A ubiquitous and high-performance sensing service is required in the next generation of wireless communication systems. The legacy Sub-6GHz bands can provide a larger sensing range, but the available bandwidth limits the resolution. In contrast, the millimeter-wave (mmWave) frequencies enhance the sensing performance with wider bandwidth. However, high-directional beamforming poses new challenges in passive target surveillance. The conventional beam sweeping in mm Wave is costly due to the significant overhead, which conflicts with the goal of communication on efficient data transmission. To reduce the overhead caused by target surveillance, we propose a dual-band sensing approach. By combining the advantages of both Sub-6GHz and mmWave frequencies, our method enables fine target detection in a wide area. Compared to beam sweeping, the pro-posed method significantly reduces the communication overhead and time consumption, while also lightening the computational load of radar signal processing.

Utilizing deep learning (DL) techniques for radio-based positioning of user equipment (UE) through channel state information (CSI) fingerprints has demonstrated significant potential. DL models can extract complex characteristics from the CSI fingerprints of a particular environment and accurately predict the position of a UE. Nonetheless, the effectiveness of the DL model trained on CSI fingerprints is highly dependent on the particular training environment, limiting the trained model’s applicability across different environments. This paper proposes a novel DL model structure consisting of two parts, where the first part aims at identifying features that are independent from any specific environment, while the second part combines those features in an environment specific way with the goal of positioning. To train such a two-part model, we propose the multi-environment meta-learning (MEML) approach for the first part to facilitate training across various environments, while the second part of the model is trained solely on data from a specific environment. Our findings indicate that employing the MEML approach for initializing the weights of the DL model for a new unseen environment significantly boosts the accuracy of UE positioning in the new target environment as well the reliability of its uncertainty estimation. This method outperforms traditional transfer learning methods, whether direct transfer learning (DTL) between environments or completely training from scratch with data from a new environment. The proposed approach is verified with real measurements for both line-of-sight (LOS) and non-LOS (NLOS) environments.

User positioning with deep learning (DL) models based on channel state information (CSI) fingerprints, e.g., obtained at a base station (BS), has emerged as a promising technology. Related prior works generally assume a CSI fingerprint with multiple spatial dimensions (i.e antennas or beams) at the BS but only a single spatial dimension at the user equipment (UE). However, a UE may be equipped with multiple antennas or may need to perform beamforming, e.g., to support transmissions at higher frequencies. In this work we consider user positioning with DL models based on uplink beamformed CSI fingerprints considering multiple spatial dimensions at both the BS and the UE. By considering a single or multiple beams at the BS and UE, the use of different CSI fingerprints is proposed. The positioning accuracy achieved with the different beamformed CSI fingerprints is evaluated and compared. The different orientation during training and UE deployment is also considered. In addition, we also consider the positioning of UEs with different spatial capabilities, i.e. with different number of beams. This work provides valuable insights into the design of wireless positioning with CSI fingerprints considering multiple spatial dimensions at both the BS and UE.

Radio based positioning of a user equipment (UE) based on deep learning (DL) methods using channel state information (CSI) fingerprints have shown promising results. DL models are able to capture complex properties embedded in the CSI about a particular environment and map UE’s CSI to the UE’s position. However, the CSI fingerprints and the DL models trained on such fingerprints are highly dependent on a particular propagation environment, which generally limits the transfer of knowledge of the DL models from one environment to another. In this paper, we propose a DL model consisting of two parts: the first part aims to learn environment independent features while the second part combines those features depending on the particular environment. To improve transfer learning, we propose a meta learning scheme for training the first part over multiple environments. We show that for positioning in a new environment, initializing a DL model with the meta learned environment independent function achieves higher UE positioning accuracy compared to regular transfer learning from one environment to the new environment, or compared to training the DL model from scratch with only fingerprints from the new environment. Our proposed scheme is able to create an environment independent function which can embed knowledge from multiple environments and more effectively learn from a new environment.

Deep learning (DL) methods have been recently proposed for user equipment (UE) localization in wireless communication networks, based on the channel state information (CSI) between a UE and multiple base stations (BSs) in the uplink. With the CSI from the available BSs, UE localization can be performed in different ways. On the one hand, a single neural network (NN) can be trained for the UE localization by considering the CSI from all the available BSs as one overall fingerprint of the user’s location. On the other hand, the CSI at each BS can be used to obtain an estimate of the UE’s position with a separate NN at each BS, and then the position estimates of all BSs are combined to obtain an overall estimate of the UE position. In this work, we show that UE localization with the latter approach can achieve a higher positioning accuracy. We propose to consider the uncertainty in the UE localization at each BS, such that overall UE’s position is determined by combining the position estimates of the different BSs based on the uncertainty at each BS. With this approach, a more reliable position estimate can be obtained in case of variations in the channel.

In this paper, we present the ray tracing (RT) simulation in the 3D model of one highly dense clutter industrial hall, which is scanned by laser scanner and reconstructed based on accurate point cloud. The whole processing chain from the scanning of the physical environment to running the simulation is presented in detail. To validate the simulation results, the synthetic channel characteristics and large-scale parameters, including delay spread (DS), angular spread (AS) and path loss (PL), are compared with those obtained from channel sounding measurement in both LOS and NLOS cases, at 6.75 GHz, 30 GHz and 60 GHz. The simulation results show that some scatters are significant in all bands and may be well identified and tracked. This indicates that our target to generate a deterministic channel model or a hybrid channel model at multi-band for industrial scenario may be possible.

The lower THz bands are foreseen as candidates to achieve the data-rates demanded in the sixth generation (6G) of wireless communications. Hence, multiple measurements for characterization of propagation at these frequencies in different scenarios are being conducted all around the world. This also impulses activities on standardization of measurement equipment and methodologies to define common practices and make measurements from different actors comparable. Therefore, in the present paper we investigate the accuracy of a dual-polarized ultra-wideband channel sounder in the time, angular, and polarization domains by means of a verification methodology based on interferometry.

Single side mMIMO channel measurements over 100 MHz bandwidth at 3.75 GHz were performed in two German cities. In dense urban as well as urban/suburban environments, a total track length of about 20km has been measured. Multipath parameters by means of the high-resolution parameter estimator RIMAX are estimated and subsequently used to derive the statistics of large scale parameters and to allow a comparison to the statistics from standard channel model (3GPP, WINNER). While most of the parameters match quite well, for the angular spread at the base station (BS) partly larger deviations are observed. The correlation properties between the 32 dual polarized antenna patches of the BS antenna are investigated with respect to the antenna patch separation as well as to the distance between the BS and user equipment (UE).

In this paper we study the use of the Channel State Information (CSI) as fingerprint inputs of a Convolutional Neural Network (CNN) for localization. We examine whether the CSI can be used as a distinct fingerprint corresponding to a single position by considering the inconsistencies with its raw phase that cause the CSI to be unreliable. We propose two methods to produce reliable fingerprints including the phase information. Furthermore, we examine the structure of the CNN and more specifically the impact of pooling on the positioning performance, and show that pooling over the subcarriers can be more beneficial than over the antennas.

Research on non-line-of-sight emitter localization relies almost exclusively on simulation data for the evaluation of proposed algorithms. This paper aims to encourage the use of measurements by introducing a setup based on off-the-shelf components that enables 1.5 ns synchronization accuracy and 2 cm ground truth accuracy for multiple distributed receivers and a mobile transmitter. Results from a measurement campaign in a 16 km2urban area are used to validate the system's performance.

We propose a new method to enhance the performance of radio frequency localization in strong multipath and non-line-of-sight (NLOS) situations. The knowledge about the geometrical structure of multipath propagation environment is exploited by using a ray-tracing tool. We further apply the Random Forest (RF) algorithm embedded in a machine learning framework to extract a reference data-set of Time Differences of Arrival (TDOA) fingerprints in multipath outdoor scenarios. Site-specific fingerprints are processed with a multidimensional cross-correlation, called Volume Cross-Correlation function (VCC), to extract the multipath features from measurements. The performance and feasibility of our method was evaluated by simulations and measurements.

In multiuser multiple-input multiple-output (MU-MIMO) systems, channel correlation is detrimental to system performance. We demonstrate that widely used, yet overly simplified, correlation models that generate identical correlation profiles for each terminal tend to severely underestimate the system performance. In sharp contrast, more physically motivated models that capture variations in the power angular spectra across multiple terminals, generate diverse correlation patterns. This has a significant impact on the system performance. Assuming correlated Rayleigh fading and downlink zero-forcing precoding, tight closed-form approximations for the average signal- to-noise-ratio, and ergodic sum spectral efficiency are derived. Our expressions provide clear insights into the impact of diverse correlation patterns on the above performance metrics. Unlike previous works, the correlation models are parameterized with measured data from a recent 2.53 GHz urban macrocellular campaign in Cologne, Germany. Overall, results from this paper can be treated as a timely re-calibration of performance expectations from practical MU-MIMO systems.

Spatial correlation across an antenna array is known to be detrimental to the terminal signal-to- interference-plus-noise-ratio (SINR) and system spectral efficiency. For a downlink multiuser multiple-input multiple-output system (MU-MIMO), we show that the widely used, yet overly simplified, correlation models which generate fixed correlation patterns for all terminals tend to underestimate the system performance. This is in contrast to more sophisticated, yet physically motivated, remote scattering models that generate variations in the correlation structure across multiple terminals. The remote scattering models are parameterized with measured data from a recent 2.53 GHz urban macrocellular channel measurement campaign in Cologne, Germany. Assuming spatially correlated Ricean fading, with maximum-ratio transmission precoding, tight closed-form approximations to the expected (average) SINR, and ergodic sum spectral efficiency are derived. The expressions provide clear insights into the impact of variable correlation patterns on the above performance metrics. Our results demonstrate the sensitivity of the MU-MIMO performance to different correlation models, and provide a cautionary tale of its impact.

We perform simultaneous vehicle-to-vehicle (V2V) multi-band double-directional measurements at 6.75 GHz, 30 GHz, and 60 GHz in a street canyon environment. We analyse both line-of-sight channels and those obstructed by vehicle blocker. For the different bands, we analyse delay spread, angular spread, vehicle blockage loss, and path loss. Finally, we discuss how the system aspects influence the radio channel.

Reliable and accurate multipath clustering results are essential to design and parameterize geometry based (stochastic) channel models proposed by organizations such as 3GPP, COST, WINNER and others. Clustering within wireless channel models assumes that multipath components arrive/depart with similar properties in the considered parameter domains. We propose a new approach combining tracking and initialization of the classic K-Means clustering algorithm. The results show a significantly improved consistency for cluster estimation, which subsequently allows for analyzing the lifetime of these clusters. Finally, we apply the proposed approach on two urban macro channel sounding datasets and compare them to the current 3GPP standard. We found that the angular parameters are rather similar to the standard whereas the cluster delay spreads and the number of rays per cluster differ significantly.

The small scale fading measurements at the 60 GHz frequency band are carried out to investigate the second-order statistics of a fading multipath cluster. The cluster is illuminated with different gain antennas (emulated beamforming gains). The analysis reveal that the measured channel impulse response (CIR) is approximately circular (or circularly symmetric) with correlated inphase (I) and quadrature (Q) components. These properties correspond to the sparse multipath channels and are different from the Rayleigh/Rician fading channels which assume that the I and Q components are mutually uncorrelated colored Gaussian variates. These differences prevent the application of the Rayleigh/Rice fading models in the intra-cluster multipath modeling.

This paper presents a localization procedure to locate UAV in an urban scenario and improve the Time Difference of Arrival (TDoA) performance in Non-line of Sight (NLoS) situation. The idea is to use ray-tracing simulation and Time Difference of Arrival (TDoA) to build a geographic information database that reproduces the “site-specific” multipath patterns that enhance the position estimation performance in multipath scenarios. The conventional emitter location systems based on TDoA sensors mitigate errors trying to avoid NLoS measurements, while the proposed method, uses the multipath information to extract the time of arrival characteristic from the environment. A measurement campaign was done to validate the technique and showed an enhancement in the localization processing, depending on the precision of the buildings and obstacles information.

Identifying the precise location of a vehicular mobile is a challenging task because satellite navigation system standalone is unable to give the expected performance in an Urban propagation scenario. This paper proposes a novel technique which integrates ray-tracing simulation tool and Channel Impulse Response (CIR) estimation and improves the vehicular location even in a Dense Multipath (DM) scenario. In this paper, we try to overcome these limitations and propose a novel technique of using the Ray-tracing simulation tool to build a radio frequency map of CIE of every point in the simulation domain. Finally, we match the multipath components drawn estimated to a defined location. The proposed scheme is comparatively much accurate to other state-of-art techniques for position estimation.

Multidimensional characterization of outdoor urban macrocellular propagation channels is essential for the analysis and design of next-generation (5G and beyond) cellular massive MIMO (multiple-input-multiple-output) systems. Since most massive MIMO arrays will extend in two or three dimensions, an understanding of 3-D parameters (i.e., azimuth and elevation) of the multipath components (MPCs) is required. This paper presents an extensive measurement campaign for 3-D outdoor propagation channels in an urban macrocellular environment. Measurements were performed with a 20 MHz wideband polarimetric MIMO channel sounder centered at 2.53 GHz and MPCs were extracted using RIMAX-an iterative maximum likelihood algorithm. The physical propagation mechanisms of the observed discrete MPCs are explained in terms of waveguiding, over-the-rooftop propagation, and scattering by far-away objects. MPCs exhibit clustering in the temporal and spatial domains; both intra- and inter-cluster parameters and their relevant statistics are provided. We also extract diffuse MPCs, show that they can comprise a moderate portion of the overall energy, and provide a statistical characterization.

Large absolute channel bandwidths at millimeter wave (mmWave) frequencies and more directional antenna gains may lead to quasi-deterministic (less random) channel fading behavior. Randomness in the cross polarization discrimination (XPD) is an attribute related to the channel fading behavior. In narrow-band channels, it is usually modeled as a log-normal random variable. From channel modeling perspectives, XPD power level in a particular propagation environment and its degree of randomness are of fundamental interest. In this paper, XPD behavior is characterized as a function of bandwidth in 4 different propagation setups including line of sight (LOS), reflections from black board, a rough wall and double bounce reflections from both surfaces. It has been observed that in all cases, the coefficient of variation of XPD around its mean value reduces exponentially with an increase in channel bandwidth demonstrating an evolution towards quasi-deterministic channel behavior. Additionally, a 5-10 dB difference in XPD values has also been observed between narrow-band and UWB channels in the considered propagation setups.

In this paper, directional small scale fading measurements are used to extract the fading statistics of the received signal magnitude and the parameters of the underlying stochastic (Rayleigh/Rician) model. After that sum-of-cisoids (SOC) principle is used to model the deterministic samples (channel snapshots) of the stochastic model with a special focus on the number of sinusoids/scatterers and their weights. It has been established that theoretical and simulated results of the SOC fading channel simulator provide an excellent approximation of the measured received signal magnitude per cluster with a small number of sinusoids i.e. $N=4$.

Compressed sensing (CS) is a sampling paradigm that allows us to simultaneously measure and compress signals that are sparse or compressible in some domain. The choice of a sensing matrix that carries out the measurement has a defining impact on the system performance and it is often advocated to draw its elements randomly. It has been noted that in the presence of input (signal) noise, the application of the sensing matrix causes signal-to-noise ratio (SNR) degradation due to the noise folding effect. In fact, it might also result in the variations of the output SNR in compressive measurements over the support of the input signal, potentially resulting in unexpected nonuniform system performance. In this letter, we study the impact of a distribution from which the elements of a sensing matrix are drawn on the spread of the output SNR. We derive analytic expressions for several common types of sensing matrices and show that the SNR spread grows with the decrease of the number of measurements. This makes its negative effect especially pronounced for high compression rates that are often of interest in CS.

The 3-D multiple-input multiple-output (3-D MIMO) systems have received great interest recently because of the spatial diversity advantage and capability for full-dimensional beamforming, making them promising candidates for practical realization of massive MIMO. In this paper, we present a low-cost test equipment (channel sounder) and post-processing algorithms suitable for investigating the 3-D MIMO channels as well as the results from a measurement campaign for obtaining elevation and azimuth characteristics in an outdoor-to-indoor (O2I) environment. Due to limitations in available antenna switches, our channel sounder consists of a hybrid switched/virtual cylindrical array with effectively 480 antenna elements at the base station. The virtual setup increased the overall MIMO measurement duration, thereby introducing phase drift errors in the measurements. Using reference antenna measurements, we estimate and correct for the phase errors during post-processing. We provide the elevation and azimuth angular spreads for the measurements done in an urban macro-cellular and urban micro-cellular environments, and study their dependence on the user equipment (UE) height. Based on the measurements done with UE placed on different floors, we study the feasibility of separating users in the elevation domain. The measured channel impulse responses are also used to study the channel hardening aspects of the massive MIMO and the optimality of the maximum ratio combining receiver.

Wideband spectrum sensing plays a crucial role in a number of applications among which the cognitive radio (CR) is one of the most prominent. In this work we consider a scenario where the wide band of interest is comprised of multiple communication channels occupied by several independent transmissions. Due to the propagation conditions, some of the transmissions can cause potential interference by occupying the same channel at different locations. In order to alleviate the effect of such interference, we employ a network of distributed sensing nodes that sample the wideband signal at sub-Nyquist rate and share the acquired data with the fusion center. We show that using the structure of the correlations between the sub-Nyquist samples obtained at different sensors, we can detect the presence of potential in-channel interference. We present a concrete approach for estimation of the central frequencies of the channels in which it occurs and demonstrate its effectiveness in simulations.

3-dimensional Multiple-Input Multiple-Output (3D MIMO) systems have received great interest recently because of the spatial diversity advantage and capability for full-dimensional beamforming, making them promising candidates for practical realization of massive MIMO. For 3D MIMO system design, it is important to have full characterization of the 3D MIMO propagation channel, i.e., characterize both the elevation and azimuth characteristics of the wireless propagation channel, especially at the base station end. In this paper, we present first results from a measurement campaign for obtaining these characteristics. For those measurements we use a hybrid switched/virtual cylindrical array with 480 antenna elements at the base station (BS), and a switched array with 24 antenna elements at the user equipment (UE). We perform outdoor-to-indoor (O2I) channel measurements in an urban macro-cellular (UMa) and a micro-cellular (UMi) environments. We provide the elevation and azimuth angular spreads at the transmitter and receiver, and study their dependence on the UE height. With the increase in the UE height, the BS elevation spreads decreased from 1.24 deg to 1 deg and 1.15 deg to 0.78 deg respectively for UMa and UMi; the azimuth spreads remained approximately the same (7.5 deg). Based on the measurements done with the UE placed on different floors, we study the feasibility of separating users in the elevation domain. Users were separable in 44% and 54.17% scenarios respectively for the UMa and UMi environments.

The application of the Compressed Sensing (CS) paradigm to the sampling of sparse wireless signals allows a significant reduction of the sampling rate compared to the one dictated by the Nyquist sampling theorem. The majority of the theoretical results derived within CS are expressed in terms of the known sparsity order of the signal. In this work we address the problem of sparsity order estimation of multiband signals with unknown sparse spectral supports. We show that it can be estimated directly in the compressed domain as the dimension of the signal subspace of the observations' covariance matrix. We analyze how the results of the sparsity estimation can be utilized during the reconstruction step and which requirements it imposes on the performance of the subspace estimation algorithms. The results of the numerical study demonstrate that the reconstruction step is particularly sensitive to type II errors. This in turn indicates that the classical non-parametric model order selection algorithms might be unfavorable for this application since they tend to underestimate model order in the low SNR regime. As a remedy we propose to apply parametric approaches that allow to compromise resulting probabilities of over- and underestimation.

Double-directional, ultra wideband dual-polarized channel measurements at 70 GHz have been carried out in a small-office environment. The aim of these measurements is to study the temporal, directional, and polarization properties of the channel for modelling purposes of the future wireless communication systems at mm-waves. A highly deterministic behaviour of the channel and the environment has been identified, as well as the limitations of only parameterizing channel models as WINNER for the new mm-wave channel models.

Existing fast antenna selection algorithms have either low performance gains or they are efficient only for the selection of particular size sub-matrices. Our motivation to the study of NP-Hard antenna selection problem is two-fold. First, we intend to propose an optimal algorithm that is equally efficient for each possible subset cardinality of the transmit and/or receive antennas. Second, different from any of the antenna selection algorithms proposed so far, our algorithm deterministically increases the strength of the singular values during the selection of each column of the selected matrix. This increase in the singular values is defined by a very tight bound. This bound has previously been used to analyze the efficiency of low rank approximation algorithms. Hence, our antenna selection procedure is as efficient as that of a best algorithm for matrix low rank approximations.

When a MIMO wireless channel matrix H is rank deficient, it is often required to select a submatrix Hp ϵ H such that the columns of Hp lie close to the optimal subspace that constructs H. Main motivation for the selection of Hp is to gain the same spectral efficiency benefits as that of H, but at a lower computational and RF hardware cost. In this contribution, we first present an analogy between the antenna and user selection problems. Secondly, a matrix volume maximization criterion that is applicable to both problems have been presented. We have proposed a low complexity deterministic algorithm for the selection of antennas/users such that the volume of Hp is maximum. We have shown that under particular channel propagation setup, smaller subsets can provide the same spectral efficiency as that of the full MIMO channel. We used a 3D extension of the WINNER channel model at center frequency 2.53 GHz to evaluate the performance of the proposed algorithm.

This work studies the dependency of MIMO performance figures on two characteristic multipath cluster parameters, the innercluster Angular Spread of Arrival (ASA) and the number of clusters. Both these parameters play a vital role in geometric based stochastic channel models as from the WINNER or COST families. Our motivation to the study these parameters is two-fold. First, we study their impact on the changing statistics of channel transmit and receive side correlations. Second, we will investigate their impact on the single user MIMO channel capacity. We present a detailed analysis for different MIMO order setups with both single as well as dual polarized antenna elements under Line-of-Sight (LOS) and Non-LOS (NLOS) propagation conditions. One key result is, that the number of clusters is more dominant than the angular spread of it. This empirical investigation considered a 3D extension of the WINNER channel model and the urban macro cell scenario at a center frequency of 2.53 GHz.

A wideband, fully polarimetric and directional radio channel sounding campaign conducted at 70 GHz is presented. Measurement site was chosen as an entrance hall, featuring femtocell size. Transmitter and receiver were placed according to an access-point scenario. Initial large-scale parameters like excess-delay, delay-spread and angular spread are derived for channel characterisation. It is not the paper's goal to describe a new channel model. The main focus is the identification of propagation effects and characteristics, which have to be considered in future modelling. It was found, that first more specular than diffuse components occur and second the channel is spatially sparse.

Owing to the limitation in bandwidth of currently used frequency bands, future fifth generation (5G) mobile networks will need to operate at higher frequencies. However, one important step in the research activities for the next generation of mobile networks is the broadband fully polarimetric analysis of the multipath propagation in dynamic single and multiuser scenarios under time-variant shadowing environments in millimetre- wave (mm-wave) channels. This paper presents a channel sounding campaign at 70 GHz in an urban outdoor scenario focusing on broadband channel characteristic at mmwave. For the measurements a novel dual-polarized (DP) ultrawideband (UWB) multi-channel sounder (DP-UMCS) architecture, developed at the TU Ilmenau for propagation measurements in cellular networks, was used to study the directional and polarization properties of the channel in view of the future wireless communication systems.

In this paper, we present a new channel model for mobile-to-mobile (M2M) channels based on extensive measurements conducted at 30MHz–400MHz in various environments. We use the tapped-delay-line model and determine the discrete tabs by distributing scatterers on ellipses in the environment. For a distinctive characterization of the M2M channel we extract channel parameters from the measurements using the high-resolution parameter estimation algorithm RIMAX and perform a cluster algorithm for statistical analysis. In order to make the measurement results accessible for simulations we derive a cluster-based statistical parametrization of the model. We perform simulations to compare the results with measurements and show that the model is well-suited for simulating M2M channels.

Compressed Sensing (CS) has been recently applied to direction of arrival (DOA) estimation, leveraging the fact that a superposition of planar wavefronts corresponds to a sparse angular power spectrum. However, to apply the CS framework we need to construct a finite dictionary by sampling the angular domain with a predefined sampling grid. Therefore, the target locations are almost surely not located exactly on a subset of these grid points. This leads to a model mismatch which deteriorates the performance of the estimators. In this paper we take an analytical approach to investigate the effect of such grid offsets on the recovered spectra. We show that each off-grid source can be well approximated by the closest two neighboring points on the grid. We propose a simple and efficient scheme to estimate the grid offset for a single source or multiple well-separated sources. We also discuss a numerical procedure for the joint estimation of the grid offsets of closer sources. Simulation results demonstrate the effectiveness of the proposed methods.

This paper provides an overview of over-the-air (OTA) testing for next generation communication devices with special consideration of Car-to-Car (C2C) and Car-to-Infrastructure (C2I) communication. Existing state-of-the-art techniques, their merits, and limitations are discussed. We identify the requirements for the OTA test in virtual electromagnetic environment (OTAinVEE) for the C2C/C2I communication scenario, where interference emulation becomes a highly challenging task. We propose systematic test procedure and closed-loop OTA set-up applicable for such scenario. Since the main test challenge in C2C/C2I communication is the modeling of highly time variant channel, we discuss channel modeling concepts and propose a framework appropriate for such communication. The ongoing activities in Ilmenau towards simulation and testing for C2C/C2I communication is also briefly discussed.

Within this contribution the robustness and reliability of MIMO order (MOS) and antenna subset selection (AS) procedures has been studied. We considered AS for spatial multiplex (SM) systems, where the total power is distributed equally among selected antennas. MIMO channel data sets from a channel sounding campaign in an urban macro cell scenario have been used for advanced performance evaluation. At first, we studied correlation characteristics along various measurement tracks with single and dual polarized antenna configurations. We found that even under NLOS high correlation between antenna elements appears. Based on channel correlation at the transmit side and the receive SNR we discussed different methods for adaptive switching of the number of active RF modules and followed by optimized antenna subset selection (AS). Until a certain SNR threshold our novel low complex proposals perfectly follow the known algorithms from literature.

In this paper, we study the spatial structure of multiple-input multiple-output (MIMO) links in small urban macro-cells at 2.6 GHz. We resolve the double-directional structure of the radio channel. Also we compute common metrics used to characterize MIMO links, i.e. the structure of singular values and the resulting capacity. In a line-of-sight (LOS) link, we find that local scattering is not enough to create full-rank MIMO channels. Even behind a sector, we observe a low-rank channel. In a non-line-of-sight (NLOS) scenario, although we resolve several multi-paths with individual delay, direction of departure (DoD) and direction of arrival (DoA), the impact of local scattering is limited. Singular values indicate few more degrees of freedom for NLOS channels, but less than for a random matrix. We can model the spatial degrees of freedom in small cells better by assuming a random but small number of nearly specular paths feeding some local scattering widely spread in azimuth and elevation. Based on our results, we discuss modeling aspects and the value of large antenna arrays in mobile networks.

Spectrum sensing plays a key role for radio resource awareness in cognitive radio. To enhance the capabilities of cognitive radio nodes, exploiting the spatial resource in addition to frequency and time resources seems reasonable. In this paper we investigate the impact of using the spatial information from a sector antenna on sensing reliability as well as on availability of opportunity for secondary communication in cognitive radio. A ray tracer is used for modeling the urban environment and for wave propagation simulation. The power angular profiles obtained at different locations are further processed with MATLAB using measured and analytical radiation beam patterns to evaluate opportunity and detection performance. The measured beam patterns are obtained from a sector antenna specifically devised for exploiting spatial resources, whereas the analytical patterns are obtained from mathematical expressions representing three dipole elements in a circular array. The performance is compared to that obtained with an omni-directional antenna. The results show that exploiting the spatial dimension in spectrum sensing in cognitive radio improves the performance in terms of increased opportunities for secondary communications as well as the better detection of primary signals.

This paper investigates the performance of energy detection (ED) for random signals having sufficient autocorrelation. As opposed to the uncorrelated noise, practical communication signals have autocorrelation, which can be exploited for optimal detection if the correlation is known a priori. Otherwise ED can be employed as a generalized sensing method for unknown signals. However, since ED is optimal only when signal samples are uncorrelated, its performance degrades due to the correlation present. We investigate the detection performance of ED both analytically and by simulation under the assumption of Gaussian signal having exponential autocorrelation function. Analysis and simulation show that the performance degradation is negligible for low signal-to-noise ratio (SNR) levels, and also for the first-lag correlation less than 0.5. For highly correlated signals, the degradation is pronounced when SNR is above -12 dB.

In wireless communication systems, dual-polarized (DP) instead of single-polarized (SP) multiple-input multiple-output (MIMO) transmission is used to improve the spectral efficiency under certain conditions on the channel and the signal-to-noise ratio (SNR). In order to identify these conditions, we first propose a novel channel model for DP mobile Ricean MIMO channels for which statistical channel parameters are readily obtained from a moment-based channel decomposition. Second, we derive an approximation of the mutual information (MI), which can be expressed as a function of those statistical channel parameters. Based on this approximation, we characterize the required SNR for a DP MIMO system to outperform an SP MIMO system in terms of the MI. Finally, we apply our results to channel measurements at 2.53 GHz. We find that, using the proposed channel decomposition and the approximation of the MI, we are able to reproduce the SNR values above which DP MIMO systems outperform SP MIMO systems.

Ambiguity function (AF) provides an effective way for analysis of the resolving performance of a radar system. With the development of ultra-wideband (UWB) and multiple-input-multiple-output (MIMO) radio techniques, recently, the combination of them-the concept of UWB MIMO radar has been proposed and received much attention. For a UWB MIMO radar, first, the signal waveform with an exponential expression which is widely adopted in narrowband cases is not appropriate anymore. Second, the MIMO radar sensors are typically organized in a certain spatial topology, which greatly impacts the performance of the MIMO system. In addition, for a typical MIMO radar application scenario, generally, there are relative motions existing between the radar sensors and the targets. The presence of the relative motions would further complicate the performance analysis of the system. In fact, for a MIMO radar, the overall performance of the system is jointly determined by the parameters, such as the signal waveform, the topology, and the relative motions. Each of the parameters impacts the resolving performance in a particular way. In order to evaluate the contribution of each individual parameter of interest, in this paper, a UWB MIMO AF is developed and factorized into several factors. By this means, each individual parameter can be evaluated via the corresponding factor to examine a certain specific characteristic of the system, instead of via the complicated whole MIMO AFs.

Dual-polarized (DP) multiple-input multiple-output (MIMO) systems are known to be beneficial in terms of the spectral efficiency under certain conditions on the channel and the signal-to-noise ratio (SNR). In order to assess under which conditions DP antenna setups are advantageous over single-polarized (SP) antenna setups, an accurate yet analytically tractable modeling of such channels is a necessary but challenging task. We propose a novel channel model for DP mobile Ricean MIMO channels for which the statistical channel parameters are readily obtained from a moment-based channel decomposition. An approximate evaluation of the mutual information (MI), expressed as a function of the statistical channel parameters of the proposed channel model, is derived. This approximation allows to assess when it is beneficial in terms of the MI to switch from an SP to a DP setup. Finally, we evaluate the MI for selected SP and DP setups based on channel measurements at 2.53 GHz. We find that the DP setup only provides an improvement in terms of the MI in medium- to high-K-factor scenarios above a certain SNR. The approximate evaluation of the MI is able to accurately reproduce the SNR at which one should switch from an SP to the DP setup.

We propose a general channel model for mobile Ricean multiple-input multiple-output (MIMO) channels, which is characterized by parameters that are readily obtainable from measurements. To this end, a moment-based channel decomposition is derived. For the case of statistical channel state information (CSI) at the transmitter (TX) and instantaneous CSI at the receiver, we derive an approximation of the achievable rate, i.e., the mutual information (MI), which is only a function of the channel parameters of the proposed channel model and thus gives insight into the parameters that influence the MI. Finally, we evaluate the MI for a 4 × 4 MIMO system based on channel measurements at 2.53 GHz for two low-complexity TX strategies, i.e., a beamforming (BF) and a spatial multiplexing (SM) strategy. We find that the proposed channel model and the approximate evaluation of the MI are both accurate for realistic signal-to-noise ratio (SNR) values. The approximate evaluation is able to reproduce crossing points between the MI of the considered TX strategies; it thus reflects the SNR at which one should switch from the BF to the SM strategy.

In this paper the principles of polarimetric Ultrawideband Radar are discussed. First an overview of the state of the art and the theory is given. The calibration principles of polarized UWB Radar data as well as possible imaging algorithms are discussed. Possible applications are listed and one example, to detect certain targets using a polarimetric MIMO UWB radar by performing an analysis of the radar cross section (RCS) of the individual targets, is shown.

The extension of multiple-input multiple-output (MIMO) systems to the polarization domain is known to have potential benefits in terms of the spectral efficiency. It is thus of importance to understand how the channel influences the spectral efficiency, e.g., when a dual-polarized (DP) antenna setup should be favored over a single-polarized (SP) one. In this work, we study the achievable rate, i.e., the sum mutual information (MI) of all transmitted streams, over DP MIMO channels with linear receivers. We derive an approximation of the sum MI with a linear minimum mean squared error (LMMSE) or a zero forcing receiver which is an explicit function of statistical channel parameters. The evaluations of selected 4×4 SP and DP MIMO setups are based on channel measurements performed at 2.53 GHz in an urban macrocell scenario. The results reveal that DP antenna setups can yield substantial gains in the sum MI on links with high K-factors and high signal-to-noise ratios (SNRs). Moreover, the degradation in the sum MI with two streams due to the use of an LMMSE receiver is found to be small, especially for the DP antenna setup. The approximate evaluation of the sum MI is able to reproduce the SNR threshold region in which a second transmitted stream should be activated or one should switch from an SP to a DP antenna setup.

Interference is one of the major bottlenecks in current cellular networks. A realistic evaluation of the achievable performance in interference-limited systems based on measured channels is thus necessary; however, only few results are known from literature. We evaluate the achievable performance in a cellular network with inter-cell interference based on measured channels in an urban macrocell scenario at 2.53 GHz. To this end, the mutual information is introduced as an appropriate performance measure over fast and slow fading channels that are non-stationary but doubly underspread. We discuss appropriate channel normalizations and the limitations of an evaluation based on sequential measurements. Moreover, we analyze the accuracy of an approximate but commonly used evaluation of the mutual information. A second-order multivariate Taylor series expansion reveals the signal and interference contributions to the approximation error. We find that the approximation is accurate in realistic ICI scenarios.

Channel sounding, channel modeling and their parameter extraction in particular considering MIMO configurations, plays an important role for todays realistic wireless system design and evaluation. The analysis of the gathered channel sounding data has to match the specifics in the measurement system configuration. Within this contribution directional antenna arrays are used at both sides of the measured link. Furthermore, the measurement system applies automatic gain control between subsequent MIMO snapshots and additionally within the MIMO matrix. Both features lead to improved measurement accuracy. A new extension of existing power and delay domain analysis procedures considering the automatic gain control within the MIMO matrix is introduced and evaluated. The results show that delay spread and K-factor are more affected, while shadow fading and cross polarization ratios are almost not.

Extensive analysis of channel measurement data for the investigation of new channel models plays a fundamental role in designing and evaluating todays wireless communication systems. Besides the knowledge of the parameters and their distribution functions, the cross correlation between them is of great interest and can be exploited for more sophisticated channel simulations. In this contribution the cross correlation characteristics of the large scale parameters (LSP) in the power and delay domain are investigated for different parameter estimation procedures (taking the spatial filtering due to directional antennas into account or neglecting it), for various distance ranges between base station (BS) and mobile station (MS) as well as for different antenna heights.

The joint characterization of the multiple links becomes important for mesh topologies, relaying, distributed antennas, cooperative transmission, virtual MIMO etc. For that purpose simultaneous measurements are needed. Since a suited measurement setup requires multiple channel sounders, a very few of such campaigns have been conducted. A single-sounder approach can be considered as an attractive, less expensive alternative as long as environment stationarity can be insured between consecutive measurement runs. This contribution investigates the variability of large-scale parameters, such as delay and directional spreads, derived from measurements at 2.53 GHz in an outdoor macro-cell scenario. A mobile receiver has been moved twice over the same track whereas the transmitter has been fixed. The large-scale parameters are estimated as averages within local stationarity regions corresponding to areas with a constant multi-path structure. The observed differences between two large-scale parameter sets are used to evaluate stationarity of the outdoor environment between repeated measurements. The reasonably low decorrelation levels have indicated that large-scale characterization of multi-link configuration can be performed sequentially with single sounding device.

This paper discusses basic requirements for passive ultra-wideband (UWB) localization systems that aim at home-entertainment applications such as smart audio systems, which can adapt the sound according to the user location, providing an optimum listening experience to the user. We have analyzed over hundred thousand channel impulse responses measured by a real-time UWB channel sounder in different scenarios. Our analyses show that passive localization systems must possess a large real-time dynamic range in order to be capable of detecting a listener. We assume that the listener does not carry any tag and is detected and localized just by electro-magnetic (EM) waves reflected from his body. Such passive localization is demonstrated by a measurement example.

Ultra-wideband (UWB) techniques are recently targeted as a broadband short range solution for in-flight-entertainment (IFE) systems. Despite the in-cabin channel can be considered as an indoor channel, under the current aviation regulations, and due to the usual metal cabins and cylindrical shapes, these scenarios are particular for wireless applications. In this paper is presented an experimental characterization of the UWB channel within an Airbus 319 aircraft cabin based on a real-time multiple-input-multiple-output (MIMO) UWB channel sounder. Here, the delay spread and coherent bandwidth have been calculated for different antennas configurations and distances. Besides, the in-cabin path-loss exponent has been calculated based on an extensively amount of data. Moreover, dynamic range metrics have been introduced for channel analysis and performance comparisons along the cabin.

60 GHz technology has been proposed recently for multi-Giga-bit/s access within different application scenarios, e.g. in crowded multi-user public transportation (PT) environments. Hence, channel sounders that are used to investigate multipath propagation and multi-user access techniques including spatial diversity and beam-forming in time-variant shadowing environments require ultra-wideband (UWB) operation, multiple antennas, and real-time. This paper presents probably the first channel sounder architecture fulfilling these requirements. Moreover, an experimental study is described which is performed for planning purposes and deployment of 60 GHz WLAN/WPAN networks in PT scenarios. Coverage analysis based on different PHY channelization schemes, including multiple access point configurations and antennas is addressed for the Airbus 340 cabin. Additionally, distributions of time dispersion, coherent bandwidth, and Rice K-factor in the in-cabin coverage area are presented. Specifically, a brief analysis under dynamic channel conditions due to human activities is introduced. The suitability of the 60 GHz-UWB channels sounder for PT scenarios is demonstrated. Besides, some channel results for finding the most suitable system parameters for 60 GHz access deployment of in-flight-entertainment (IFE) systems are given.

A common simplification in the treatment of random linear channels is the wide-sense stationary and uncorrelated scattering (WSSUS) assumption. For wireless channels, this assumption is, however, only fulfilled in an approximative sense inside local time-frequency regions. Since algorithms in wireless digital communications often rely on knowledge of second order statistics of the channel, it is important to know the size of local quasi-stationarity regions. Thus, we determine quasi-stationarity regions in distance for an urban macrocell scenario. We observe that, based on the chosen measure and in our specific scenario, the time-frequency properties are dominant compared to the spatial properties in defining the size of quasi-stationarity regions. Furthermore, we find that in some cases the quasi-stationarity regions strongly depend on the mobile terminal orientation.

A common simplification in the treatment of random linear channels is the assumption of stationarity of the channel in time. The wireless channel is, however, known to be inherently non-stationary. We detail a methodology for non-stationarity analysis from an algorithmic perspective. For the determination of local quasi-stationarity regions, we consider a multi-link downlink scenario where multiple base stations use transmit beamforming to concurrently transmit to a single mobile terminal per time slot. We obtain an algorithm-specific measure, i.e., the signal-to-interference-plus-noise ratio (SINR) degradation, with which we evaluate local quasi-stationarity regions. Furthermore, we relate and compare the SINR degradation to the correlation matrix distance (CMD). In an urban macrocell scenario relevant to 3GPP Long Term Evolution (LTE), we find that the resulting local quasi-stationarity distances show the same trends, but that the CMD overestimates the average distances for our system model.

This paper presents results of wide band channel measurements at 2.53 GHz for a representative urban macro cell environment in Ilmenau, Germany. The extensive channel sounding campaign covered the MIMO radio links from 22 mobile tracks to 3 different base stations and 1 relay station. The results presented in this paper provide insight into the large scale parameter analysis of the power, delay domain, including the transmission loss and the statistical distributions of the shadow fading, narrowband k-factor and delay spread. Large scale parameters from angle domain (azimuth and elevation) are derived based on the high resolution multipath parameter estimation (RIMAX). Furthermore the cross correlation of these parameters are investigated and compared to state-of-the-art channel models as from the IST-WINNER.

Novel channel models as from COST 273 and IST-WINNER projects are models to evaluate the performance of multi-antenna concepts under link-level and system-level. For consistent performance evaluation the channel models needed to be parameterized by multipath parameters based on measurements. It seems these parameters can be grouped into geometrically co-located paths, so called clusters. The reliability and reproducibility of the estimated parameter groups, depend inter alia on the decision criterions, initialization and the chosen cluster algorithm itself. In this paper the focus is to analyse the performance of different clustering algorithms and initialization stages. Furthermore an improved initialization approach is presented.

Interdependences of radio-channel model parameters, being observed in some measurement data, should be reproduced by the model. For that purpose the statistical correlation is often used. From the same wideband multiple-input and multiple- output (MIMO) channel data, estimated correlation between large-scale propagation parameters (e.g. delay spread and shadow fading) could be different due to non-uniqueness of post-processing procedure. In this paper, through examining a set of measurement data, we studied the impacts of differently parameterized post-processing procedures of measurement data on the auto- and cross-correlation properties of large scale parameters. We focus on the single parameter in postprocessing procedures: different margins of noise cutting level. The measurement data are gathered in a public hotspot bridge-to-car highway LOS scenario in Ulm, Germany.

The IST-WINNER channel model (WIM) offers a complete channel model description in a sense of large-scale as well as small-scale effects in MIMO radio-channel for B3G system designs. This paper proposes the extension of 1D regular array structures being currently supported within WIM (uniform linear array - ULA) to arbitrary 3D antenna arrays. Arbitrary antenna structures are necessary for both modelling and validation purposes since antenna arrays define the spatial dimension of the radio-channel. General 3D antenna array description also embeds polarization concepts that are becoming important for modelling of 3D rotation of antenna array. In this paper two different strategies of using 3D antenna arrays in channel model simulations are proposed and discussed. Both methods are compared in terms of the computational complexity and their suitability for the current WIM channel model implementation.

This paper is summarizing and comparing properties of channel models used for Beyond-3G (B3G) MIMO simulations: 3GPP spatial channel model (SCM), its extension (SCME), and models developed by WINNER. Compared models are offering complete channel model description in a sense of large-scale as well as small-scale effects in MIMO radio-channel. WINNER targeted model was supposed to provide reliable tool for estimation of system performance, covering frequencies up to 5 GHz and bandwidths of 100 MHz in different types of environment. Since SCM was originally proposed for 2 GHz range and 5 MHz bandwidth, certain extensions (SCME) were necessary. However, SCME performance was restricted since it has been design as backward compatible with SCM. That was the motivation to start using the new WINNER generic channel model, where model parameters are extracted from channel-sounding measurements covering targeted frequency range and bandwidth. This paper describes all important differences and compares features and performances of the models.

Statistical channel models are attractive for their simplicity but sometimes lack in precision. In order to improve modelling accuracy, statistical model parameters, which are dependent on the environment, are extended to include spatial and temporal correlation. In outdoor scenarios these parameters are assumed constant over "large" areas of several wavelengths hence the name large scale parameters. This paper studies the large scale fading (LSF) and the applicability of bringing this previously used outdoor variable to the indoor case. The impacts of the model of the LSF on the outdoor system-performance have been studied and several relevant models have been proposed for outdoor cases. We present the intra-site autocorrelation as well as the inter-site cross correlation of the LSF for an indoor channel. The results are based on two separate measurement campaigns conducted at KTH, Stockholm and TUI, Ilmenau, using a single mobile station (MS) and multiple base stations (BSs) to investigate such models. We observe that the areas under which the LSF could be assumed constant are, in indoor scenarios, so small that it can be assumed independent from one local area to another. Furthermore, we find results that point towards the existence of inter-site correlation in some specific scenarios.

In this paper, we introduce adaptive link control techniques for multiple-input multiple-output (MIMO) systems with broadband single-carrier signaling. Soft cancellation and minimum mean squared error turbo equalization is assumed, where matching between coding and equalization plays crucial roles to achieve high throughput. This paper uses multilevel coded bit-interleaved coded modulation (ML-BICM) with linear mapping as a core part of the transmission, on top of which this paper applies automatic repeat request (ARQ) with adaptive coding (AC) for link control. The reason behind the use of the linear mapping ML-BICM is the separability between the layers in the modulation format: This should bring us a significant benefit in designing the link control strategies because each layer has its own reliability, and the adaptive link control should be optimized based on the each layer's reliabilities. For ARQ with ML-BICM, we introduce layer-by-layer retransmission control, where turbo equalization and retransmission control can be performed independently over the layers because of the layer-separability. The layer-by-layer concept is then applied to AC with ML-BICM, where the code parameters such as code rate and generator polynomials are chosen so that after several iterations the mutual information between the transmitted and the soft-input soft-output decoder output information can reach a value very close to one while minimizing the rate loss due to the mismatch between the equalization and decoder. Extrinsic information transfer analysis is performed for the MIMO channel realization being given. The transmitter is notified of the selected codes for the each layer via the feedback channel, and uses the selected codes for the following transmission, assuming that the channel state information (CSI) stays the same at least over two consecutive frames. Finally, this paper evaluates the throughput performances of the ML-BICM ARQ and AC techniques using multidimensional field measurement data. The performance tendencies are correlated with propagation properties, obtained as results of the high-resolution channel analysis, such as spatial spreads at the transmitter and receiver sides.

This paper investigates the effects of impulse noise on the performance of multidimensional parity check codes (MDPC) in additive white Gaussian noise and Rayleigh fading channels. The Bhattacharya upper bound is derived for impulsive noise and is found to closely match with the simulation results. It is observed that impulse noise can degrade the bit error rate performance by several dBs. Further, the use of incorrect impulse noise model in the receiver decoding algorithm is found to cause severe penalty in fading channels. Our results also show that MDPC codes outperform traditional convolutional codes in impulsive noise channels. The performance of MDPC codes is found to be slightly inferior to turbo codes, when the latter uses slightly lower code rate and higher complexity. The capacity limits for impulsive noise channels are also calculated

Convergence properties of frequency domain turbo equalization with multilevel bit-interleaved coded modulation (MLBICM) and bit-interleaved coded modulation (BICM) are studied in measured quasi-static fading channels by the means of semi-analytical convergence analysis. Spatial channel parameters are matched to the reached mutual information on a per-measurement basis and trends in the behavior of the equalizer are obtained, when the channel characteristics, in terms of transmit and receive side azimuth spreads, change. Robust convergence of frequency-domain turbo equalization combined with multilevel BICM is demonstrated through the convergence analysis. Three channel codes are evaluated in the measurement-based simulation showing the effects of outer code selection

The performance of future wireless communication systems can be highly increased by utilizing multiple antennas at both sides of a wireless communication link. A concept for MIMO double directional wide-band channel measurements is presented and high-resolution channel parameter estimation is applied. Furthermore, a procedure, where MIMO capacities are obtained by one single SISO channel transfer function, is discussed. Finally, a ray-tracing tool is used for comparison.

We measure the capacity of multiple-input multiple-output radio systems in microcellular environments. We use a new data evaluation method that allows to evaluate the cumulative distribution function of the capacity from a single measurement. This method is based on an extraction of the parameters of the multipath components and, thereafter, a synthetic variation of their phases. In the analyzed environments, we find capacities to be about 30% smaller than would be anticipated from an idealized model. In addition, the frequency selectivity of the channel makes the CDF of the capacity steeper and, thus, increases the outage capacity, compared with the frequency-flat case, but the influence on the mean capacity is small.

A parametric modeling approach for multiple input multiple output (MIMO) radio channels is introduced that is based on measured data. The MIMO measurement principle can be effectively exploited to estimate the propagation direction of any significant path at both ends of the wireless link simultaneously. From the estimated parameter sets a precise reconstruction of the multidimensional wave field in the aperture domains of time, frequency and space is possible. This enables various analyses and simulations of MIMO transmission links in a very realistic way.