID bands. Our proposed antenna is compared to a modified monopole antenna, which is in resonance at the same bands. Both antennas were manufactured and afterwards measured in an anechoic chamber. Directional patterns and measured antenna efficiencies are presented.

In this paper, we discuss and analyze results from real-world vehicle-to-infrastructure measurements in an IEEE 802.11p-based vehicular ad hoc network. For our experiments we have used six roadside units (RSUs) mounted on highway gantries equipped with five different antenna types. We compare the performance of directional and omnidirectional antennas and analyze performance improvements in terms of coverage range and throughput, achievable by using directional antennas. Our results show that the use of directional antennas yields substantial performance improvements in IEEE 802.11p-based networks. However, for high-gain antennas even more careful RSU antenna positioning with respect to the lane geometry is required.

An ultra-wideband (UWB) antenna array is designed to satisfy given constraints in a (large) contiguous frequency range. Space limitations are tight in many applications and it is often infeasible to design the antenna array with preferred element spacings of λ/2 at the centre frequency. In this study, the authors compare uniform linear UWB antenna arrays and non-uniform reduced-aperture UWB antenna arrays with more compact element spacings following a geometric progression. The authors analyse and discuss performance degradations with respect to irregularly spaced monopoles. The manufactured non-uniform linear UWB antenna arrays are characterised by measurements in an anechoic chamber. The results are correlated with the arrayfactor and compared with measured data of uniform linearly spaced monopoles in the time and frequency domains. It is found that a compact non-uniform linear UWB array with element spacings following a geometric progression can be designed such that its main lobe performs very close to that of a uniform linear array, with an antenna array size reduction of 23.6% for a seven-element non-uniform linear UWB array.

In this contribution, we discuss and analyze results from real-world performance measurements for IEEE 802.11p during September 2010 along the highway S1 near Vienna, Austria. More specifically, we evaluate the frame success ratio and goodput of the IEEE 802.11p physical (PHY) layer for a infrastructure-to-vehicle (I2V) scenario in a tunnel. We report and discuss the observed frame success ratios and goodputs for various PHY parameter settings and investigate the impact of the propagation environment and the traffic situation inside the tunnel.

In this paper we apply constrained optimization techniques to optimally allocate bandwidth and transmit power to the users in a cellular network. We utilize partial frequency reuse as inter-cell interference mitigation technique considering multiple users uniformly located in the cell. Efficient algorithms are proposed to select the user in the cell regions. Moreover, based on user's selection the maximization of the minimum rate is used to optimally allocate the bandwidth and power to the users. We further demonstrate by simulations that using partial frequency reuse as inter-cell interference mitigation technique is more efficient in power use than using frequency reuse-1 or frequency reuse-3.

IEEE802.11p is an approved amendment to the IEEE802.11 standard providing wireless access in vehicular environments (WAVE). It defines enhancements to the Physical layer (PHY) and Medium Access Control (MAC) for the support of Intelligent Transport Systems (ITS). This includes communication links between vehicles as well as between vehicles and a roadside infrastructure. The current MAC method uses randomized backoff in the case of access collisions which induces upredictable communication delays. It is known that such unpredictable delays severely limit the value of safety-related services. The most effective way forward is to design a protocol that suits vehicular traffic and safety- related service constraints and can coexist with the current IEEE802.11p MAC method. Self-Organizing Time Division Multiple Access (STDMA) is a suitable alternative, due to its structured channel access, predictable delay and periodic character. This contribution presents the time-evolution of throughput based on measured signal-to-noise ratio time-series of four vehicles driving on the same road joining the channel. These time-series were acquired during a real-world experiment in the 5.9 GHz band during 2010. Our results show that a collision-free access featuring predictable communication delay is feasible.

Inside a tunnel, electromagnetic wave propagation differs strongly from the well understood "open-air" situation. The characterization of the tunnel environment is crucial for deploying vehicular communication systems. In this paper we evaluate vehicle-to-vehicle (V2V) radio channel measurements inside a tunnel. We estimate the time-varying root mean square (rms) delay and Doppler spreads, as well as the excess delay and the maximum Doppler dispersion. The fading process in V2V communications is inherently non-stationary. Hence, we characterize the stationarity time, for which we can consider the fading process to be wide sense stationary. We show that the spreads, excess delay, and maximum Doppler dispersion are larger on average when both vehicles are inside the tunnel compared to the "open-air" situation. The temporal evolution of the stationarity time is highly influenced by the strength of time-varying multipath components and the distance between vehicles. Furthermore, we show the good fit of the rms delay and Doppler spreads to a lognormal distribution, as well as for the stationarity time. From our analysis we can conclude that the IEEE 802.11p standard will be robust towards inter-symbol and inter-carrier interference inside a tunnel.

We apply constrained optimization techniques to optimally allocate the bandwidth and power to the users in a cellular network. Partial frequency reuse with multiple users in the inner region and the outer region is considered as inter-cell interference mitigation scheme. We show that the maximization of the minimum rate under variable bandwidth and variable power can be transformed into a geometric programming problem and solved efficiently using disciplined convex programming. CVX is used to solve the transformed optimization problem. The optimal solution assigns all available bandwidth to the inner and the outer users and only the satisfactory level of power that the users achieve higher rates that the minimum required rate.

To make transportation safer, more efficient, and less harmful to the environment, traffic telematics services are currently being intensely investigated and developed. Such services require dependable wireless vehicle-to-infrastructure and vehicle-to-vehicle communications providing robust connectivity at moderate data rates. The development of such dependable vehicular communication systems and standards requires accurate models of the propagation channel in all relevant environments and scenarios. Key characteristics of vehicular channels are shadowing by other vehicles, high Doppler shifts, and inherent nonstationarity. All have major impact on the data packet transmission reliability and latency. This paper provides an overview of the existing vehicular channel measurements in a variety of important environments, and the observed channel characteristics (such as delay spreads and Doppler spreads) therein. We briefly discuss the available vehicular channel models and their respective merits and deficiencies. Finally, we discuss the implications for wireless system design with a strong focus on IEEE 802.11p. On the road towards a dependable vehicular network, room for improvements in coverage, reliability, scalability, and delay are highlighted, calling for evolutionary improvements in the IEEE 802.11p standard. Multiple antennas at the onboard units and roadside units are recommended to exploit spatial diversity for increased diversity and reliability. Evolutionary improvements in the physical (PHY) and medium access control (MAC) layers are required to yield dependable systems. Extensive references are provided.

In this paper we analyze the Ricean K-factor for vehicle-to-vehicle (V2V) communications in a typical open sub-urban street crossing. The channel conditions vary from non line-of sight (NLOS) to line-of-sight (LOS). The antenna arrays used for recording the radio channels consist of 4 elements with directional radiation patterns. We measured 16 individual single-input single-output channels, with a bandwidth of 240MHz for a duration of 20 s. We performed two kind of evaluations. For the first analysis we partitioned the 240MHz bandwidth into 24 sub-bands with 10MHz each, according to 802.11p. The small-scale fading of the first delay bin is Ricean distributed with a time-varying K-factor. The later delay bins are mostly Rayleigh distributed. We observe that the large/small K-factor values are not necessarily correlated with the received power. We show that the K-factor can not be assumed to be constant in time, frequency, and space. The antenna radiation patterns, and the illuminated objects by them at different time instances are the cause of these variations. The second evaluation considers the 240MHz bandwidth, and the narrow-band K-factor is calculated for each frequency bin, with Δf = 312 kHz. We corroborate the need to consider the frequency variation of the K-factor. We conclude that a multi-dimensional varying K-factor models the large-scale statistical behaviour more accurately than a constant K-factor.

Due to the legal situation in some states and the beneficial effects on vehicular safety, fuel consumption and comfort issues, tyre pressure monitoring systems became very popular in the recent years. Despite this situation, few measurement data about the propagation channel between the sensor units and the vehicle's reader antenna are available, especially for frequency bands above 433MHz and for reader antennas mounted on the vehicle's bottom plate. In this contribution we present static channel measurements for an advanced tyre monitoring system (ATMS) in the 868MHz and the 2.45GHz bands, carried out with a vehicle equipped with a single dual-band receive antenna at the vehicle bottom. ATM systems obtain sensor data directly in the tyre, so the sensor unit antennas were placed at the tyre sidewalls.

The number of signals plays a crucial role in array processing. The performance of most direction finding algorithms relies strongly on a correctly specified number of signals. When this information is not available, conventional approaches apply information theoretic criteria or multiple hypothesis tests to simultaneously estimate model order and parameter. These methods are usually computationally intensive, since ML estimates are required for a hierarchy of nested models. In the previous work, we proposed a computationally efficient solution to avoid this full search procedure and demonstrated its feasibility by extensive simulations. Here we extend to broadband data, and address issues unique to the broadband case. Our max-search approach computes ML estimates only for the maximally hypothesized number of signals, and selects relevant components through hypothesis testing. Another novelty of this work is the reduction of indistinguishable components caused by overparameterization. Our approach is based on the rank of the estimated steering matrix. Numerical experiments show that despite an unknown number of signals, the proposed method achieves comparable estimation and detection accuracy as standard methods, but at much lower computational expense.

Vehicle-to-vehicle (VTV) communications are of interest for applications within traffic safety and congestion avoidance, but the development of suitable communications systems requires accurate models for VTV propagation channels. This paper presents a new wideband MIMO (multiple-input- multiple-output) channel model for VTV channels based on extensive MIMO channel measurements performed at 5.2 GHz in rural environments in Lund, Sweden. The measured channel characteristics, in particular the non-stationarity of the channel statistics, motivate the use of a geometry-based stochastic channel model (GSCM) instead of the classical tapped-delay line model. We introduce generalizations of the generic GSCM approach and find it suitable to distinguish between diffuse and discrete scattering contributions. The time-variant contributions from discrete scatterers are tracked over time and delay using a high resolution algorithm, and our observations motivate their power being modeled as a combination of a deterministic part and a stochastic part. The paper gives a full model parameterization and the model is verified by comparison of MIMO antenna correlations derived from the channel model to those obtained directly from measurements.

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.

We derive bounds on the expected capacity and outage capacity of a three-node relay network for UWB communications. We also provide a simple tight approximation for the derived upper bound on the capacity and then using this bound we obtain the outage probability of the network. Numerical results show that a significant improvement in the system capacity and outage probability is obtained by adding a relay node. Moreover, our theoretical results reveal that the diversity gain of a relay channel substantially increases by using UWB links instead of NB links. We also derive these bounds when we have a constraint on the total transmitted power of the source and the relay nodes.

Vehicle-to-vehicle (VTV) wireless communications have many envisioned applications in traffic safety and congestion avoidance, but the development of suitable communications systems and standards requires accurate models for the VTV propagation channel. In this paper, we present a new wideband multiple-input-multiple-output (MIMO) model for VTV channels based on extensive MIMO channel measurements performed at 5.2 GHz in highway and rural environments in Lund, Sweden. The measured channel characteristics, in particular the nonstationarity of the channel statistics, motivate the use of a geometry-based stochastic channel model (GSCM) instead of the classical tapped-delay line model. We introduce generalizations of the generic GSCM approach and techniques for parameterizing it from measurements and find it suitable to distinguish between diffuse and discrete scattering contributions. The time-variant contribution from discrete scatterers is tracked over time and delay using a high resolution algorithm, and our observations motivate their power being modeled as a combination of a (deterministic) distance decay and a slowly varying stochastic process. The paper gives a full parameterization of the channel model and supplies an implementation recipe for simulations. The model is verified by comparison of MIMO antenna correlations derived from the channel model to those obtained directly from the measurements.

Abstract—Channel estimation inaccuracy is known to affect significantly the error performance of coded communication systems. This applies in particular to broadband MIMO channels, often considered in conjunction with OFDM such as in the IEEE 802.11n and 802.16 standards. The focus of this work is on an 802.11n compliant MIMO-OFDM communication system. Different channel estimation techniques (based on pilot symbol insertion) are considered and their relevant error performance is analyzed. More specifically, genie-aided, mismatched, and optimum channel estimation techniques are studied with special emphasis on the last one as far as concerns the relative error performance versus complexity trade-off in suboptimum implementation. It is shown that the optimum receiver can be implemented by limiting the channel processing to the dominant eigenmodes, in order to reduce the ensuing complexity. The approach followed in this work may be seen as an extension of previous results relevant to the narrowband MIMO channel. I.

Car-to-car and car-to-infrastructure (henceforth called C2X) communications are constantly gaining importance for road- safety and other applications. In order to design efficient C2X systems, an understanding of realistic C2X propagation channels is required, but currently, only few measurements have been published. This paper presents a description of an extensive measurement campaign recently conducted in an urban scenario, a rural scenario, and on a highway. We focused on 4 x 4 multiple-input multiple-output (MHVIO) measurements at a center frequency of 5.2 GHz with high Doppler resolution. As first results from evaluating the measurement data we present the power-delay profile and the delay-Doppler spectrum from a selected, especially interesting measurement run from an urban measurement route. We observe dispersed Doppler contributions between zero and the Doppler shift corresponding to the relative speed of the cars, and very concentrated (in the Doppler domain), contributions from double reflections. Surprisingly, we also found paths with larger delays and zero Doppler shifts.

We present the basic methodology for minimum-energy bandlimited prediction of time-variant flat Rayleigh-fading channels. This predictor is based on a subspace spanned by time-concentrated and bandlimited sequences. The concept of time-variant channel estimation using time-concentrated and band-limited sequences was introduced recently by Zemen et al. We extend this concept to the problem of time-variant channel prediction. Slepian showed that discrete prolate spheroidal (DPS) sequences can be used to calculate the minimum-energy bandlimited continuation of a finite sequence. DPS sequences are optimal for a time-variant channel with flat Doppler spectrum. We generalize the concept of time-concentrated and band-limited sequences to arbitrary Doppler spectra approaching closely the lower prediction error limit defined by the Wiener filter. In practical systems detailed channel covariance information is not available. We design a set of subspaces spanned by DPS sequences with fixed time-concentration but growing bandwidth. The best DPS subspace is selected dynamically for each observation interval using a Doppler bandwidth estimate allowing for low complexity channel prediction. The performance of the new predictor is compared to that of the Wiener filter by means of Monte Carlo simulations.

On the way to answer the controversial question "What is a cluster?", we introduce a novel cluster tracking mechanism which is based on the multi-path component distance (MCD). We perform a temporal tracking of cluster centroids in the multidimensional parameter domain, starting from cluster estimates obtained by a recently introduced framework which automatically clusters parametric MIMO channel data. To validate our algorithm, we use both synthetic and measured MIMO channels. We generate the synthetic channel using the IlmProp simulation tool. For the real-world channels we use data from an outdoor measurement campaign in a rural area. Simulation results with synthetic channels validate the tracking algorithm, while its application on measurement data shows the occurrence of several clusters with different lifetimes

We investigate the update rate for channel estimation in time-varying channels for UMTS-HSDPA. Our channel estimator is based on correlation of the received chip stream and the known pilot stream. This results in an averaging of the estimated channel over a block. First, we investigate the influence of the update rate by averaging over the perfectly known channel at the receiver. This corresponds to the upper bound for block averaging. Second, we examine the throughput by averaging over correlation based channel estimation. We find specific update rates for the ITU-channel models PB3, VA30, and VA120 in combination with RAKE reception or LMMSE equalization for 16-QAM symbol constellation

Joint time-variant channel estimation and multi-user detection are key building-blocks for wireless broadband communication for mobile users at vehicular speed. We propose an iterative receiver for a multi-carrier (MC) code division multiple access (CDMA) system in the uplink. Multi-user detection is implemented through iterative parallel interference cancellation and conditional linear minimum mean square error (MMSE) filtering. MC-CDMA is based on orthogonal frequency division multiplexing (OFDM), thus time-variant channel estimation can be performed for every subcarrier individually. The variation of a subcarrier over the duration of a data block is upper bounded by the maximum Doppler bandwidth which is determined by the maximum velocity of the users. We exploit results from the theory of time-concentrated and bandlimited sequences and apply a Slepian basis expansion for time-variant subcarrier estimation. This approach enables time-variant channel estimation without complete knowledge of the second-order statistics of the fading process. The square bias of the Slepian basis expansion is one order of magnitude smaller compared to the Fourier basis expansion. The square bias of the basis expansion is the determining factor for the performance of the iterative joint channel estimation and data detection. We present an iterative linear MMSE estimation algorithm for the basis expansion coefficients in a multi-user system. The consistent performance of the iterative receiver using the Slepian basis expansion is validated by simulations for a wide range of velocities

Signal-to-noise ratio is an important parameter in many receivers. In this contribution, we derive a fixed-point equation whose solution coincides with the maximum-likelihood (ML) estimate of the amplitude of a binary phase-shift keying modulated signal and the variance of the additive white Gaussian noise. The resulting fixed-point equation is efficiently solved in a few iterations.

Research on multi-user diversity (MUD) is traditionally carried out from an information-theoretic point of view. Such information-theoretic approaches are feasible in homogeneous systems where all terminals use the same modulation, channel coding, transmission rate, and packet length. In homogeneous systems, the selection of the terminal with highest SNR leads to the highest system goodput. Unfortunately, in heterogeneous systems, choosing the user with highest SNR does not lead to highest system goodput in general. In this paper, we propose a crosslayer approach to the MUD scheduling problem in heterogeneous systems. More specifically, we propose using a maximum expected goodput criterion for MUD scheduling. Furthermore, contrary to the traditional approach where link adaptation is not considered in MUD scheduling studies, we combine MUD scheduling with a link adaptation strategy based on the same maximum expected goodput criterion. Such techniques are analyzed and simulated in a IEEE802.11e environment.

Multidimensional harmonic retrieval (HR) problems often appear in the context of MIMO wireless channel sounding. In particular, for a double-directional parametric MIMO channel model with uniform linear transmit and receive arrays, and a fixed wireless scenario (static - no Doppler), fitting the channel model parameters amounts to a 3D harmonic retrieval problem. For this latter problem, we develop two new algorithms. One is based on conjugate-folding of the 3D data and reduction to an eigenvalue decomposition problem; the other on a 3D version of the rank reduction estimator (RARE) applied to a subspace extracted from a single data snapshot, using 3D conjugate-folding. Both algorithms remain operative close to the best known model identifiability boundary. The two algorithms are compared via pertinent simulations.

This paper focuses on how computational complexity associated to an algorithm for the maximisation of throughput in a multiple-input multiple-output system can be reduced. Throughput optimisation is achieved by selecting the appropriate subset of antennas on the transmit side. Given the highly non-linear nature of the throughput expression and the finite set of combinations, a straightforward approach consists in checking every single subset. However, this results in a prohibitive complexity. This paper presents two methods, named top-down and bottom-up approaches, that lower computational burden by recursively obtaining throughput expressions and, thus, avoiding unnecessary re-computations. The resulting schemes are compared in terms of number of required floating-point operations.

In this work we compare the channel estimation algorithms for use in an iterative CDMA receiver in a block fading environment. The receiver consists of a soft multiuser data estimator, a bank of single user decoders, und a multiuser channel estimator. The multiuser data estimator is implemented as parallel interference canceler with unconditional post-MMSE filtering (PIC-MMSE) and the decoder is a soft-in soft-out MAP decoder. In the channel estimator we make use of dedicated pilot symbols and fed back soft-code symbols which are exploited as additional soft pilot symbols when the iterations proceed. We show that using extrinsic information increases the receiver performance significantly compared to using a posteriori information in the feedback for channel estimation. We introduce a linear MMSE (LMMSE) estimator which takes into account the variances of fed back code symbols and compare it to approximations of the least-squares (ALS) estimator and the linear minimum-mean-square-error (ALMMSE) estimator. Performance results are illustrated in terms of bit error rate (BER) and average normalized square error (ANSE) of the channel estimators. They show that the newly proposed LMMSE algorithm outperforms the ALS and ALMMSE algorithms.

Multiple-input-multiple-output (MIMO) systems are under intense investigation for wireless transmission systems. Space-time coding is an efficient approach to exploit the huge diversity offered by the MIMO channel. One of the main problems of space-time block coding (STBC) is that the offered diversity can only be exploited by the optimal ML receiver which has exponential complexity. In contrast, linear receivers like the ZF-receiver are less complex, but achieve in general only a fraction of the offered diversity. In this paper, we show that it is also possible to achieve high diversity with linear receivers utilizing STBCs with a specific structure. We investigate the diversity properties of cyclic codes and extended Alamouti codes. It is shown, that the diversity order D increases asymptotically linear with the number of receive antennas (n/sub R/) and that the extended Alamouti code achieves already almost full diversity utilizing linear receivers and only small n/sub R/.

In iterative receiver structures, soft information becomes available after the decoding stage. This information is used to enhance the quality of the channel estimates for the next iteration. We derive a generalized estimator based on the linear minimum mean square error (LMMSE) principle for deterministic pilot information combined with soft information. We present the special case of multi-carrier code division multiple access (MC-CDMA) in detail and provide simulation results. The presented channel estimation algorithm can be also applied to direct sequence (DS)-CDMA and multiple-input multiple-output (MIMO) systems.

In this paper, a new approach to the multi-dimensional harmonic retrieval problem is proposed. The novel method is based on a multi-dimensional extension of the Rank Reduction Estimator (RARE), originally developed for DOA estimation in partly calibrated arrays. In the K-D RARE algorithm the frequency parameters in the various dimensions are sequentially estimated. The dimensionality of the estimation problem and therefore the computational load of the optimization procedure is successively reduced exploiting the rich multidimensional structure of the estimation problem. This important property yields various benefits like high estimation performance, weak identifiability conditions and automatically associated parameter estimates. The performance of the algorithm is illustrated with the example of MIMO communication channel estimation based on the double-directional channel model. Numerical examples based on simulated and measured data recorded from the RUSK vector channel sounder at 2 GHz are presented.

In this paper, new space-time coding schemes for multiple transmit and receive antenna systems (MIMO) are proposed. By applying a specific block coding scheme for transmission, the MIMO channel is structured in an advantageous manner. Diversity can be gained simultaneously with savings in receiver complexity. In a second step, such structured schemes and BLAST schemes are combined, offering and continuous trade-off between quality of service (QoS) and choice of data rate. Due to the simplicity of the coding schemes, only very moderate modifications in the existing UMTS standard are required in order to support them. If the transmitter knows the numbers of antenna elements at both sides of the radio link then this is sufficient for selecting the most appropriate scheme.

Multi-carrier code division multiple access (MC-CDMA) systems are under intense investigation for high bit rate wireless communications systems. Their equalization is based on the fast Fourier transform, allowing for an efficient implementation. Iterative receivers with joint detection and decoding have been shown to achieve very good performance for direct-sequence (DS)-CDMA systems. We apply this concept to MC-CDMA, the multiuser detector is implemented as parallel interference canceller with post-minimum mean squared error filtering. In this contribution a new pilot based channel estimation scheme based on random time sequences is developed. The presented simulation results for a multi path scenario show, that in a fully loaded system with 64 users the single user bound can be approached up to 1 dB. A bit error rate (BER) of 10/sup -3/ is reached already at an E/sub b//N/sub 0/ of 12 dB with a 4 state, rate 1/2 convolutional code.

Environmental parameter estimation for a shallow ocean is addressed by using wideband shipping noise as a source of acoustic energy. Unknown locations of the broadband acoustic sources are estimated simultaneously with the ocean depth using the approximate conditional maximum likelihood estimator (CMLE). This procedure is tested via computer simulations and applied to the experimental hydrophone towed array data.

The performance of high-resolution direction finding methods degrades in several practical situations where the wavefronts have imperfect spatial coherence. The original solution to this problem was proposed by Paulraj and Kailath (1988), but their technique requires a priori knowledge of the matrix characterizing the loss of wavefront coherence along the array aperture. A novel solution to this problem is proposed, which does not require a priori knowledge of the spatial coherence matrix. Our technique is based on the multidimensional minimization of the appropriate concentrated cost function using the genetic algorithm (GA).

Matched field processing (MFP) refers to signal and array processing techniques in which, rather than a planewave arrival model, complex-valued (amplitude and phase) field predictions for propagating signals are used. Matched field processing has been successfully applied in ocean acoustics. In this paper the extension of MFP to the electromagnetic domain, i.e., electromagnetic (EM) MFP (EM-MFP) is described. Simulations of EM-MFP in the tropospheric setting suggest that, under suitable conditions, EM-MFP methods can enable EM sources to be both detected/localized and used as sources of opportunity for estimating the environmental parameters that determine EM propagation.

A generalized likelihood ratio test is considered for testing acoustic environmental models with application to parameter inversion using an acoustic propagation code. In the following, we use the term "hierarchy of models" to denote a sequence of model structures M/sub 1/, M/sub 2/, ... in which each particular model structure M/sub m/ contains all previous ones as special cases. We propose a combined parameter estimation and multiple sequential test for simultaneously determining the model order and its parameters: given the observed data, how many parameters should be included in the model? The last question is important for the order selection problem in hierarchies of models with increasing number of parameters where the observations are corrupted by additive noise. Monte Carlo simulations show the behaviour of the sequential test for selecting a model order as a function of the SNR. Finally, the test is applied to broadband data measured using a vertical array near the island of Elba in the Mediterranean Sea and compared with Akaike's information criterion.

The estimation of source and environmental parameters for stationary broadband wave propagation in an unknown shallow ocean is considered. We apply an approximate maximum-likelihood technique (AMLE) in the frequency domain after cleaning the observations from outliers using robust prewhitening. This method is compared with the previously used multitaper techniques. Environmental noise in data obtained by horizontally towed arrays just beneath the ocean surface has been observed to be more spiky than Gaussian due to the sound originating from individual breaking waves. Therefore, we apply the robust-resistant spectral methods introduced by Martin and Thomson (1982). The advantage of this approach is the physically and statistically justified full broadband estimator. Simulations show the feasibility of the presented approach. Finally, the algorithm is applied to real sonar data recorded by a horizontally towed receiver array in the Baltic Sea in order to estimate the ocean depth and source bearings and to interpret the impinging signals.

Addresses the simultaneous detection and classification of signal arrivals, as well as the estimation of source parameters of signals impinging on an array of sensors. The authors develop a procedure for short-time stationary broadband signal propagation in a shallow ocean. The procedure is applied to sensor data obtained from a towed horizontal receiver array in the Baltic Sea for interpreting the impinging signals. The authors apply a multiple test procedure to three propagation models: a Green's function model, uncorrelated normal modes and plane waves. They apply an F-test which calculates signal to noise ratios for the three models, and select the model with highest SNR. Thereby, they are able to classify previous phantom bearing estimations as uncorrelated normal mode propagation originating from the towing ship itself.

We present an analysis of the interference coherence effects on the performance of an antenna array processor, which maximizes the output signal-to-noise ratio (SNR) for a coherent wave. The analytical expression for the optimal weight vector, directional pattern and output SNR is derived as a function of the coherence coefficient, interference power and parameters of the adaptive processor. We show how different models of interference coherence affect the array performance. Finally, to illustrate the theory, several numerical results are given.

The estimation of source and environmental parameters for stationary broadband wave propagation in a slowly time-varying shallow ocean environment is considered. The "model mismatch" problem is avoided by joint estimation of the waveguide and source parameters by an approximate maximum-likelihood function. The advantage of the approach is the physically and statistically justified incorporation of broadband data into the model and reducing the dimension of the parameter space for the required maximization of the log-likelihood function. The algorithm is applied to real sonar data recorded by a horizontally towed receiver array in the Baltic Sea for interpreting the impinging signals.>