Understanding wireless channels is crucial for the design of wireless systems. For mobile communication, sounders and antenna arrays with short measurement times are required to simultaneously capture the dynamic and spatial channel characteristics. Switched antenna arrays are an attractive option that can overcome the high cost of real arrays and the long measurement times of virtual arrays. Optimization of the switching sequences is then essential to avoid aliasing and increase the accuracy of channel parameter estimates. This paper provides a novel and comprehensive analysis of the design of switching sequences. We first review the conventional spatio-temporal ambiguity function, extend it to dual-polarized antenna arrays, and analyze its prohibitive complexity when applied to ultra-massive antenna arrays. We thus propose a new method that uses the Fisher information matrix to tackle the estimation accuracy. We also propose to minimize the ambiguity by choosing a switching sequence that minimizes side lobes in its Fourier spectrum. In this sense, we divide the sequence design problem into Fourier-based ambiguity reduction and Fisher-based accuracy improvement, and coin the resulting design approach as Fourier-Fisher. Simulations and measurements show that the Fourier-Fisher approach achieves identical performance and significantly lower computational complexity than that of the conventional ambiguity-based approach.

Distributed multiple-input multiple-output (MIMO), also known as cell-free massive MIMO, emerges as a promising technology for sixth-generation (6G) systems to support uniform coverage and reliable communication. For the design and optimization of such systems, measurement-based investigations of real-world distributed MIMO channels are essential. In this paper, we present a sub-6 GHz indoor channel measurement campaign, featuring eight distributed antenna arrays with 128 elements in total. Multi-link channels are measured at 50 positions along a 12-meter user route. A clustering algorithm enabled by interacting objects is proposed to identify clusters in the measured channels. The algorithm jointly clusters the multipath components for all links, effectively capturing the dynamic contributions of common clusters to different links. In addition, a Kalman filter-based tracking framework is introduced for cluster prediction, tracking, and updating along the user movement. Using the clustering and tracking results, cluster-level characterization of the measured channels is performed. First, the number of clusters and their visibility at both link ends are analyzed. Next, a maximum-likelihood estimator is utilized to determine the entire cluster visibility region length. Finally, key cluster-level properties, including the common cluster ratio, cluster power, shadowing, spread, among others, are statistically investigated. The results provide valuable insights into cluster behavior in typical multi-link channels, necessary for accurate modeling of sub-6 GHz distributed MIMO channels.

Hybrid analog-digital beamforming stands out as a key enabler for future communication systems with a massive number of antennas. In this paper, we investigate the hybrid precoder design problem for angle-of-departure (AoD) estimation, where we take into account the practical constraint on the limited resolution of phase shifters. Our goal is to design a radio-frequency (RF) precoder and a base-band (BB) precoder to estimate AoD of the user with high accuracy. To this end, we propose a two-step strategy where we first obtain the fully digital precoder that minimizes the angle error bound, and then the resulting digital precoder is decomposed into an RF precoder and a BB precoder, based on the alternating optimization and the alternating direction method of multipliers. Furthermore, we derive the quantization error upper bound and provide convergence conditions for the proposed algorithm. Numerical results demonstrate the superior performance of the proposed method over state-of-the-art baselines.

We present a synchronized multisensory dataset for accurate and robust indoor localization: the Lund University Vision, Radio, and Audio (LuViRA) Dataset. The dataset includes color images, corresponding depth maps, inertial measurement unit (IMU) readings, channel response between a 5G massive multiple-input and multiple-output (MIMO) testbed and user equipment, audio recorded by 12 microphones, and accurate six degrees of freedom (6DOF) pose ground truth of 0.5 mm. We synchronize these sensors to ensure that all data is recorded simultaneously. A camera, speaker, and transmit antenna are placed on top of a slowly moving service robot, and 89 trajectories are recorded. Each trajectory includes 20 to 50 seconds of recorded sensor data and ground truth labels. Data from different sensors can be used separately or jointly to perform localization tasks, and data from the motion capture (mocap) system is used to verify the results obtained by the localization algorithms. The main aim of this dataset is to enable research on sensor fusion with the most commonly used sensors for localization tasks. Moreover, the full dataset or some parts of it can also be used for other research areas such as channel estimation, image classification, etc. Our dataset is available at: https://github.com/ilaydayaman/LuViRA_Dataset

A deep understanding of double-directional wireless channels is imperative for wireless system design. This necessitates the development of precise channel models through channel sounding. Given the dynamic nature of wireless channels, a short measurement time is highly desirable. In this paper, we present and validate a dual-polarized millimeter-wave channel sounder that uses a rotating mirror mechanism. This enables quick sweep of the azimuthal plane to capture spatial channel characteristics at 27.5-28.5 GHz in less than a second. This is radically faster than conventional virtual-array channel sounders. Following a detailed discussion of the sounder implementation and the rotating mirror concept, we present an indoor verification measurement scenario. The results demonstrate several discernible propagation paths with parameters that agree with the actual geometry of the indoor measurement environment.

User grouping based on geographic location in the fifth generation (5G) New Radio (NR) systems has several applications that can significantly improve network performance, user experience, and service delivery. We demonstrate how Sounding Reference Signals channel fingerprints can be used for dynamic user grouping in a 5G NR commercial deployment based on outdoor positions and heading direction employing machine learning methods such as neural networks combined with clustering methods.

mmWave communication has come up as the un-explored spectrum for 5G services. With new standards for 5G NR positioning, more off-the-shelf platforms and algorithms are needed to perform indoor positioning. An object can be accurately positioned in a room either by using an angle and a delay estimate or two angle estimates or three delay estimates. We propose an algorithm to jointly estimate the angle of arrival (AoA) and angle of departure (AoD), based only on the received signal strength (RSS). We use mm-FLEX, an experimentation platform developed by IMDEA Networks Institute that can perform realtime signal processing for experimental validation of our proposed algorithm. Codebook-based beampatterns are used with a uniquely placed multi-antenna array setup to enhance the reception of multipath components and we obtain an AoA estimate per receiver thereby overcoming the line-of-sight (LoS) limitation of RSS-based localization systems. We further validate the results from measurements by emulating the setup with a simple ray-tracing approach.

Millimeter-wave (mmWave) technology holds the potential to revolutionize head-mounted displays (HMDs) by enabling high-speed wireless communication with nearby processing nodes, where complex video rendering can take place. However, the sparse angular profile of mmWave channels, coupled with the narrow field of view (FoV) of patch-antenna arrays and frequent HMD rotation, can lead to poor performance. We introduce six channel performance metrics to evaluate the performance of an HMD equipped with mmWave arrays. We analyze the metrics using analytical models, discuss their impact for the application, and apply them to 28 GHz channel sounding data, collected in a conference room using eight HMD patch-antenna arrays, offset by 45° from each other in azimuth. Our findings confirm that a single array performs poorly due to the narrow FoV, and featuring multiple arrays along the HMD’s azimuth is required. Namely, the broader FoV stabilizes channel gain during HMD rotation, lessens the attenuation caused by line of sight (LoS) obstruction, and increases the channel’s spatial multiplexing capability. In light of our findings, we conclude that it is imperative to either equip the HMD with multiple arrays or, as an alternative approach, incorporate macroscopic diversity by leveraging distributed access point (AP) infrastructure.

We investigate the problem of low-complexity, high-dimensional channel estimation with beamspace observations, for the purpose of localization. Existing work on beamspace ESPRIT (estimation of signal parameters via rotational invariance technique) approaches requires either a shift-invariance structure of the transformation matrix, or a full-column rank condition. We extend these beamspace ESPRIT methods to a case when neither of these conditions is satisfied, by exploiting the full-row rank of the transformation matrix. We first develop a tensor decomposition-based approach, and further design a matrix-based ESPRIT method to achieve auto-pairing of the channel parameters, with reduced complexity. Numerical simulations show that the proposed methods work in the challenging scenario, and the matrix-based ESPRIT approach achieves better performance than the tensor ESPRIT method.

Cellular user positioning is a promising service provided by Fifth Generation New Radio (5G NR) networks. Besides, Machine Learning (ML) techniques are foreseen to become an integrated part of 5G NR systems improving radio performance and reducing complexity. In this paper, we investigate ML techniques for positioning using 5G NR fingerprints consisting of uplink channel estimates from the physical layer channel. We show that it is possible to use Sounding Reference Signals (SRS) channel fingerprints to provide sufficient data to infer user position. Furthermore, we show that small fully-connected moderately Deep Neural Networks, even when applied to very sparse SRS data, can achieve successful outdoor user positioning with meter-level accuracy in a commercial 5G environment.

High-precision cellular-based localization is one of the key technologies for next-generation communication systems. In this paper, we investigate the potential of applying machine learning (ML) to a massive multiple-input multiple-output (MIMO) system to enhance localization accuracy. We analyze a new ML-based localization pipeline that has two parallel fully connected neural networks (FCNN). The first FCNN takes the instantaneous spatial covariance matrix to capture angular information, while the second FCNN takes the channel impulse responses to capture delay information. We fuse the estimated coordinates of these two FCNNs for further accuracy improvement. To test the localization algorithm, we performed an indoor measurement campaign with a massive MIMO testbed at 3.7 GHz. In the measured scenario, the proposed pipeline can achieve centimeter-level accuracy by combining delay and angular information.

The geometry-based stochastic channel models (GSCM), which can describe realistic channel impulse responses, often rely on the existence of both local and far scatterers. However, their visibility from both the base station (BS) and mobile station (MS) depends on their relative heights and positions. For example, the condition of visibility of a scatterer from the perspective of a BS is different from that of an MS and depends on the height of the scatterer. To capture this, we propose a novel GSCM where each scatterer has dual disk visibility regions (VRs) centered on itself for both BS and MS, with their radii being our model parameters. Our model consists of short and tall scatterers, which are both modeled using independent inhomogeneous Poisson point processes (IPPPs) having distinct dual VRs. We also introduce a probability parameter to account for the varying visibility of tall scatterers from different MSs, effectively emulating their noncontiguous VRs. Using stochastic geometry, we derive the probability mass function (PMF) of the number of multipath components (MPCs), the marginal and joint distance distributions for an active scatterer, the mean time of arrival (ToA), and the mean received power through non-line-of-sight (NLoS) paths for our proposed model. By selecting appropriate model parameters, the propagation characteristics of our GSCM are demonstrated to closely emulate those of the COST-259 model.

Channel sounding is essential for the development of radio systems. One flexible strategy is the switched-array-based channel sounding, where antenna elements are activated at different time instants to measure the channel spatial characteristics. Although its hardware complexity is decreased due to fewer radio-frequency (RF) chains, sequentially switching the antenna elements can result in aliasing in the joint estimation of angles and Doppler frequencies of multipath components (MPCs). Therefore, pseudo-random switching has been proposed to mitigate such aliasing and increase estimation accuracy in both angular and Doppler domains. Nevertheless, the increased Doppler resolution could cause additional post-processing complexity of parameter estimation, which is relevant when the Doppler frequencies are not of interest, e.g., for spatial channel modeling. This paper proposes an improved hybrid sequential and random switching scheme. The primary purpose is to maintain the estimation accuracy of angles of MPCs while decreasing the resolution of Doppler frequencies for minimized complexity of channel parameter estimation. A simulated-annealing algorithm is exploited to obtain an optimized switching sequence. The effectiveness of the proposed scheme is also demonstrated with a realistic antenna array.

Opportunistic navigation using cellular signals is appealing for scenarios where other navigation technologies face challenges. In this paper, long-term evolution (LTE) downlink signals from two neighboring commercial base stations (BS) are received by a massive antenna array mounted on a passenger vehicle. Multipath component (MPC) delays and angle-of-arrival (AOA) extracted from the received signals are used to jointly estimate the positions of the vehicle, transmitters, and virtual transmitters (VT) with an extended fast simultaneous localization and mapping (FastSLAM) algorithm. The results show that the algorithm can accurately estimate the positions of the vehicle and the transmitters (and virtual transmitters). The vehicle’s horizontal position error of SLAM fused with proprioception is less than 6 meters after a traversed distance of 530 meters, whereas un-aided proprioception results in a horizontal error of 15 meters.

Accurate statistical characterization of electromagnetic propagation is necessary for the design and deployment of radio systems. State-of-the-art channel models such as the Enhanced COST 2100 Channel Model utilize the concept of clusters of multipath components, and characterize channels by their inter- and intra-cluster statistics. Automatic clustering algorithms have been proposed in literature, but the subjective nature of the problem precludes any from being deemed objectively correct. In this paper, a new algorithm is proposed, based on density-reachability and ground truth receiver pose, with the explicit focus of extracting clusters for the purpose of channel characterization. Measurements of downlink signals from a commercial LTE base station by a passenger vehicle driving in an urban environment with a massive antenna array on the roof are used to evaluate the repeatability and intuitiveness of the proposed clustering algorithm.

This paper presents LuMaMi28, a real-time 28 GHz multi-user (MU) multiple-input multiple-output (MIMO) testbed. In this testbed, the base station has 16 transceiver chains with a fully-digital beamforming architecture (with different pre-coding algorithms) and simultaneously supports multiple user equipments (UEs) with spatial multiplexing. The UEs are equipped with a beam-switchable antenna array for real-time antenna selection where the one with the highest channel magnitude, out of four pre-defined beams, is selected. For the beam-switchable antenna array, we consider two kinds of UE antennas, with different beam-width and different peak-gain. Based on this testbed, we provide measurement results for millimeter-wave (mmWave) MU-MIMO performance in different real-life scenarios with static and mobile UEs. We explore the potential benefit of the mmWave MU-MIMO systems with antenna selection based on measured channel data, and discuss the performance results through real-time measurements.

Mission-critical applications such as industrial control processes are evolving towards a new development paradigm by offloading their heavy computations to the edge of the emerging Fifth Generation Wireless Specifications (5G) network. In this manner, the applications can gain the economical and efficiency benefits of cloud computing, as well as reliable communication from the 5G network. However, the limited access to a configurable infrastructure of the 5G network and its edge computing infrastructure has restrained academic researchers from experimenting and validating their mission-critical application design under reasonable communication and computation scenarios. In this paper, we present a configurable mid-band 5G Stand-Alone (SA) deployment and demonstrate a control process that is running over the edge of the 5G network. We show in this paper a complete system setup for Control over the Edge (CoE) of the 5G network, and validate the feasibility of deploying similar mission-critical applications over the edge of 5G network.

Channel fingerprinting entails associating a point in space with measured properties of a received wireless signal. If the propagation environment for that point in space remains reasonably static with time, then a receiver with no knowledge of its own position experiencing a similar channel in the future might reasonably infer proximity to the original surveyed point. In this article, measurements of downlink LTE Common Reference Symbols from one sector of an eNodeB are used to generate channel fingerprints for a passenger vehicle driving through a dense urban environment without line-of-sight to the transmitter. Channel estimates in the global azimuthal-delay domain are used to create a navigation solution with meter-level accuracy around a city block.

This paper proposes a belief propagation (BP)-based algorithm for sequential detection and estimation of multipath component (MPC) parameters based on radio signals. Under dynamic channel conditions with moving transmitter/receiver, the number of MPCs, the MPC dispersion parameters, and the number of false alarm contributions are unknown and time-varying. We develop a Bayesian model for sequential detection and estimation of MPC dispersion parameters, and represent it by a factor graph enabling the use of BP for efficient computation of the marginal posterior distributions. At each time step, a snapshot-based parametric channel estimator provides parameter estimates of a set of MPCs which are used as noisy measurements by the proposed BP-based algorithm. It performs joint probabilistic data association, and estimation of the time-varying MPC parameters and the mean number of false alarm measurements, by means of the sum-product algorithm rules. The algorithm also exploits amplitude information enabling the reliable detection of “weak” MPCs with very low component signal-to-noise ratios (SNRs). The performance of the proposed algorithm compares well to state-of-the-art algorithms for high SNR MPCs, but it significantly outperforms them for medium or low SNR MPCs. Results using real radio measurements demonstrate the excellent performance of the proposed algorithm in realistic and challenging scenarios.

Reflective reconfigurable intelligent surface (RIS) technology is regarded as an innovative, cost- and power-effective solution that aims at influencing the wireless channel through controlled scattering. The technology can be realized by using metamaterials and/or resonant elements that scatter electromagnetic waves with a configurable phase shift. Most of the previous work on beamforming techniques for RIS assumes ideal hardware and, thus, continuous phase shifts. However, hardware constraints limit the phase shift resolution, manifested into the amount of discrete phase shifts that can be configured into each RIS element. This paper aims to offer a discrete phase shift beamforming algorithm for reflective RISs that targets minimization of the quantization error resulting from discretization of continuous phase shifts. The beamforming solution proves to be optimal under perfect channel knowledge for any discrete set of uniformly distributed phase shifts. The required complexity to find the optimal beamforming vector for our approach is found to be linear with the number of RIS elements, the minimum needed to obtain optimal results. Simulated behavior is validated by measurements, showing robustness against angle misalignments and distance variations.

Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications, including intrusion detection, patient care, and smart home. Researchers have shown different methods to classify activities and their potential benefits by utilizing WiFi signals. In this paper, we analyze classification of moving objects by employing machine learning on real data from a massive multi-input-multi-output (MIMO) system in an indoor environment. We conduct measurements for different activities in both line-of-sight and non line-of-sight scenarios with a massive MIMO testbed operating at 3.7 GHz. We propose algorithms to exploit amplitude and phase-based features classification task. For the considered setup, we benchmark the classification performance and show that we can achieve up to 98% accuracy using real massive MIMO data, even with a small number of experiments. Furthermore, we demonstrate the gain in performance results with a massive MIMO system as compared with that of a limited number of antennas such as in WiFi devices.

Mobile communications have been undergoing a generational change every ten years or so. However, the time difference between the so-called “G's” is also decreasing. While fifth-generation (5G) systems are becoming a commercial reality, there is already significant interest in systems beyond 5G, which we refer to as the sixth generation (6G) of wireless systems. In contrast to the already published papers on the topic, we take a top-down approach to 6G. More precisely, we present a holistic discussion of 6G systems beginning with lifestyle and societal changes driving the need for next-generation networks. This is followed by a discussion into the technical requirements needed to enable 6G applications, based on which we dissect key challenges and possibilities for practically realizable system solutions across all layers of the Open Systems Interconnection stack (i.e., from applications to the physical layer). Since many of the 6G applications will need access to an order-of-magnitude more spectrum, utilization of frequencies between 100 GHz and 1 THz becomes of paramount importance. As such, the 6G ecosystem will feature a diverse range of frequency bands, ranging from below 6 GHz up to 1 THz. We comprehensively characterize the limitations that must be overcome to realize working systems in these bands and provide a unique perspective on the physical and higher layer challenges relating to the design of next-generation core networks, new modulation and coding methods, novel multiple-access techniques, antenna arrays, wave propagation, radio frequency transceiver design, and real-time signal processing. We rigorously discuss the fundamental changes required in the core networks of the future, such as the redesign or significant reduction of the transport architecture that serves as a major source of latency for time-sensitive applications. This is in sharp contrast to the present hierarchical network architectures that are not suitable to realize many of the anticipated 6G services. While evaluating the strengths and weaknesses of key candidate 6G technologies, we differentiate what may be practically achievable over the next decade, relative to what is possible in theory. Keeping this in mind, we present concrete research challenges for each of the discussed system aspects, providing inspiration for what follows.

With the potential to provide a clean break from massive multiple-input multiple-output, large intelligent surfaces (LISs) have recently received a thrust of research interest. Various proposals have been made in the literature to define the exact functionality of LISs, ranging from fully active to largely passive solutions. Nevertheless, almost all studies in the literature investigate the fundamental spectral efficiency performance of these architectures. In stark contrast, this paper investigates the implementation aspects of LISs. Using the fully active LIS as the basis of our exposition, we first present a rigorous discussion on the relative merits and disadvantages of possible implementation architectures from a radio-frequency circuits and real-time processing viewpoints. We then show that a distributed architecture based on a common module interfacing a smaller number of antennas can be scalable. To avoid severe losses with analog signal distribution, multiple common modules can be interconnected via a digital nearest-neighbor network. Furthermore, we show that with such a design, the maximum backplane throughput scales with the number of served user terminals, instead of the number of antennas across the surface. The discussions in the paper can serve as a guideline toward the real-time design and development of LISs.

Fifth-generation (5G) new radio (NR) deployments are being rolled out in both the C-band (3.3 - 5.0 GHz) and millimeter-wave (mmWave) band (24.5 - 29.5 GHz). For outdoor scenarios, the C-band is expected to provide wide area coverage and throughput uniformity, whereas the mmWave band is expected to provide ultra-high throughput to dedicated areas within the C-band coverage. Due to the differences in the frequency bands, both systems are expected to be designed with different transmit and receive parameters, naturally resulting in performance variations proportional to the chosen parameters. Unlike many previous works, this paper presents measurement evaluations in central Auckland, New Zealand, from a pre-commercial deployment of a single-user, single-cell 5G-NR system operating in both bands. The net throughput, coverage reliability, and channel rank are analyzed across the two bands with baseband and analog beamforming. Our results show that the C-band coverage is considerably better than mmWave, with a consistently higher channel rank. Furthermore, the spatial stationarity region (SSR) for the azimuth angles-of-departure (AODs) is characterized, and a model derived from the measured beam identities is presented. The SSR of azimuth AODs is seen to closely follow a gamma distribution.

Massive multiple-input multiple-output (MIMO) promises significantly higher performance relative to conventional multiuser systems. However, the promised gains of massive MIMO systems rely heavily on the accuracy of the absolute frontend calibration, as well as quality of channel estimates at the base station (BS). In this paper, we analyze user equipment-aided calibration mechanism to estimate the amplitude scaling and phase drift at each radio-frequency chain connected to the BS array. Assuming a uniform linear array at the BS and Ricean fading, we obtain the estimation parameters with moment-based (amplitude, phase) and maximum-likelihood (phase-only) estimation techniques. In stark contrast to previous works, we mathematically articulate the equivalence of the two approaches for phase estimation. Furthermore, we rigorously derive a Cramér-Rao lower bound to characterize the accuracy of the two estimators. Via numerical simulations, we evaluate the estimator performance with varying dominant line-of-sight powers, dominant angles-of-arrival, and signal-to-noise ratios.

Ultra-reliable, low-latency communication (URLLC) is being developed to support critical control applications over wireless networks. Exploiting spatial diversity through relays is a promising technique for achieving the stringent requirements of URLLC, but coordinating relays reliably and with low overhead is a challenge. Adaptive relay selection techniques have been proposed as a way to simplify implementation while still achieving the requirements of URLLC. Identifying good relays with low overhead and high confidence is critical for such adaptive relay selection techniques.Channel dynamics must be taken into account by adaptive relay selection algorithms because channel quality may degrade in the time it takes to estimate the relay's channel and schedule a transmission. Spatial channel dynamics are well studied in many settings such as RADAR and the fast-fading wireless channels, but less so in the URLLC context where rare events neglected in other models may be important. In this work, we perform measurements to validate channel models in the slow fading regime of interest. We compare measurements to Jakes's model and discuss the appropriateness of Jakes's model for URLLC relay selection. This is further applied to demonstrate that easily implementable relay selection techniques perform well in practical settings.Polynomial interpolation and neural-net-based algorithms were evaluated as channel prediction algorithms. These techniques perform orders of magnitude better than relay selection on average (nominal) SNR.

Cellular radio based localization can be an important complement or alternative to other localization technologies, as base stations continuously transmit signals of opportunity with beneficial positioning properties. In this paper, we use the long term evolution (LTE) cell-specific reference signal for this purpose. The multipath component delays are estimated by the ESPRIT algorithm, and the estimated multipath component delays of different snapshots are associated by global nearest neighbor with a Kalman filter. Rao-Blackwellized particle filter based simultaneous localization and mapping (SLAM) is then applied to estimate the position of user equipment and that of the base station and virtual transmitters. In a measurement campaign, data from one base station was logged, and the analysis based on the data shows that, at the end of the measurement, the SLAM performance is 11 meters better than that with only inertial measurement unit (IMU).

Positioning is an essential feature in Narrow-Band Internet-of-Things (NB-IoT) systems. Observed Time Difference of Arrival is one of the supported positioning techniques for NB-IoT. It utilizes the downlink NB positioning reference signal (NPRS) generated based on a length-31 Gold sequence. Although a Gold sequence has good auto-correlation and cross-correlation properties, the correlation properties of NPRS in NB-IoT are still sub-optimal. The reason is mainly due to two facts: the number of NPRS symbols in each subframe is limited, and the featured sampling-rate is low. In this paper, we propose to modify the NPRS generation by exploiting the cross-correlation function of the NPRS. That is, for each orthogonal frequency division multiplexing (OFDM) symbol we generate the first NPRS symbol as specified in the current standard, i.e., a Gold sequence; while the second OFDM symbol is set to the additive inverse of the first one. Our simulation results show that the proposed NPRS sequence results in improving the correlation properties, particularly with respect to the cross-correlation property. Furthermore, 15% -30% positioning-accuracy improvements can be attained with the proposed method, compared to the legacy one under both Additive White Gaussian Noise and Extended-Pedestrian-A channels. The proposed NPRS sequence can also be applied to other similar systems, such as long-term-evolution (LTE).

Massive multiple-input multiple-output (MIMO) is expected to be a vital component in future 5G systems. As such, there is a need for new modeling in order to investigate the performance of massive MIMO not only at the physical layer but also higher up the networking stack. In this paper, we present general optimization models for massive MIMO, based on mixed-integer programming and compatible sets, with both maximum ratio combining and zero-forcing precoding schemes. We then apply our models to the case of joint device scheduling and power control for heterogeneous devices and traffic demands, in contrast to the existing power control schemes that consider only homogeneous users and saturated scenarios. Our results show that substantial benefits, in terms of energy usage, can be achieved without sacrificing throughput and that both the signaling overhead and the complexity of end devices can be reduced by abrogating the need for uplink power control through efficient scheduling.

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

In this paper, we present a robust multipath-based localization and mapping framework that exploits the phases of specular multipath components (MPCs) using a massive multiple-input multiple-output (MIMO) array at the base station. Utilizing the phase information related to the propagation distances of the MPCs enables the possibility of localization with extraordinary accuracy even with limited bandwidth. The specular MPC parameters along with the parameters of the noise and the dense multipath component (DMC) are tracked using an extended Kalman filter (EKF), which enables to preserve the distance-related phase changes of the MPC complex amplitudes. The DMC comprises all non-resolvable MPCs, which occur due to finite measurement aperture. The estimation of the DMC parameters enhances the estimation quality of the specular MPCs and, therefore, also the quality of localization and mapping. The estimated MPC propagation distances are subsequently used as input to a distance-based localization and mapping algorithm. This algorithm does not need prior knowledge about the surrounding environment and base station position. The performance is demonstrated with real radio-channel measurements using an antenna array with 128 ports at the base station side and a standard cellular signal bandwidth of 40MHz. The results show that the high accuracy localization is possible even with such a low bandwidth.

The performance of wireless communication systems is fundamentally determined by wireless communication channel properties. Wireless vehicular communication channels exhibit multipath propagation and non-stationary channel statistics. Methods and tools for the repeatable test of wireless communication systems and signal processing algorithms in such environments are urgently needed to enable the development of reliable communication links with low-latency. In this paper we present the measurement and validation of a real time channel emulation method for non-stationary vehicular scenarios based on a geometry-based stochastic channel model.

Realistic network simulations are necessary to assess the performance of any communication system. In this paper, we describe an implementation of a channel model for vehicle-to-vehicle (V2V) communication in the OMNeT++/Plexe simulation environment. The model is based on previous extensive measurements in a V2V multilink highway scenario and cover line-of-sight (LOS) as well as obstructed LOS (OLOS) scenarios, which occurs when one or more vehicles obstruct the LOS component. The implementation captures both the temporal autocorrelation and the joint multilink cross-correlation processes to achieve a realistic behavior. Preliminary results show that the implementation now generates stochastic large-scale fading with an autocorrelation function that agrees well with measured data. A representation of the cross-correlation process is now implemented through proper channel model selection since the geometry and location of objects are known in Plexe. We also show the impact of the suggested V2V physical layer (PHY) on the performance evaluation results observed at the facilities layer. As a metric, we use the data age, which is a measure how old the information about a vehicle is. When considering the autocorrelation in simulations, the experienced data-age increases. Examples show an increase of the 10% percentile data-age from 0.1~s to 1.5~s, which may affect the application performance significantly in critical situations.

Based on multi-link synchronous propagation measurements, we study the downlink performance of Coordinated Multi-point (CoMP) systems. The propagation measurements took place in an outdoor micro-cellular environment with two base stations (BSs) located 60 m apart, each equipped with two cross-polarized co-located antennas. We focus on the challenging case of having closely-located users with 0.5 m inter- user distance. We consider four virtual users, each equipped with two antennas, moving along various routes. The patterns of the users' antennas, including the user's body influence, are measured in static scenarios with an upper body phantom and a handset mockup in the LTE 2.5-2.7 GHz band. The users' composite channel matrices are constructed based on: 1) the propagation channel represented by the multipath components extracted from the propagation measurements and 2) the user- influenced antenna pattern. We first analyze the effect of the BS antenna polarization on the collinearity among the users' channel matrices. Then, we evaluate the effect of the BS antenna polarization on improving the system performance, where the performance is evaluated based on two criterion: the number of users that the system is able to serve in the same time-frequency resource, and the achievable sum-rate. It is found that using cross- polarized BS antenna improves the fairness and the sum-rate of the system. It is also found that, in contrast to the case of conventional co-located MIMO, the improvement gained by CoMP systems (with per-BS power control) from using cross-polarized BS antennas is mainly attributed to improving the multiplexing gain of the system while the system's effective power gain has insignificant contribution.

Downlink beamforming in Massive multiple-input and multiple-output (MIMO) either relies on uplink pilot measurements-exploiting reciprocity and time-division duplexing operation, or on the use of a predetermined grid of beams with user equipments reporting their preferred beams, mostly in frequency-division duplexing operation. Massive MIMO in its originally conceived form uses the first strategy, with uplink pilots, whereas there is currently significant commercial interest in the second, grid-of-beams. It has been analytically shown that with isotropic scattering (independent Rayleigh fading) the first approach outperforms the second. Nevertheless, there remains controversy regarding their relative performance in practical channels. In this contribution, the performances of these two strategies are compared using measured channel data at 2.6 GHz.

This paper presents a phase-based positioning framework using a massive MIMO system. The phase-based distance estimates of MPCs together with other parameters are tracked with an EKF, the state dimension of which varies with the birth-death processes of paths. The RIMAX and the modeling of dense multipath components in the framework further enhance the quality of parameter tracking by providing an accurate initial state and the underlying noise covariance. The tracked MPCs are fed into a time-of-arrival self-calibration positioning algorithm for simultaneous trajectory and environment estimation. Throughout the positioning process, no prior knowledge of the surrounding environment and base station position is needed. The performance is demonstrated with the measurement of a 2D complex movement, which was performed in a sports hall with an antenna array with 128 ports as base station using a standard cellular bandwidth of 40 MHz. The positioning result shows that the mean deviation of the estimated user equipment trajectory from the ground truth is 13 cm. In summary, the proposed framework is a promising high-resolution radio-based positioning solution for current and next generation cellular systems.

This paper provides an initial investigation on the application of convolutional neural networks (CNNs) for fingerprint-based positioning using measured massive MIMO channels. When represented in appropriate domains, massive MIMO channels have a sparse structure which can be efficiently learned by CNNs for positioning purposes. We evaluate the positioning accuracy of state-of-the-art CNNs with channel fingerprints generated from a channel model with a rich clustered structure: the COST 2100 channel model. We find that moderately deep CNNs can achieve fractional-wavelength positioning accuracies, provided that an enough representative data set is available for training.

Channel measurements of large-scale multiuser multiple-input multiple-output (MU-MIMO) radio propagation channels are presented. In the setup, three users with patch antennas communicate simultaneously with a base station (BS) equipped with a large antenna array in an indoor environment. Both a uniform linear array (ULA) and a uniform planar array (UPA) are used, and their relative ability to separate MU-MIMO signals is examined. At the mobile station (MS) side, the effect of inter-user spacing (i.e., the spacing between different users) is investigated. This evaluation is done by means of the correlation matrix distance metric (between each pair of users) and the singular value spread of the system. Our investigation shows that the users can be spatially separated in a large antenna array system in line-of-sight propagation conditions even when they are located close to each other. Furthermore, the users tend to be more separable when a ULA is adopted, compared to using a UPA. Finally, we also confirm that larger user separation distance results in increased channel orthogonality by measurements.

The first measured results for massive multiple-input, multiple-output (MIMO) performance in a line-of-sight (LOS) scenario with moderate mobility are presented, with 8 users served by a 100 antenna base Station (BS) at 3.7 GHz. When such a large number of channels dynamically change, the inherent propagation and processing delay has a critical relationship with the rate of change, as the use of outdated channel information can result in severe detection and precoding inaccuracies. For the downlink (DL) in particular, a time division duplex (TDD) configuration synonymous with massive MIMO deployments could mean only the uplink (UL) is usable in extreme cases. Therefore, it is of great interest to investigate the impact of mobility on massive MIMO performance and consider ways to combat the potential limitations. In a mobile scenario with moving cars and pedestrians, the correlation of the MIMO channel vector over time is inspected for vehicles moving up to 29km/h. For a 100 antenna system, it is found that the channel state information (CSI) update rate requirement may increase by 7 times when compared to an 8 antenna system, whilst the power control update rate could be decreased by at least 5 times relative to a single antenna system.

The first measured results for massive multiple-input, multiple-output (MIMO) performance in a line-of-sight scenario with moderate mobility are presented, with eight users served in real time using a 100-antenna base station at 3.7 GHz. When such a large number of channels dynamically change, the inherent propagation and processing delay has a critical relationship with the rate of change, as the use of outdated channel information can result in severe detection and precoding inaccuracies. For the downlink (DL) in particular, a time-division duplex configuration synonymous with massive MIMO deployments could mean only the uplink (UL) is usable in extreme cases. Therefore, it is of great interest to investigate the impact of mobility on massive MIMO performance and consider ways to combat the potential limitations. In a mobile scenario with moving cars and pedestrians, the massive MIMO channel is sampled across many points in space to build a picture of the overall user orthogonality, and the impact of both azimuth and elevation array configurations are considered. Temporal analysis is also conducted for vehicles moving up to 29 km/h and real-time bit-error rates for both the UL and DL without power control are presented. For a 100-antenna system, it is found that the channel state information update rate requirement may increase by seven times when compared with an eight-antenna system, whilst the power control update rate could be decreased by at least five times relative to a single antenna system.

There is considerable pressure to define the key requirements of 5G, develop 5G standards, and perform technology trials as quickly as possible. Normally, these activities are best done in series but there is a desire to complete these tasks in parallel so that commercial deployments of 5G can begin by 2020. 5G will not be an incremental improvement over its predecessors; it aims to be a revolutionary leap forward in terms of data rates, latency, massive connectivity, network reliability, and energy efficiency. These capabilities are targeted at realizing high-speed connectivity, the Internet of Things, augmented virtual reality, the tactile internet, and so on. The requirements of 5G are expected to be met by new spectrum in the microwave bands (3.3-4.2 GHz), and utilizing large bandwidths available in mm-wave bands, increasing spatial degrees of freedom via large antenna arrays and 3-D MIMO, network densification, and new waveforms that provide scalability and flexibility to meet the varying demands of 5G services. Unlike the one size fits all 4G core networks, the 5G core network must be flexible and adaptable and is expected to simultaneously provide optimized support for the diverse 5G use case categories. In this paper, we provide an overview of 5G research, standardization trials, and deployment challenges. Due to the enormous scope of 5G systems, it is necessary to provide some direction in a tutorial article, and in this overview, the focus is largely user centric, rather than device centric. In addition to surveying the state of play in the area, we identify leading technologies, evaluating their strengths and weaknesses, and outline the key challenges ahead, with research test beds delivering promising performance but pre-commercial trials lagging behind the desired 5G targets.

This paper presents a mutual coupling-based calibration method for time-division-duplex massive MIMO systems, which enables downlink precoding based on uplink channel estimates. The entire calibration procedure is carried out solely at the base station (BS) side by sounding all BS antenna pairs. An expectation-maximization (EM) algorithm is derived, which processes the measured channels in order to estimate calibration coefficients. The EM algorithm outperforms the current state-of-the-art narrow-band calibration schemes in a mean squared error and sum-rate capacity sense. Like its predecessors, the EM algorithm is general in the sense that it is not only suitable to calibrate a co-located massive MIMO BS, but also very suitable for calibrating multiple BSs in distributed MIMO systems. The proposed method is validated with experimental evidence obtained from a massive MIMO testbed. In addition, we address the estimated narrow-band calibration coefficients as a stochastic process across frequency, and study the subspace of this process based on measurement data. With the insights of this study, we propose an estimator which exploits the structure of the process in order to reduce the calibration error across frequency. A model for the calibration error is also proposed based on the asymptotic properties of the estimator, and is validated with measurement results.

In massive MIMO performance evaluations it is often assumed that the terminal has a single antenna. The combination of multiple antennas in a terminal and massive MIMO precoding at the base station side can further improve overall system performance. We present measurement results for multi antenna terminals operating in different transmission schemes and how they perform under varying loading conditions. Gain expressions are derived that enable easy comparison between the transmission schemes. The evaluation is performed on realistic antennas integrated into Sony Xperia handsets tuned to 3.7 GHz and operated together with the Lund University massive MIMO (LuMaMi) test bed. It is concluded that the approach used in today's mobile systems, where up link and down link are addressed independently, will not provide the best performance. The performance can be improved by the selection of transmission schemes optimized for massive MIMO.

This paper presents results based on polarimetric radio channel measurements at 60 GHz in a small room and in an empty, unfurnished, medium-sized room. The measurements in the small meeting room were performed using dual-polarized virtual antenna array elements at both the transmitter and receiver sides and includes LOS and NLOS scenarios. In the unfurnished room, a directional horn antenna was scanned in the azimuth plane and the Rx antenna was an omnidirectional antenna. Based on these measurements, the paper presents experimental parameter estimates for a novel model of the polarimetric propagation paths, that includes the power-decay as well as a correlated fading for the different polarization components. This work also includes an estimation method that takes measurement limitations caused by the noise floor and the limited cross-polarization discrimination of the antennas into account using censored samples.

We investigate antenna correlation of an M-antenna massive multiple-input multiple-output (MIMO) setup with the purpose of obtaining a low-rank representation of the instantaneous massive MIMO channel. Low-rank representation bases using short-term and long-term antenna correlation statistics are defined, and their performance is evaluated with data sets obtained from channel measurements in both indoor and outdoor environments at 2.6 GHz. Our results indicate that the short-term bases can capture a larger amount of the channel energy compared to the long-term ones, but they have a limited timespan, one coherence time or less. On the other hand, the long-term bases are stable over time-spans of a few seconds. Hence, they can be obtained relatively easily. We also investigate a rank-p vector-scalar LMMSE channel estimator that exploits antenna correlation. Our results show that the investigated estimator can achieve a performance similar to that of full-rank LMMSE at a (2p + 1)/M times lower cost. The investigated estimator may be used in conjunction with estimators that exploit correlation in the frequency and time domains or, alternatively, in situations in which these estimators cannot be used, e.g., when pilot separation is larger than the channel coherence bandwidth or time.

Coordinated Multi Point (CoMP) transmission and reception techniques are used in order to improve the performance of single or multiple cells in wireless systems. A system using CoMP techniques is capable of jointly optimizing its transmission and/or reception and, hence, achieving higher performance. In this work, performance evaluation of three CoMP techniques are performed based on simulations using the COST 2100 channel model and also based on measured channels collected from a synchronous multi-link measurement campaign at Lund University. The contribution of this work is twofold. First, the performance evaluation of different CoMP transmission techniques, namely, the Joint Processing, the Partial Joint Processing and the Coordinated Beamforming, are presented. Second, the capability of the COST 2100 model in predicting the relative performance of different CoMP techniques is assessed. It was found that the COST 2100 model is capable of estimating the relative performance of the studied CoMP schemes well. Moreover, detailed comparison highlighting the agreements and discrepancies between the performance results based on the measured channels and those based on the COST 2100 model are reported.

In this paper, the effect of polarization on the downlink achievable sum-rate of multi-user Cooperative Multi-Point (CoMP) systems is studied using synchronous multi-link channel measurements with two different antenna arrangements. In the first setup, four dual-polarized antenna patches with inter-patch distance varied from half wavelength to 8 m are considered at the base station (BS). In the second setup, two BSs located 60 m apart are used, each is provided with dual-polarized antennas spaced half a wavelength apart. Based on the BS antenna element selection, scenarios with different antenna polarizations at the BSs are considered. We consider four virtual users each is equipped with two antennas (V and H-polarized) with half-wavelength inter-element distance. The users are equally spaced with inter-user distance of about 0.5 m and moving in different routes. For each setup, the 8 × 4 MIMO multi-user channels are used to evaluate the achievable sum-rate of the system, where the minimum mean square error (MMSE) beamforming at both the BS and the mobile station (MS) is used. It is found that the performance improvement gained from using dual-polarized antennas at the BS vanishes if the BS antenna elements are distributed over large distance (i.e., having a large antenna array aperture). However, in the measured environment, for the case of having two BSs each of which is provided with co-located antennas, using dual-polarized antennas provide about 40% improvement in the ergodic sum-rate compared to using single-polarized antennas.

This paper presents a calibration technique for massive MIMO base stations, where the frequency responses of the transmit and/or receive analog front-ends are individually estimated and compensated for. Calibration is achieved by a first-round of channel sounding between base station antennas, followed by post-processing and a compensation stage. The proposed technique is general in the sense that is does not use external sources, nor internal dedicated circuits for calibration purposes. The only requirement of the technique is that mutual coupling between all pairs of sounded base station antennas exists and is known. Our analysis suggests that mutual coupling can be conveniently used for calibration purposes, and that multipath propagation during calibration is the most prominent source for calibration inaccuracies.

It has been widely recognized that relaying is an important method for increasing the reliability and spectral efficiency of communications systems, and it is thus helpful for improving the performance of vehicle-to-vehicle (V2V) communication systems. However, designing and evaluating V2V relay networks require understanding the effect of shadowing, as this critically impacts the performance of the relay system. Even though the theoretic performances of various relaying schemes have been well investigated, there is a lack of empirical test that incorporates realistic shadowing effects. In this paper, we analyze the performance of relaying transmission in V2V scenarios based on measurements in scenarios where shadowing occurs through large vehicles such as buses. We investigate several potential locations for the relay nodes, and the measurements are performed with two static transmitters (TX) and one dynamic receiver (RX). Outage probabilities of several relaying schemes such as multi-hop decode-and- forward, multi-hop amplify-and-forward, and diversity-amplify-and-forward are estimated and discussed based on the measured instantaneous end- to-end signal-to-noise ratio (SNR). It is found that: (i) shadowing effect caused by the bus between V2V line-of-sight (LOS) links increases the outage probability for the non-LOS (NLOS) direct transmission; (ii) using relay node on the bus roof can significantly improve transmission, however, a strong shadowing effect may reduces the acceptable communication distance of relaying scheme; and (iii) the diversity-amplify-and- forward relaying scheme generally has the best performance. Our results can be used to design a relay system for V2V communications.

Reliable connectivity between vehicles is a requirement for efficient overtaking warning systems. We investigate the 6 GHz propagation channel between oncoming vehicles with five different types of large obstructing vehicles in a poor scattering environment. The presented channel gains emphasize the advantages of different antenna positions and the difference between a straight road and curved road. We conclude that connectivity ranges on a straight road are close to the minimum distance required by a warning system. We furthermore conclude that the channel gain of antennas mounted inside the vehicle can be higher compared to antennas mounted on the roof, which can increase the connectivity range significantly.

Massive MIMO has been shown to greatly improve spectral and transmit-energy efficiency. When implementing a massive MIMO system, one challenge is high hardware complexity. A solution is to reduce the number of radio frequency (RF) transceiver chains by performing antenna selection. However, a full RF switch that connects the antennas and RF chains can be highly complex and incurs significant loss in output signal quality, especially when the number of antennas and RF chains are large. We therefore propose a simpler solution - binary switching architecture, which is suboptimal but provides better signal quality, as compared to the full switching network. To evaluate the proposed technique, we compare the sum-rate capacity when using several different configurations of binary switching with the performance of the full switching. Full MIMO performance obtained without antenna selection is also presented as a reference. The investigations in this paper are all based on measured channel data at 2.6 GHz, using a uniform linear array and a cylindrical array, both having 128 antenna elements. It is found that the proposed binary switching gives very competitive performance that are close to the full switching, for the measured channels. The results indicate a potential to simplify massive MIMO hardware by reducing the number of RF chains, and performing antenna selection with simple binary switching architecture.

In this paper, multiple-input multiple-output (MIMO) channel measurements in an outdoor micro-cell environment are used to study the effect of base-station (BS) inter-element spacing on multi-user MIMO signal separation. Users with two dual-polarized patch antennas at the mobile station (MS), and four equally-spaced dual-polarized patch antennas at the BS are considered. At the BS, the inter-element spacing (i.e., the spacing between the four dual-polarized patch antennas), is varied from half a wavelength to 8 m. For each BS inter-element spacing, the 4 × 8 MIMO channels are used to evaluate the system's capability separating closely located users. This evaluation is done by means of the correlation matrix distance metric (between each pair of closely located users) and the sum-rate capacity of the system (when 8 equally-spaced closely located users are served simultaneously). It is found that the system is capable of separating closely located users as long as the distance between them and the BS is less than the Fraunhofer distance associated with the BS antenna array. In the measured environment, for both LOS and NLOS propagation conditions, users located as close as 0.5 m from each other, having the same orientation, are separated successfully in the multi-user MIMO sense.

Fully-synchronous measurements of a massive multi-user multiple-input multiple-output (MU-MIMO) radio propagation channel are presented. We evaluate the ability of a massive MIMO system to spatially separate users located close to each other in line-of-sight (LOS) propagation conditions. The system consists of a base-station (BS) antenna array equipped with 64 dual-polarized antenna elements (128 ports) arranged in a cylindrical configuration, and eight single-antenna users. The users are confined to a five-meter diameter circle and move randomly at pedestrian speeds. The BS antenna array is located on top of a 20 m tall building and has LOS to the users. We examine user separability by studying singular value spread of the MU-MIMO channel matrix for several subsets of BS antenna array ports, along with sum-rate capacity and achievable sum-rates with both zero-forcing and matched-filtering linear precoders. We also analyze the performance of the user with the lowest rate. Finally, a comparison between the performance offered by the massive MIMO system and that of a conventional MU-MIMO system is provided. To the best of our knowledge, this is the first report of fully-synchronous dynamic measurements of a massive MIMO system. Our investigation shows that even users located close to each other in LOS propagation conditions can be spatially separated in a massive MIMO system.

This paper presents a detailed description of a propagation channel measurement campaign performed in a warehouse environment and provide a comprehensive channel model for this environment. Using a vector network analyzer, we explored both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios over a 2-8 GHz frequency range. We extracted both small-scale and large-scale channel parameters such as distance-dependent path loss exponent, frequency-dependent path loss exponent, shadowing variance, and amplitude fading statistics of the channel. We also provide the clustering analysis of the channel impulse responses by using a modified Saleh-Valenzuela approach. Our model is validated by comparing the distributions of the root-mean-square (RMS) delay spread obtained from our model and measurement data, respectively. The model developed can be used for realistic performance evaluations of ultrawideband (UWB) communications and localization systems in warehouse environments.

When a mobile station moves along a trajectory, it will see different parts of the same scatterers or scatterer groups during its movement. In this paper, we present a new method to identify such physical clusters of scatterers, and the corresponding groups of multipath components (MPCs) interacting with those clusters, from measurements of channel impulse responses. The method is based on identifying MPCs that have similar long-term properties - in the sense that they ``effectively interact with'' the same physical clusters, The method consists of four steps: (1) estimate the delays, the DOAs and the amplitudes of the MPCs in each time snapshot; (2) track the MPCs in the time-delay domain; (3) localize all the scattering points in a 2-D Cartesian coordinate system and cluster them on a map with a traditional Kmeans clustering algorithm; (4) merge the scattering points of different MPCs into physical clusters. The method is evaluated using both synthetic data and real measurement data from a suburban area. In the latter case, the locations and the sizes of the interacting objects identified from the measurements show excellent agreement with the location of physical objects in the environment such as pillars and buildings.

Urban intersections constitute an important safety-critical scenario for vehicle-to-vehicle communication. Based on measurements, this paper presents detailed investigations of the radio wave propagation processes in a typical urban intersection. We focus on one time instance of the propagation process and set up hypothesis for locations of the scattering objects. Multipath components (MPCs) are identified and an MPC tracking algorithm is applied. The number of MPCs and the lifetime of MPCs for this communication scenario are analyzed. It is concluded that the lifetime of an MPC is heavily dependent on its relative power, with typical values in the range 0.05-0.3 s corresponding to 0.5-3 m travel distance of the vehicles.

Wireless vehicular communication is, in contrast to other terrestrial types of wireless communications, more dynamic in nature. Both the transmitter and the receiver are moving at high speeds relative to each other, which generates highly dynamic wireless channels. Such channels are characterized by short stationarity regions and large Doppler spreads. Modem manufacturers face a challenge when designing and implementing equipment for such environments. Similarly, for testing and evaluation real-life measurements with vehicles are required, which often is an expensive and slow process. This paper tackles this problem by proposing a method for stress testing transceivers based on the design and implementation of a real-time wireless channel emulator for wireless vehicular communications using a software defined radio (SDR). The emulator together with the proposed test methodology enable quick on-bench evaluation of wireless modems. In the paper we also apply the test on two different IEEE 802.11p modem implementations and characterize the packet error rate performance for different Doppler-delay combinations.

Massive MIMO can greatly increase both spectral and transmit-energy efficiency. This is achieved by allowing the number of antennas and RF chains to grow very large. However, the challenges include high system complexity and hardware energy consumption. Here we investigate the possibilities to reduce the required number of RF chains, by performing antenna selection. While this approach is not a very effective strategy for theoretical independent Rayleigh fading channels, a substantial reduction in the number of RF chains can be achieved for real massive MIMO channels, without significant performance loss. We evaluate antenna selection performance on measured channels at 2.6 GHz, using a linear and a cylindrical array, both having 128 elements. Sum-rate maximization is used as the criterion for antenna selection. A selection scheme based on convex optimization is nearly optimal and used as a benchmark. The achieved sum-rate is compared with that of a very simple scheme that selects the antennas with the highest received power. The power-based scheme gives performance close to the convex optimization scheme, for the measured channels. This observation indicates a potential for significant reductions of massive MIMO implementation complexity, by reducing the number of RF chains and performing antenna selection using simple algorithms.

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.

Massive MIMO, also known as very-large MIMO or large-scale antenna systems, is a new technique that potentially can offer large network capacities in multi-user scenarios. With a massive MIMO system, we consider the case where a base station equipped with a large number of antenna elements simultaneously serves multiple single-antenna users in the same time-frequency resource. So far, investigations are mostly based on theoretical channels with independent and identically distributed (i.i.d.) complex Gaussian coefficients, i.e., i.i.d. Rayleigh channels. Here, we investigate how massive MIMO performs in channels measured in real propagation environments. Channel measurements were performed at 2.6 GHz using a virtual uniform linear array (ULA), which has a physically large aperture, and a practical uniform cylindrical array (UCA), which is more compact in size, both having 128 antenna ports. Based on measurement data, we illustrate channel behavior of massive MIMO in three representative propagation conditions, and evaluate the corresponding performance. The investigation shows that the measured channels, for both array types, allow us to achieve performance close to that in i.i.d. Rayleigh channels. It is concluded that in real propagation environments we have characteristics that can allow for efficient use of massive MIMO, i.e., the theoretical advantages of this new technology can also be harvested in real channels.

In this paper we consider time-division-duplex (TDD) reciprocity calibration of a massive MIMO system. The calibration of a massive MIMO system can be done entirely at the base station (BS) side by sounding the BS antennas one-by-one while receiving with the other BS antennas. With an M antenna BS, this generates M(M - 1) signals that can be used for calibration purposes. In this paper we study several least-squares (LS) based estimators, differing in the number of received signals that are being used. We compare the performance of the estimators, and we conclude that is possible to accurately calibrate an entire BS antenna array using the mutual coupling between antennas as the main propagation mechanism.

Vehicle-to-Vehicle (V2V) communication is an en-abler for improved traffic safety and congestion control. As for any wireless system the ultimate performance limit is determined by the propagation channel. A particular point of interest is the shadowing effect of large vehicles such as trucks and buses, as this might affect the communication range significantly. In this paper we present measurement results and model the propagation channel in which a bus acts as a shadowing object between two passenger cars. The measurement setup is based on a WARP FPGA software radio as transmitter, and a Tektronix RSA5106A real-time complex spectrum analyzer as receiver. We analyze the influence of the bus location and car separation distance on the large-scale path loss, shadowing, and small-scale fading. The main effect of the bus is that it is acting as an obstruction creating an additional 15–20 dB attenuation. A Nakagami distribution is found to describe the statistics of the small-scale fading, by using Akaike's Information Criterion and the Kolmogorov-Smirnov test. The distance-dependency of the path loss is analyzed, and a stochastic model is developed to reflect the impact.

The possibility of using the channel reciprocity between the uplink and downlink channels in Frequency Division Duplex (FDD) systems to improve the efficiency has been deeply investigated. Previous studies have come to different conclusions for the characterization of the dissimilarity in uplink and downlink channel properties. This paper analyzes the mismatch in directional properties of the uplink and downlink channels of FDD systems based on the power of their multipath clusters. At a system level, due to the limited directional resolution, the multipath components arriving at the base station are seen as clusters, rather than individual signal paths. This fact is used to describe the mismatch of directional properties between uplink and downlink. The contribution of this paper is the use of a spectral dissimilarity metric as a measure to characterize this mismatch; a detailed study of this dissimilarity metric is also presented. It is found that under favorable propagation conditions, for both actual channel measurement data and ray-tracing simulations, the directional and power properties of the downlink multipath clusters can be estimated from the uplink channel with high reliability. Therefore, directional-based beamforming transmission techniques for FDD systems will be able to benefit from such similarity in order to improve the system performance.

Spatially distributed transmission points connected to the same source, known as distributed antenna systems, is one of the promising communication techniques that is capable to improve system performance. The distribution of the transmission points over a large geographical area increases the chances for the receive mobile station (MS) to experience favorable channel conditions from one link or more, simultaneously. However, this anticipated gain depends heavily on the cross-correlation properties of the parameters of different links, especially the large scale parameters (LSP). In this contribution, propagation measurements were used in order to study the cross-correlation of the large-scale fading (LSF) of different links in a distributed antenna system. Multi-site fully-coherent wideband (40 MHz bandwidth) measurements for a semi-urban microcell environment at 2.6 GHz with four base stations (BSs) and one MS were performed. The study of the LSF cross-correlation properties was performed on the global scale (i.e., using the LSF values of the full measurement route at once), and on the local scale (i.e., over local areas of hundreds of wavelengths long). It was found that, for the investigated environment, analyzing the LSF cross-correlation properties on the local scale gives better insight compared to performing the analysis on the global scale; the variations of the LSF of the different links are caused mainly by independent interacting objects (IOs), which reduce the probability of having all the links degrade simultaneously; and a Gaussian distribution can be used to model the cross-correlation coefficients for each link pairs.

Vehicle-to-vehicle (V2V) communication is an enabler for improved traffic safety and congestion control. As for any wireless system, the ultimate performance limit is determined by the propagation channel. A particular point of interest is the shadowing effect of large vehicles such as trucks and buses, as this might affect the communication range significantly. In this paper we present measurement results and model the propagation channel, in which a bus acts either as a shadowing object or as a relay between two passenger cars. The measurement setup is based on a Wireless Open-Access Research Platform (WARP) Field-Programmable Gate Array (FPGA) software radio as transmitter and a Tektronix RSA5106A real-time complex spectrum analyzer as receiver. We analyze the influence of the bus location and car separation distance on the path loss, shadowing, small-scale fading, delay spread, and cross correlation. The main effect of the bus is that it is acting as an obstruction creating an additional 15- to 20-dB attenuation and an increase in the root-mean-square delay spread by roughly 100 ns. A Nakagami distribution is found to well describe the statistics of the small-scale fading, by using Akaike's Information Criterion and the Kolmogorov-Smirnov test. The distance dependence of the path loss is analyzed and a stochastic model is developed.

Radio based localization and tracking usually require multiple receivers/transmitters or a known floor plan. This paper presents a method for anchor free indoor positioning based on single antenna ultra wideband (UWB) measurements. By using time of arrival information from multipath propagation components stemming from scatterers with different, but unknown, positions we estimate the movement of the receiver as well as the angle of arrival of the considered multipath components. Experiments are shown for real indoor data measured in a lecture room with promising results. Simultaneous estimation of both receiver motion, transmitter and scatterer positions is performed using an factorization based approach followed by non-linear least squares optimization. A RANSAC approach to automatic matching of data has also been implemented and tested. The resulting reconstruction is compared to ground truth motion as given by the antenna positioner. The resulting accuracy is in the order of one cm.

Massive multiple-input multiple-output (MIMO) systems, where the base station (BS) is equipped with a large number of antennas and the mobile devices have a single antenna, can significantly enhance the system performance. In many wireless systems inter symbol interference (ISI) due to delay dispersion of the channel can dramatically affect the demodulation process of the received signals. Precoding in massive MIMO can reduce or eliminate ISI, while still exploiting the spatial diversity. In this paper, we investigate how much standard linear precoders help the ISI mitigation by shortening the rms delay spread. In order to evaluate the system performance, we measured a massive MIMO channel with 128 antennas at the base station and 36 users, including 26 line-of-sight and 10 non line-of-sight, each equipped with a single antenna. We also compare the results from the measurements with an independent identically distributed Gaussian channel with an exponentially decaying average power delay profile.

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.

The COST 2100 channel model is a geometry-based stochastic channel model (GSCM) for multiple-input multiple-output (MIMO) simulations. This paper presents parameterization and validation of the channel model for peer-to-peer communication in the 300 MHz band. Measurements were carried out in outdoor environments for both line-of-sight (LOS) and non line-of-sight (NLOS) scenarios. The COST 2100 channel model is characterized and parameterized based on clusters. The KpowerMeans algorithm and a Kalman filter are used for identifying and tracking clusters from measurements. General issues regarding the parameterization of the channel model are analyzed in detail. A full set of single-link parameters for the channel model is extracted from the measurements. These parameters are used as the input to the channel model validation processes, targeting delay spread, spatial correlation, and singular value distribution as well as antenna correlation. The validation results show good agreement for the spatial correlation and singular value distribution between the channel model simulations and the 300 MHz outdoor measurements. Our findings suggest that the model has potential for modeling 300 MHz channels in outdoor environments, although some modifications are needed for the distribution of cluster delay spreads and the size of cluster visibility regions.

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.

This paper presents a double directional analysis of vehicle-to-vehicle channel measurements conducted in three different traffic scenarios. Using a high- resolution algorithm, we derive channel parameters like Angle-of-Arrival (AOA), Angleof- Departure (AOD), propagation delay and Doppler shift and identify underlying propagation mechanisms by combining these estimates with maps of the measurement sites. The results show that first-order reflections from a small number of interacting objects can account for a large part of the received signal in the absence of line-of-sight (LOS). This effect is especially pronounced in the two traffic scenarios where the road is not lined with buildings. We also found that the direction spread is low (and conversely that the antenna correlation is high) in such scenarios, which suggests that beamforming rather than diversity-based methods should be used if multiple antenna elements are available. The situation is reversed, however, in the third scenario, a narrow urban intersection, where a larger number of higher-order reflections is found to result in a higher direction spread.

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.

The radio channels between nodes of an indoor peer-to-peer network show specific fast fading characteristics. Depending on the mobility and on the scattering properties of the environment, different kinds of fading distributions can occur: Ricean fading between static nodes, but also Rayleigh or even double-Rayleigh fading between mobile nodes. We investigate fast fading in indoor peer-to-peer networks based on radio channel measurements. It turns out that the fading statistics change over time. While the predominant fading mechanism is a combination of Rayleigh and double-Rayleigh fading, Ricean fading also occasionally occurs. On top of that, indoors, the statistics of the fast fading change over time even for small-motions of the nodes, since the propagation environment is inhomogeneous. We comprehensively model these effects using a hidden Markov model, parameterized from our measurements. The model is validated, revealing a convincing fit between the model and the measurements.

Wireless communication using very-large multiple-input multiple-output (MIMO) antennas is a new research field, where base stations are equipped with a very large number of antennas as compared to previously considered systems. In theory, as the number of antennas increases, propagation properties that were random before start to become deterministic. Theoretical investigations with independent identically distributed (i.i.d.) complex Gaussian (Rayleigh fading) channels and unlimited number of antennas have been done, but in practice we need to know what benefits we can get from very large, but limited, number of antenna elements in realistic propagation environments. In this study we evaluate properties of measured residential-area channels, where the base station is equipped with 128 antenna ports. An important property to consider is the orthogonality between channels to different users, since this property tells us how advanced multi-user MIMO (MU-MIMO) pre-coding schemes we need in the downlink. We show that orthogonality improves with increasing number of antennas, but for two single-antenna users there is very little improvement beyond 20 antennas. We also evaluate sum-rate performance for two linear pre-coding schemes, zero-forcing (ZF) and minimum mean squared error (MMSE), as a function of the number of base station antennas. Already at 20 base station antennas these linear pre-coding schemes reach 98% of the optimal dirty-paper coding (DPC) capacity for the measured channels.

Directional properties of the radio channel are of high importance for the development of reliable wireless systems operating in the 60 GHz frequency band. Using transfer functions measured from 61 to 65 GHz in a conference room we have extracted estimates of the multi-path component parameters using the SAGE algorithm. In the paper we compare results for line-of-sight (LOS) scenarios and the corresponding non-line-of-sight (NLOS) scenarios and present values of the direction spread at the Tx and the Rx.

A millimeter-wave (mm-wave) impulse radio is an attractive alternative to existing high-speed mm- wave radio systems because of the potential for a simpler transceiver architecture. This paper studies the feasibility of a mm-wave impulse radio system for home and office use by considering state-of-the- art transceivers and multiple-input multiple-output measured propagation channels as well as the IEEE 802.15.3c channel model. Our analysis reveals that reliable data transmission is infeasible even in line-of-sight (LOS) scenarios because of a low power level at the Rx if we do not use beamforming. However, introducing 7×7 beamforming at the Tx dramatically improves the coverage beyond 5 m distance in the LOS scenario and up to 5 m in a non- line-of-sight (NLOS) scenario, though the performance varies in the NLOS scenario, depending on the Tx and Rx locations. We propose an adaptive transmit signaling scheme that adjusts the pulse repetition frequency depending on the delay dispersion of the propagation channel in order to avoid intersymbol interference and keep the Rx structure simple. The proposed transmit signaling scheme leads to a pulse transmission rate of 250 Mpulses/s in all measured channels while the rate is lower when the 802.15.3c model is considered because of a more multipath-rich characteristics than our measured channels.

Based on static outdoor channel measurements we evaluate the influence of a vehicle on the MIMO radio channel, from a base station antenna array, to a multiple antenna handset in the hand of a user placed inside a test car. The measurement scenario is chosen to mimic a 2.6 GHz (LTE) macro-cell urban or rural scenario with two locations and orientations of the car, one at an open parking lot with a strong line-of-sight component, and one between buildings with no line-of-sight. The measurements are repeated several times with the user phantom plus handset positioned at the same spot within the car and with the car absent. Figures of the penetration loss, impact on fading statistics, mean delay, delay spread, terminal antenna correlation, eigenvalue distributions, as well as the performance of various hybrid diversity combining and spatial multiplexing schemes, are evaluated and compared with and without the vehicle present. It is found that the car make the channel statistics become more Rayleigh like and increases multipath channel richness, improving the potential of diversity gain and, to some extent, spatial multiplexing.

Multi-site measurements for urban macro cells at 2.66 GHz are performed with three base stations and one mobile station. In order to analyze the correlation properties of large scale parameters, we split up the routes into subsets, where it can be assumed that wide-sense stationarity (WSS) applies. The autocorrelation distance and correlation properties of large scale parameters for each link are analyzed. By comparing these properties with the corresponding parameters from the COST 2100 and WINNER II models, we can see that the measured autocorrelation distance of the shadow fading as well the autocorrelation distance of delay spread have similar properties as in the two models. The shadow fading and delay spread from the same link are negatively correlated and match the two models well. Based on the WSS subsets, we can see that large scale parameters for different links can be correlated, also when two BSs are far away from each other. In those cases the correlation of different links tends to be positively correlated when both base stations are in the same direction compared to the movement of the MS, otherwise the two links are usually negatively correlated.

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.

Beamforming is an important feature of 60 GHz communications. We present an analysis of the influence of beamforming in indoor ultrawideband radio channels measured in the mm-wave 60 GHz band. The performance of narrowband and wideband direction-based beamformers is investigated in terms of improving channel metrics such as the delay spread, excess delay, and the signal-to-noise ratio (SNR). The performance of the direction-based beamformers is compared with dominant eigenmode transmission and statistical beamforming. Our analysis reveals that in line-of-sight (LOS) scenarios, the two direction-based beamformers have a similar performance that approaches the upper bound set by dominant eigenmode transmission. In non-LOS (NLOS) scenarios, the direction-based beamformers show a performance degradation in relation to the upper bound, with the narrowband beamformer worse off than the wideband variant. The array gain in our measured NLOS scenarios is observed to exceed the theoretical upper limit valid for a rich scattering environment. We show that this result follows from the spatial structure of the measured NLOS channels that has only a few strong reflected components. We investigate the influence of array size on beamforming performance; 5×5 planar arrays are observed to improve the channel's delay metrics as well as the larger 7×7 planar arrays.

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.

This paper presents the results of an empirical study of wireless propagation channels for vehicle-to-vehicle communications in street intersections, a scenario especially important for collision avoidance applications. The results are derived from a channel measurement campaign performed at 5.6 GHz in four different types of urban intersections. We present results on typical power delay profiles, pathloss and delay spreads and discuss important propagation mechanisms. By comparing the results of the different intersections, we find that absence of line-of-sight is problematic for system coverage, especially when there are few other significant scattering objects in and around the intersection. Roadside buildings can create important propagation paths that account for a considerable part of the total received power.

There has been extensive research about single link multiple-input multiple-output (MIMO) channels, both simulated and measured, but only few studies have targeted multilink analysis with measured channel data. Hence, in this paper, we analyze the influence of spatial separation of user terminals and the human shadowing effect on the MIMO capacity and to the signal to interference ratio (SIR) in a time-variant dual-link scenario. The paper analyses the dual-link channel capacity based on simulations as well as measurements conducted using dynamic dual-link dual-polarized wideband MIMO channel sounder in an indoor office environment, and compare the results to the singlelink capacity. Here the analysis is restricted to 4 x 4 MIMO links. The following conclusions have been obtained from the analyses: 1) Compared to the single link case, the dual-link channel capacity is more affected by the SIR than the spatial separation of the users. 2) The human shadowing introduced variations of SIR up to ±10 dB, causing large fluctuations in the dual-link capacity.

This study presents sample measurements and analysis characterising the radio channel for outdoor short-range sensor networks. A number of transmit and receive antennas are placed on the ground in an open area. The measured propagation channel is time varying because of the controlled motion of a person walking in the vicinity of the nodes. The statistics of both the line-of-sight (LOS) path and the scattered component of the measured channel are observed to be non-stationary. The channel (power) gains are found to be significantly influenced by the pedestrian movement, only when the LOS path is momentarily blocked. The authors present a generic approach to model receive signal fluctuations because of body blockage of the LOS path. Our approach, which is similar to the referenced work of Pagani and Pajusco, additionally models the time-variant Doppler spectrum of the residue (scattered) component of the measured channel, that is the remainder of the measured channel after the LOS path has been extracted. The proposed modelling approach is parameterised and validated from the measurements.

The immediate environment of handset antennas, including the casings and the users holding the handsets, has a strong impact on the radio channel in mobile communication. In this paper we investigate a composite channel method that synthetically combines double-directional measurements of the user-less propagation channel with measured super-antenna patterns, i.e., patterns of the combined antenna-casing-user arrangement. We experimentally evaluate the method by comparing results (power, capacity, and eigenvalue distribution) obtained from this composite method with direct measurements in the same environment. The measurements were done in two static 8 × 4 MIMO scenarios at 2.6 GHz, with the user indoors and the base station located outdoors and indoors, respectively. A realistic user phantom together with a"smart-phone" handset mock-up with four antenna elements was used, and different configurations and orientations were tested. The method gives statistical distributions of the MIMO eigenvalues, that are close to the measured. By using the composite method, we found that the user, apart from introducing hand and body loss that mainly decreases the SNR of the channel, slightly increases the correlation between the fading at the antenna elements.

Recent years have seen an increasing attention given to wireless Personal Area Networks (PANs), which are typically networks with small transmitter-receiver separation. The desire for high data rates has led to an interest in deploying multipleinput multiple-output (MIMO) transmission for such systems, but up until this date there exists, to the authors¿ best knowledge, no MIMO channel model that enables performance simulations of such systems. An important characteristic of PANs, and at the same time an important difference to regular wireless local area networks, is the interaction between the antenna array and the user. In conjunction with the irregular antenna arrangements that are typical for PAN devices, this has been shown to lead to flexible channel statistics. In this paper we present a MIMO model for PANs that incorporates these effects by prescribing different small-scale statistics and gains to different antenna elements. The proposed model can thus be seen as a generalization of the classical MIMO model for line-of-sight situations. The model is compared to several sets of measurement data and found to provide a very good description of the essential PAN channel characteristics. We also provide a detailed parameterization of the model for a particular PAN scenario.

This paper presents results from an outdoor measurement campaign for ultra-wideband channels at gas stations. The results are particularly relevant for "infostations" where large amounts of data are downloaded to a user within a short period of time. We describe the measurement setup and present a novel high-resolution algorithm that allows the identification of the scatterers that give rise to multipath components. As input, the algorithm uses measurements of the transfer function between a single-antenna transmitter and a long uniform linear virtual array as receiver. The size of the array ensures that the incoming waves are spherical, which improves the estimation accuracy of scatterer locations. Insight is given on how these components can be tracked in the impulse response of a spatially varying terminal. We then group the detected scatterers into clusters, and investigate the angular power variations of waves arriving at the receiver from the clusters. This defines the cluster's "radiation pattern." Using sample measurements we show how obstacles obstruct the line-of-sight component -- a phenomenon commonly referred to as "shadowing." We compare the measurement data in the shadowing regions (locations of the receiver experiencing shadowing) with the theoretical results predicted by diffraction theory and find a good match between the two.

In this paper we establish a geometry-based stochastic ultra-wideband channel model for gas stations. We statistically describe the two-dimensional spatial location and power of clustered scatterers, and the shape of their visibility and shadowing regions. We also separately model the diffuse part of the impulse response (i.e., the part that cannot be explained by the scatterers' multipath components), and show that its amplitude fading statistics can be best described by a Weibull distribution with a delay dependent beta-parameter. A step-by-step implementation recipe demonstrates how the model can be built. Finally, we validate our model by comparing simulated and measured channel parameters such as the rms delay spread.

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 paper radio wave scattering processes in an indoor hall environment are reported by analyzing double-directional multiple-input-multiple-output (MIMO) channel sounding data at 5.3 GHz. The pathways and scattering processes of the dominant propagation phenomena were identified using a measurement-based ray tracer (MBRT) software. It is shown that the dominant propagation phenomena included a line-of-sight (LOS) or obstructed LOS component, single, double, and triple bounce scattering processes from walls and ground. The proportion of power carried by each identified pathway was studied. The number of active pathways varied between 3 and 8, which indicates that the multipath environment changes significantly during the measurement route.

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.

Sensor networks and ad-hoc networks, where nodes inter-communicate without fixed infrastructure, have recently attracted interest due to potential use in industrial, environmental, and safety-related applications. The fading statistics of the propagation channels between sensor nodes are essential to determine the possible data rate, outage, and latency of sensor networks. This paper presents (to the best of our knowledge) the first in-depth analysis, based on measurements, of the propagation channels between typical sensor node locations in office environments. We find that the amplitude fading distribution can be characterized as Ricean. The Rice factor is analyzed as a function of distance and it is determined that it is not a monotonically decreasing function. Even in pure line-of-sight situations, Rice factors show a random behavior and are on the order of 10 or less. We propose models for the small- and large-scale fading correlation. A simulation model based on our analysis is also provided. Our results have relevance for the analysis of bit error rates and diversity order in clustered sensor networks.

This paper aims at validation of an iterative receiver for multiple-input multiple-output with orthogonal frequency division multiplexing (MIMO-OFDM) systems using real-measurement channel data from an indoor scenario. The receiver performs iterative multi-user detection (MUD) and Channel Estimation (CE) via soft information from the single- user decoders. The channel measurements were performed for a dynamic dual MIMO link scenario. The case with two users with multiple antennas interfering each other is considered. CE at the receiver exploits the frequency correlation of the MIMO link. Simulation results for the performance are shown in terms of bit- error rate (BER) vs. signal-to-noise ratio (SNR). Performance for the whole system are provided and compared with respect to the case of perfect channel-state information (PCSI) at the receiver, as well as for the single user. We also provide an analysis of BER with respect to signal-to-interference ratio (SIR). CE performance are evaluated in terms of normalized mean square error (NMSE).

The immediate environment of the transmit (TX) and receive (RX) antennas, including the antenna casings and the users holding the antennas, has a strong impact on the propagation channel and thus on wireless systems. In this paper we experimentally evaluate a method that synthetically combines double-directional measurements of the propagation channel (without the user influence) with measured antenna patterns of antennas-plus-users, by comparing obtained sample results with direct measurements in the same environment. The measurements are done for a static microcell 8times4 MIMO scenario at 2.6 GHz. A realistic user phantom was used together with a test terminal prototype with four antenna elements, and a number of different configurations and orientations of the phantom were tested. In average over all test cases, the mean signal power deviation between composite channel method and measurements was well within 1 dB. The composite method shows 6% higher terminal antenna correlation but similar statistical distributions as the measured. The differences between the model and measurements for the strongest eigenvalue (relevant for MRC combining) was found to be within 1 dB above 10% outage level. Relative deviations in the ergodic MIMO capacity were smaller than 10%.

We present a new high delay resolution method to detect ultra-wideband (UWB) scatterers when using frequency domain measurements. Our approach makes use of the impulse response envelope amplitudes and delays measured over a distance that is larger than the region of stationarity, and detects the 2D coordinates of the channel scatterers, assuming that only single-scattering (single-interaction) processes occur. The identification methodology is based on multiple application of interference cancellation: at every step, we detect the strongest scatterer from an array of measurements, save its information, cancel it from the channel and search for the next strongest scatterer. To precisely define the strength of each scatterer, we present a method to define its birth and death locations along the measurement array. Finally, we verify the method by applying it to measurement results in an outdoor environment; the scatterer locations identified from the measurements show excellent agreement with the physically present objects like walls and columns.

Personal area networks (PANs) are wireless communications systems with high data rates but small coverage area. PAN propagation channels differ from the well-explored propagation channels of wide-area networks due to several reasons: (i) the distances are typically very small, (ii) the antenna arrangements can be quite different, and (iii) the influence from human presence in the environment is different. The current paper presents results of a channel measurement campaign, where measurements are conducted over distances of 1-10 m using several multi-antenna devices, combined to create different PAN scenarios. For each measured Tx-Rx separation, channel realizations are obtained by small spatial movements of the antenna devices, and by rotating the persons holding the devices. From the results, we draw two main conclusions: (i) The small scale amplitude statistics, analyzed as the variations over a small sampling area and frequency subchannels, cannot be described in a satisfactory way using only the Rayleigh or Ricean distribution, rather a mixed distribution, the generalized gamma distribution, is more suitable; (ii) it is advantageous to distinguish between two types of large-scale fading: body shadowing (due to the orientation of the person holding the device) and shadowing due to surrounding objects (lateral movement). We also define and parameterize a complete statistical model for all fading.

Ad-hoc networks and sensor networks, where nodes communicate with each other without fixed infrastructure, are of great importance for many industrial, environmental, and safety-related applications. The fading statistics of the propagation channels between sensor nodes are essential to determine the possible data rate, outage, and latency of sensor networks. In this paper, we present results from a recent measurement campaign that for the first time provides an in-depth analysis of the propagation channels between typical sensor node locations in office environments. We analyze the Rice factor as a function of distance and find that it is not a monotonically decreasing function. Even in pure LOS situations, Rice factors are on the order of 10 or less. This has important consequences for analysis of bit error rates, diversity order, and diversity/multiplexing tradeoff in clustered sensor networks.

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.

The results of three ultra-wideband (UWB) measurement campaigns conducted in two different industrial environments are presented. A frequency range of 3.1-10.6 or 3.1-5.5 GHz was measured using a vector network analyzer and a virtual array technique enabling the investigation of small-scale statistics. The results show that the energy arrives in clusters, and that the abundance of metallic scatterers present in the factory hall causes dense multipath scattering. The latter produces a small-scale fading that is mostly Rayleigh distributed; the only exception being the delay bin containing the line-of- sight component. The power delay profile can be modeled by a generalized Saleh-Valenzuela model, where different clusters have different ray power decay constants. It is also noted that the number of multipath components required to capture a majority of the energy is quite large. More than a hundred components can be needed to capture 50% of the total available energy.

This paper presents results from the first double-directional measurements of the multiple-input multiple-output (MIMO) peer-to-peer radio channel in the 300 MHz frequency range. The measurements were performed with vertically polarized circular antenna arrays mounted on cars driving along a route in a semi rural and sub-urban environment. We evaluate the measurements with a high resolution algorithm, thus obtaining a wideband, double-directional characterization of the propagation channel. The delay- and directional spread, as well as the small-scale fading statistics are analyzed along the measurement routes and major scatterers are identified. The measurements, performed with low antennas heights, show high angular spreads, caused by reflections from trees and buildings and by diffractions over terrain irregularities. We further observe multiple clusters in both the delay and angular domains. The large spreads in these domains indicate a high degree of available diversity, and rich multipath in the considered environment.

We investigate the diversity gain of MIMO systems with antenna selection in measured propagation channels for wireless personal area networks. We measure both the communication from an access point to a laptop, and between two handheld devices. Both the transmitter and receiver use antenna selection for diversity transmission and reception. We consider a closed-loop system where the transmitter has full channel state information, and analyze a number of different antenna selection algorithms and signal combining methods. We find that line of sight (LOS) and non-line of sight (NLOS) situations have fairly similar behavior, and that different polarizations result in similar SNR gains. We also find that RF-preprocessing of the signals is less effective for the hand held devices scenario than for the access point and laptop scenario. Finally, we compare bulk selection (same antenna subset is used for all frequency sub-channels) to per-tone selection (different antenna subsets can be used for each frequency sub-channel) for a wideband channel.

In this paper we analyze the effects of body shadowing in multiple-input-multiple-output (MIMO) channels used for personal area networks (PANs). We give physical reasoning to why PANs may experience two different types of shadowing, and to support our argument, we present results from a measurement campaign for three different PAN channels with human influence. The campaign is performed using different types of multi-element antenna devices; an access point, a body-worn device and two hand-held devices, conducted over a series of distances between 1 -10 m. For each distance, a number of channel realizations are obtained by moving the antenna devices over a small area, and by rotating the persons holding the devices. The results show that it is suitable to distinguish between body shadowing (due to the rotation of the person holding the device) and shadowing due to surrounding objects (lateral movement). We also present a statistical model where the two types of shadowing are described as separate log-normal processes. Furthermore, we find that body shadowing has a big influence on the capacity of the investigated PAN channels.

In this paper, we present a cluster based analysis of an outdoor-to-indoor Multiple-Input Multiple-Output (MIMO) measurement campaign, and extract model parameters for the COST273 channel model. The measurements were performed at 5.2 GHz for 159 measurement locations in an office building. Multipath component (MPC) parameters have been extracted for these positions using a high-resolution algorithm. We analyze the clustering of MPCs, i.e., grouping together of MPCs with similar DOAs, DODs, and delays. We compare cluster identification by visual inspection to automatic identification by the recently proposed algorithm of Czink et al. In the paper we include results on the intercluster properties such as the distribution of the number of clusters and the cluster powers, as well as intracluster properties such as the angle and delay spreads within the clusters. In particular, we extract parameters for the COST 273 channel model, a standardized generic model for MIMO propagation channels.

Fourth-generation (4G) systems are expected to support data rates of the order of 100 Mb/s in the outdoor environment and 1 Gb/s in the indoor/stationary environment. In order to support such large payloads, the radio physical layer must employ receiver algorithms that provide a significant increase in spectrum efficiency (and, hence, capacity) over current wireless systems. Recently, an explosion of multiple-input-multiple-output (MIMO) studies have appeared with many journals presenting special issues on this subject. This has occurred due to the potential of MIMO to provide a linear increase in capacity with antenna numbers. Environmental considerations and tower loads will often restrict the placing of large antenna spans on base stations (BSs). Similarly, customer device form factors also place a limit on the antenna numbers that can be placed with a mutual spacing of 0.5 wavelength. The use of cross-polarized antennas is widely used in modern cellular installations as it reduces spacing needs and tower loads on BSs. Hence, this approach is also receiving considerable attention in MIMO systems. In order to study and compare various receiver architectures that are based on MIMO techniques, one needs to have an accurate knowledge of the MIMO channel. However, very few studies have appeared that characterize the cross-polarized MIMO channel. Recently, the third-generation partnership standards bodies (3GPP/3GPP2) have defined a cross-polarized channel model for MIMO systems but this model neglects the elevation spectrum. In this paper, we provide a deeper understanding of the channel model for cross-polarized systems for different environments and propose a composite channel impulse model for the cross-polarized channel that takes into account both azimuth and elevation spectrum. We use the resulting channel impulse response to derive closed-form expressions for the spatial correlation. We also present models to describe the dependence of cross-polarization discrimination (XPD) on distance, azimuth and elevation and delay spread. In addition, we study the impact of array width, signal-to-noise ratio, and antenna slant angle on the mutual information (MI) of the system. In particular, we present an analytical model for large system mean mutual information values and consider the impact of elevation spectrum on MI. Finally, the impact of multipath delays on XPD and MI is also explored.

It has been predicted theoretically that for some environments, the capacity of wireless multiple-input multiple-output systems can become very low even for uncorrelated signals; this effect has been termed "keyhole" or "pinhole". In this paper the first unique measurements of this effect are presented. The measurements were performed in a controlled indoor environment that was designed to obtain a keyhole channel. We analyze limitations due to measurement imperfections for measurement-based capacity calculations and keyhole investigations. We further present a bound for the higher eigenmodes as a function of the finite measurement signal-to-noise ratio and multipath component leakage. The bound is compared to the measurement results and shows excellent agreement. Finally, we analyze the envelope distribution and, as expected from theory, it follows a double-Rayleigh distribution

Transmitted-reference (TR) schemes for time-hopping impulse radio (TH-IR) ultra-wideband (UWB) communications allow the use of simple receiver structures that are able to combine energy from different multipath components without channel estimation. A conventional TR receiver consists of a simple delay-and-multiply operation combined with an integrator. On the downside, it shows a performance loss due to non-linear operations on noise terms (generation of noise-noise cross-terms) when forming the decision variable. This paper describes a hybrid receiver structure for UWB communications that reduces these noise-noise cross-terms by first performing a "matched filtering" operation matched to the time-hopping sequence of pulses. The receiver retains most of the simplicity of the conventional TR receiver, but requires an analog correlator for the time-hopping sequence of pulses. The performance the proposed receiver is analyzed in both AWGN and multipath channels. For the AWGN case, the exact expression for the bit error probability is obtained, which takes into account the nonGaussian nature of the noise-noise cross-terms arising in the correlators. For the multipath case, both inter-frame interference and multipath interference from the reference pulse to the data pulse are considered, and approximate closed-form expressions are derived based on the assumption of a large integration interval. Also approximate criteria for optimal integration interval are obtained for the best receiver performance. Simulation studies are presented to analyze the performance of the proposed receiver structure and to confirm the theoretical analysis

We consider a time-hopping impulse-radio system that uses transmitted-reference pulses for implicit channel estimation and equalization. A hybrid receiver structure first performs a filtering matched to the hopping sequence, and a subsequent correlation of the data pulses with the reference pulses. We analyze the performance of such a system both in AWGN and in multipath. For the AWGN case, we give exact expressions for the bit error probability that take into account the non-Gaussian nature of the noise-noise crossterms arising in the correlators. For the multipath case, we analyze inter-frame interference, as well as multipath interference from the reference pulse to the data pulse, providing dosed-form equations in the limit of a large number of multipath components.

In this paper, we present the (to our knowledge) first measurement results for ultra-wideband channels in industrial environments, i.e., a factory hall. The measurements are done with virtual arrays, which allows analysis of the small-scale fading statistics, as well as a directional analysis. We find that there is dense multipath scattering due to the abundance of metallic scatterers in the considered environment. Multiple scatterer clusters can be identified both in the delay and the angular domain. Typical rms delay spreads lie between 30 ns for LOS scenarios and 40 ns for NLOS scenarios. For non-LOS scenarios at large distances, the maximum of the power delay profile is observed some 40 ns after the arrival of the first multipath components. We also draw conclusions about the behavior of typical UWB system designs in the measured channels.

We analyze the channel capacity of multiple-input multiple-output (MIMO) systems in frequency selective channels, with channel knowledge at the transmitter side. An optimum transmission scheme uses Shannon's principle of water filling jointly in the frequency and the spatial domain. However, we show that the largest gain by using water filling resides in the spatial domain, and that the capacity gain from the frequency domain is very small for MIMO systems. We quantify the gain for a theoretical Rayleigh fading channel model and for microcellular channels as measured in an enclosed courtyard.