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Cellular-connected unmanned aerial vehicle (UAV) has attracted a surge of research interest in both academia and industry. To support aerial user equipment (UEs) in the existing cellular networks, one promising approach is to assign a portion of the system bandwidth exclusively to the UAV-UEs. This is especially favorable for use cases where a large number of UAV-UEs are exploited, e.g., for package delivery close to a warehouse. Although the nearly line-of-sight (LoS) channels can result in higher powers received, UAVs can in turn cause severe interference to each other in the same frequency band. In this contribution, we focus on the uplink communications of massive cellular-connected UAVs. Different power allocation algorithms are proposed to either maximize the minimal spectrum efficiency (SE) or maximize the overall SE to cope with severe interference based on the successive convex approximation (SCA) principle. One of the challenges is that a UAV can affect a large area meaning that many more UAV-UEs must be considered in the optimization problem, which is essentially different from that for terrestrial UEs. The necessity of single-carrier uplink transmission further complicates the problem. Nevertheless, we find that the special property of large coherent bandwidths and coherent times of the propagation channels can be leveraged. The performances of the proposed algorithms are evaluated via extensive simulations in the full-buffer transmission mode and bursty-traffic mode. Results show that the proposed algorithms can effectively enhance the uplink SEs. This work can be considered the first attempt to deal with the interference among massive cellular-connected UAV-UEs with optimized power allocations., Comment: The final version can be found in IEEE Transactions on Vehicular Technology

Navigation with terrestrial wireless infrastructure is appealing to overcome geometrical limitations of satellite navigation for users in environments with limited sky views. However, terrestrial signals are also prone to multipath that can result in angular and range estimates that are not representative of actual transmitter-receiver geometry. In this paper, some of these propagation effects are quantified for a particular urban non line-of-sight (NLOS) scenario, based on measurements of downlink reference symbols transmitted by a commercial Long Term Evolution (LTE) base station (eNodeB) and received by a massive antenna array mounted on a passenger vehicle. Empirical results indicate that large-scale statistics for a user making multiple passes through the same urban environment look similar when represented in terms of angles and delays despite changes in orientation and drive direction. Additionally, it is demonstrated that multipath effects can be utilized advantageously; it is possible to estimate not only user position but also orientation through wireless fingerprinting.

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.

Millimeter-Wave (mmWave) massive multiple-input multiple-output (MIMO) has been considered as a key enabler for the fifth-generation (5G) communications. It is essential to design and test mmWave 5G devices under various realistic scenarios, since the radio propagation channels pose intrinsic limitations on the performance. This requires emulating realistic dynamic mmWave channels in a reproducible manner in laboratories, which is the goal of this paper. In this contribution, we firstly illustrate the dominant-cluster(s) concept, where the non-dominant clusters in the mmWave channels are pruned, for mmWave 5G devices applying massive MIMO beamforming. This demonstrates the importance and necessity to accurately emulate the mmWave channels at a cluster level rather than the composite-channel level. Thus, an over-the-air (OTA) emulation strategy for dynamic mmWave channels is proposed based on the concept of dominant-cluster(s) in a sectored multiprobe anechoic chamber (SMPAC). The key design parameters including the probe number and the angular spacing of probes are investigated through comprehensive simulations. A cost-effective switchcircuit is also designed for this purpose and validated in the simulation. Furthermore, a dynamic mmWave channel measured in an indoor scenario at 28-30 GHz is presented, where the proposed emulation strategy is also validated by reproducing the measured reality., Comment: Accepted by IEEE Transactions on Antennas and Propagation

Propagation models constitute a fundamental building block of wireless communications research. Before we build and operate real systems, we must understand the science of radio propagation, and develop channel models that both reflect the important propagation processes and allow a fair comparison of different systems. In the past five decades, wireless systems have gone through five generations, from supporting voice applications to enhanced mobile broadband. To meet the ever increasing data rate demands of wireless systems, frequency bands covering a wide range from 800 MHz to 100 GHz have been allocated for use. The standardization of these systems started in the early/mid 1980s in Europe by the European Telecommunications Standards Institute with the advent of Global System for Mobile Communications. This motivated the development of the first standardized propagation model by the European Cooperation in Science and Technology (COST) 207 working group. These standardization activities were continued and expanded for the third, fourth, and fifth generations of COST, as well as by the Third Generation Partnership Project, and the International Telecommnunication Union. This paper presents a historical overview of the standardized propagation models covering first to fifth-generation systems. In particular, we discuss the evolution and standardization of pathloss models, as well as large and small-scale fading parameters for single antenna and multiple antenna systems. Furthermore, we present insights into the progress of deterministic modelling across the five generations of systems, as well as discuss more advanced modelling components needed for the detailed simulations of millimeter-wave channels. A comprehensive bibliography at the end of the paper will aid the interested reader to dig deeper.

ispartof: pages:12-17 ispartof: Proceedings of the ... ACM/IEEE Joint Conference on Digital Libraries. ACM/IEEE Joint Conference on Digital Libraries pages:12-17 ispartof: EmergingWireless '22: Proceedings of the 1st International Workshop on Emerging Topics in Wireless location:Rome, Italy date:9 Dec - 9 Dec 2022 status: Published online

As ultra-massive multiple-input multiple-output (UM-MIMO) has emerged as a key technology for millimeter-wave and terahertz communications, the spherical wave propagation should be considered for channel modeling. Therefore, it is critical to identify the locations and evolving behaviors of scatterers, i.e., the sources of the spherical wavefronts. In this contribution, a novel space-alternating generalized expectation-maximization (SAGE) based scatterer localization algorithm is proposed, where a large-scale antenna array is divided into multiple sub-arrays. Due to the decreased aperture of each sub-array, plane wave assumption can be applied to estimate the angles of departure/arrival, delays and amplitudes of multipath components (MPCs). Based on the angle variations of MPCs observed at different sub-arrays, the corresponding scatterers can be located. The proposed algorithm is verified in a simulation using a large-scale uniform circular array (UCA) system. Moreover, we apply this algorithm to an indoor measurement campaign conducted at 27-29 GHz in a hall scenario. Dominant scatterers are identified, which can be used for the development of further geometry-based stochastic channel models.

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.

This paper presents a shared vision among stake- holders across the value chain on the use of radio positioning and sensing for road safety in the 5G ecosystem. The key enabling technologies and architectural functionalities are explored, focus- ing on the extremely stringent localization and communication requirements. A case study for joint radar and communication using experimental data showcases the potential of the new enablers that are paving the way towards enhanced road safety in Beyond 5G scenarios.

Millimeter-wave cellular communication requires beamforming procedures that enable alignment of the transmitter and receiver beams as the user equipment (UE) moves. For efficient beam tracking it is advantageous to classify users according to their traffic and mobility patterns. Research to date has demonstrated efficient ways of machine learning based UE classification. Although different machine learning approaches have shown success, most of them are based on physical layer attributes of the received signal. This, however, imposes additional complexity and requires access to those lower layer signals. In this paper, we show that traditional supervised and even unsupervised machine learning methods can successfully be applied on higher layer channel measurement reports in order to perform UE classification, thereby reducing the complexity of the classification process., Accepted for Publication in 2021 IEEE International Workshop on Machine Learning for Signal Processing, 6 Pages, 7 Figures, 1 Table

Wireless-based activity sensing has gained significant attention due to its wide range of applications. We investigate radio-based multi-class classification of human activities using massive multiple-input multiple-output (MIMO) channel measurements in line-of-sight and non line-of-sight scenarios. We propose a tensor decomposition-based algorithm to extract features by exploiting the complex correlation characteristics across time, frequency, and space from channel tensors formed from the measurements, followed by a neural network that learns the relationship between the input features and output target labels. Through evaluations of real measurement data, it is demonstrated that the classification accuracy using a massive MIMO array achieves significantly better results compared to the state-of-the-art even for a smaller experimental data set., accepted for publication in IEEE Wireless Communications Letters

Smart vehicles are emerging as a possible solution for multiple concerns in road traffic, such as mobility and safety. This work presents radio localization methods based on simultaneous direction of arrival (DOA), time-delay, and range estimation using the SAGE algorithm. The proposed methods do not rely on external sources of information, such as global navigation satellite systems (GNSS). The proposed methods take advantage of signals of opportunity and do not require the transmission of location-specific signals; therefore, they do not increase the network load. A set of simulations using synthetic and measured data is provided to validate the proposed methods, and the results show that it is possible to achieve accuracy down to decimeter and centimeter-level.

To enable realistic studies of massive multiple-input multiple-output systems, the COST 2100 channel model is extended based on measurements. First, the concept of a base station-side visibility region (BS-VR) is proposed to model the appearance and disappearance of clusters when using a physically-large array. We find that BS-VR lifetimes are exponentially distributed, and that the number of BS-VRs is Poisson distributed with intensity proportional to the sum of the array length and the mean lifetime. Simulations suggest that under certain conditions longer lifetimes can help decorrelating closely-located users. Second, the concept of a multipath component visibility region (MPC-VR) is proposed to model birth-death processes of individual MPCs at the mobile station side. We find that both MPC lifetimes and MPC-VR radii are lognormally distributed. Simulations suggest that unless MPC-VRs are applied the channel condition number is overestimated. Key statistical properties of the proposed extensions, e.g., autocorrelation functions, maximum likelihood estimators, and Cramer-Rao bounds, are derived and analyzed., Submitted to IEEE Transactions of Wireless Communications

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., Comment: 18 pages (two column), 7 figures, IEEE Transaction on Wireless Communications

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 combing 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 existing power control schemes that consider only homogeneous users and saturated scenarios. Our results show substantial benefits in terms of energy usage can be achieved without sacrificing throughput, and that both signalling overhead and the complexity of end devices can be reduced by abrogating the need for uplink power control through efficient scheduling.

Reliable wireless communication links are a critical but challenging aspect for high-speed railway (HSR). To develop and analyze such systems, accurate models for the propagation channel are required. The radio channel properties in HSR scenarios are different from those in cellular scenarios not only due to the high velocity, but also due to the special construction elements and the type of surroundings along the train lines. We performed a measurement campaign at a carrier frequency of 2.4 GHz on the Guangzhou–Shenzhen dedicated high-speed train line in China. Motivated by the results from these measurements, we propose the concept of active scatterer region as an improvement for geometry-based stochastic channel models for HSR, to better describe the non-stationary properties of such channels. Further contributions to the total channel impulse response are also studied, including the line-of-sight component and the scattering component from overhead line poles. Each type of component is modeled and fully parameterized based on the measurement results.

We present a localization algorithm for low-power long-range Internet-of-things (IoT) networks, which exploits angle of arrival (AoA) and range information from non-coherent received signal strength (RSS) measurements. In this work, each anchor node is equipped with array antennas of known geometry and radiation patterns. The position of the target node and the path-loss exponent to each anchor are unknown and possibly time-varying. The joint estimation problem is formulated with a Bayesian model, where the likelihood functions are derived from the classical path-loss model and an RSS difference model. A message passing method is then exploited for efficient computation of the marginal posterior distribution of each unknown variable. The proposed algorithm is validated using real outdoor measurements from a low-power wide area network based IoT system in a challenging scenario. Results show that the proposed algorithm can adapt to dynamic propagation conditions, and improve the localization accuracy compared to a method that exploits only single geometric feature. Furthermore, the algorithm scales well in different antenna array configurations, and is compatible with various existing IoT standards.

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., Comment: To be published in proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021

In this contribution, a recently conducted measurementcampaign in a suburban scenario for the Unmanned AerialVehicle (UAV) Air-to-Ground (A2G) radio channel is introduced.The downlink signals in an in-service Long Term Evolution(LTE) network were collected and utilized to extract the ChannelImpulse Responses (CIRs). A high-resolution parameterestimation algorithm derived based on the Space-AlternatingGeneralized Expectation-maximization (SAGE) principle is appliedto estimate the delays, Doppler frequencies and complexamplitudes of MultiPath Components (MPCs) from the CIRs.Based on the MPC estimation results, fast fading characteristicsof the A2G channels are investigated. It is found that theRician distribution models the fast fading the best comparedto Nakagami, Lognormal and Rayleigh distributions. Rician Kfactorsare also calculated for the A2G channels.

IoT networks are getting overcrowded following the vast increase in number of Internet-of-Things (IoT) devices and connections. Networks can be extended with more gateways, increasing the number of supported devices. However, as investigated in this work, massive MIMO has the potential to increase the number of simultaneous connections and moreover lower the energy expenditure of these devices. We present a study of the channel characteristics of massive MIMO in the narrowband unlicensed sub-GHz band. The goal is to support IoT applications with strict requirements in terms of number of devices, power consumption, and reliability. The assessment is based on experimental measurements using both a uniform linear and a rectangular array. Our study demonstrates and validates the advantages of deploying massive MIMO gateways to serve IoT nodes. While the results are general, here we specifically focus on static nodes. The array gain and channel hardening effect yield opportunities to lower the transmit power of IoT nodes while also increasing reliability. The exploration confirms that exploiting large arrays brings great opportunities to connect a massive number of IoT devices by separating the nodes in the spatial domain. In addition, we give an outlook on how static IoT nodes could be scheduled based on partial channel state information.

Low-Power Wide-Area Networks operating in the unlicensed bands are being deployed to connect a rapidly growing number of Internet-of-Things devices. While the unlicensed sub-GHz band offers favorable propagation for long-range connections, measurements show that the energy consumption of the nodes is still mostly dominated by the wireless transmission affecting their autonomy. We investigate the potential benefits of deploying massive MIMO technology to increase system reliability and at the same time support low-energy devices with good coverage at sub-GHz frequencies. The impact of different antenna configurations and propagation conditions is analyzed. Both actual average experienced array gain and channel hardening are examined. The assessment demonstrates the effect of channel hardening as well as the potential benefits of the experienced array gain. These measurements serve as a first assessment of the channel conditions of massive MIMO at sub-GHz frequencies and are, to the best of our knowledge, the first of its kind.

We present a belief propagation (BP) algorithm with probabilistic data association (DA) for detection and tracking of specular multipath components (MPCs). In real dynamic measurement scenarios, the number of MPCs reflected from visible geometric features, the MPC dispersion parameters, and the number of false alarm contributions are unknown and time-varying. We develop a Bayesian model for specular MPC detection and joint estimation problem, and represent it by a factor graph which enables the use of BP for efficient computation of the marginal posterior distributions. A parametric channel estimator is exploited to estimate at each time step a set of MPC parameters, which are further used as noisy measurements by the BP-based algorithm. The algorithm performs probabilistic DA, and joint estimation of the time-varying MPC parameters and mean false alarm rate. Preliminary results using synthetic channel measurements demonstrate the excellent performance of the proposed algorithm in a realistic and very challenging scenario. Furthermore, it is demonstrated that the algorithm is able to cope with a high number of false alarms originating from the prior estimation stage.

Massive multiple-input multiple-out (MIMO) technology is vital in millimeter-wave (mmWave) bands to obtain large array gains. However, there are practical challenges, such as high hardware cost and power consumption in such systems. A promising solution to these problems is to adopt a hybrid beamforming architecture. This architecture has a much lower number of transceiver (TRx) chains than the total antenna number, resulting in cost- and energy-efficient systems. In this paper, we present a real-time mmWave (28 GHz) massive MIMO testbed with hybrid beamforming. This testbed has a 64-antenna/16-TRx unit for beam-selection, which can be expanded to larger array sizes in a modular way. For testing everything from baseband processing algorithms to scheduling and beam-selection in real propagation environments, we extend the capability of an existing 100-antenna/100-TRx massive MIMO testbed (below 6 GHz), built upon software-defined radio technology, to a flexible mmWave massive MIMO system., 54th Asilomar Conference on Signals, Systems, and Computers, Nov. 2020

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.

A channel sounder is an advanced measurement system to estimate the behavior of wireless propagation channels. In this paper, we present the antenna array design for a 28GHz massive multiple-input multiple-output system, capable of measuring 256×128 dual-polarized channels, including dynamic properties of angular, delay, Doppler, and polarization parameters, respectively. Unique in comparison to other designs at 28 GHz, our approach is based on a switched, dual polarized patch antenna array architectures at both transmit and receive link ends. The interactions between the switch circuitry and the elements is analyzed and the measured radiation characteristics are compared to the simulated counterparts.

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 front-end 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 interfacing with 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 Cramer-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., Accepted in the Proceedings of IEEE International Conference on Communications (ICC) 2020, Dublin, Ireland

Connected autonomous vehicles and industry 4.0 production scenarios require ultrareliable low-latency communication links. The varying positions of transmitter, reflecting objects, and receiver cause a nonstationary time- and frequency-selective fading process. In this paper, we present the necessary hardware architecture and signal processing algorithms for a real-time geometry-based channel emulator, that is needed for testing of wireless control systems. We partition the nonstationary fading process into a sequence of local stationarity regions and model the channel impulse response as sum of propagation paths with time-varying attenuation, delay, and Doppler shift. We implement a subspace projection of the propagation path parameters, to compress the time-variant channel impulse response. This enables a low data-rate link from the host computer, which computes the geometry-based propagation paths, to the software defined radio unit, that implements the convolution on a field programmable gate array (FPGA). With our new architecture, the complexity of the FPGA implementation becomes independent of the number of propagation paths. Our channel emulator can be parametrized by all known channel models. Without loss of generality, we use a parameterization by a geometry-based stochastic channel model, due to its nonstationary nature. We provide channel impulse response measurements of the channel emulator, using the RUSK Lund channel sounder for a vehicular scenario with 617 propagation paths. A comparison of the time-variant power delay profile and Doppler spectral density of simulated and emulated channel impulse response showed a close match with an error smaller than $-35$ dB. The results demonstrate that our channel emulator is able to accurately emulate nonstationary fading channels with continuously changing path delays and Doppler shifts.

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).

Reliability is becoming increasingly important for many applications envisioned for future wireless systems. A technology that could improve reliability in these systems is massive MIMO (Multiple-Input Multiple-Output). One reason for this is a phenomenon called channel hardening, which means that as the number of antennas in the system increases, the variations of channel gain decrease in both the time- and frequency domain. Our analysis of channel hardening is based on a joint comparison of theory, measurements and simulations. Data from measurement campaigns including both indoor and outdoor scenarios, as well as cylindrical and planar base station arrays, are analyzed. The simulation analysis includes a comparison with the COST 2100 channel model with its massive MIMO extension. The conclusion is that the COST 2100 model is well suited to represent real scenarios, and provides a reasonable match to actual measurements up to the uncertainty of antenna patterns and user interaction. Also, the channel hardening effect in practical massive MIMO channels is less pronounced than in complex independent and identically distributed (i.i.d.) Gaussian channels, which are often considered in theoretical work., Accepted to IEEE Open Journal of the Communications Society

Massive multiple-input multiple-out (MIMO) is vital in millimeter-wave (mmWave) bands to obtain large array gains. However, there are practical challenges, such as high hardware cost and power consumption in such systems. A promising solution to these problems is to adopt a hybrid beamforming architecture. This architecture has a much lower number of transceiver (TRx) chains than the total antenna number, resulting in cost- and energy- efficient systems. In this demo, we present a real- time mmWave (28GHz) massive MIMO testbed with hybrid beamforming. This testbed has a 64- antenna/16-TRx unit for beam-selection, which can be expanded to larger array sizes in a modular way. For testing everything from baseband processing algorithms to scheduling and beam-selection in real propagation environment, we extend the capability of an existing 100-antenna/100-TRx massive MIMO testbed (below 6GHz), built upon software-defined radio technology, to a flexible mmWave massive MIMO system.

Fifth generation cellular systems will be deployed in the microwave and millimeter–wave (mmWave) frequency bands (i.e., between 0.5–100 GHz). Propagation characteristics at these bands have a fundamental impact on each aspect of the cellular architecture, ranging from equipment design to real-time performance in the field. While we have a reasonable understanding of the propagation characteristics at microwave (< 6 GHz) frequencies, the same cannot be said for mmWave. This article explains key differences in the propagation characteristics between the microwave and mmWave bands, and further gives examples of how these differences impact 5G system design.

In this letter, we analyze cluster characteristics and millimeter wave channel characteristics based on an indoor channel measurement campaign at 28 GHz. The spatial properties of clusters are analyzed, and we investigate the intra-cluster angular spreads and the number of clusters. Also, we introduce the random cluster number (RCN) feature to the primary module of the International Mobile Telecommunications (IMT)-2020 channel model. The RCN is based on a Poisson distribution to simulate the evolution of clusters above 6 GHz to get distributions closer to real channel conditions. Examples of the singular value distributions with and without the RCN feature are given. From the comparisons, we can see the benefits of using the RCN feature in simulations with the IMT-2020 model.

We investigate the ability of Massive multiple-input multiple-output (MIMO) systems to spatially separate up to eighteen users located close to one another in line-of-sight (LOS) propagation conditions, in both indoor and outdoor environments. For that, we use fully-synchronous measured channels at 2.6 GHz of single-antenna users moving within a small area and concurrently communicating with a base station (BS) equipped with a compact 128-port array. To quantify the degree of spatial user separability, we use three scalar metrics, namely, the achievable sum-rates, the condition number of the channel matrix, and the angle to interference factor. Our results show that Massive MIMO with zero-forcing (ZF) or regularized ZF (RZF) can spatially separate nine, even eighteen, concurrent users at practical SNR values even in the challenging case of dominant LOS propagation. In particular, signal-to-noise ratio losses relative to ideal (non-interfering and equally strong) channels can be reduced dramatically compared with standard multiuser MIMO systems, which typically have the same number of users as BS antennas. Our findings suggest that with RZF or ZF the ratio of BS antennas to number of served users should be at least three to four, to harvest most of the available spatial gains that the environment can offer. Although orthogonality and array gains complement each other, for the suggested ratios of antennas to users, the main contribution to improving system performance, measured in sum-rates, comes from the orthogonality gain.

Spatially distributed transmission points connected to the same source, known as distributed antenna systems, can improve system performance compared with single-link traditional systems. However, the anticipated gain depends heavily on the cross-correlation properties of the large-scale parameters (LSPs) of the different links. Usually, measured LSPs - except the large-scale fading-have non-Gaussian distributions. Therefore, in order to study their multi-link cross-correlation properties, scenario- and parameter-specific ad-hoc transformations are applied, such that the LSPs have Gaussian distributions in the transform domain [1], [2]. In this paper, we propose using the Box-Cox transformation as a general framework for homogenizing this conversion step. The Box-Cox transformation is, by nature, not distribution specific; therefore, it can be used regardless of the empirical distributions of the studied LSPs. We demonstrate the applicability of the proposed framework by studying multi-link fully-coherent propagation measurements of four base stations and one mobile station in a suburban microcell environment at 2.6 GHz. The inter- and intra-link cross-correlation of four LSPs - the large-scale fading, the delay, azimuth, and elevation spreads-are analyzed and their distributions are modeled. Based on our analysis, it is found that for the investigated environment: 1) the LSPs of the different links can be modeled using unimodal and bimodal Gaussian distributions; and 2) the inter- and intra-link cross-correlation coefficients of the different studied LSPs can be modeled using the Truncated Gaussian distribution. The proposed models are validated using the Kolmogorov-Smirnov test, and their parameters are provided.

Shadowing from vehicles can significantly degrade the performance of vehicle-to-vehicle (V2V) communication in multilink systems, e.g., vehicular ad hoc networks (VANETs). It is, thus, important to characterize and model the influence of common shadowing objects like cars properly when designing these VANETs. Despite the fact that for multilink systems it is essential to model the joint effects on the different links, the multilink shadowing effects of V2V channels on VANET simulations are not yet well understood. In this paper, we present a measurement-based analysis of multilink shadowing effects in a V2V communication system with cars as blocking objects. In particular, we analyze, characterize, and model the large scale fading, both regarding the autocorrelation and the joint multilink cross-correlation process, for communication at 5.9 GHz between four cars in a highway convoy scenario. The results show that it is essential to separate the instantaneous propagation condition into line-of-sight (LOS) and obstructed LOS (OLOS), by other cars, and then apply an appropriate pathloss model for each of the two cases. The choice of the pathloss model not only influences the autocorrelation but also changes the cross correlation of the large scale fading process between different links. By this, we conclude that it is important that VANET simulators should use geometry-based models, that distinguish between LOS and OLOS communication. Otherwise, the VANET simulators need to consider the cross correlation between different communication links to achieve results close to reality.

It is essential to capture the cross-correlation properties of large-scale parameters (LSPs) among different base station links in cooperative multilink systems in order to make realistic performance assessments. In this work, propagation measurements are used to study the cross-correlation properties of different LSPs, namely large-scale fading, delay spread, azimuth spread, and elevation spread of four links. The interlink cross-correlation coefficients of these LSPs are assessed based on two different measurement approaches: 1) synchronous measurements, where the values of the LSPs of the considered links are estimated from the same measurement run; and 2) repeated measurements, where the values of the LSPs of the considered links are estimated from different measurement runs. Repeated measurements are attractive because they are simpler and less expensive. In this paper, we address the following question: Can repeated measurements be used instead of synchronous measurements in order to estimate the LSPs' cross-correlation properties of different links? Based on analysis of wideband synchronous and repeated multilink measurements in a suburban microcell environment at 2.6 GHz, we found that: 1) the mean values of the cross-correlation coefficients are preserved with repeated measurements, and 2) the estimates of the cross-correlation coefficients from repeated measurements are less spread around the mean value than those from synchronous measurements. These findings are explained based on detailed investigation of specific measured cases and further supported by results obtained from Monte Carlo simulations.

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 current state-of-the-art narrow-band calibration schemes in a mean squared error (MSE) 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., Comment: Submitted to IEEE Transactions on Wireless Communications, 21/Feb/2017

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., In the Proceedings of 2019 IEEE International Conference on Communications (ICC), Dublin, Ireland

In massive MIMO systems the uplink pilot signals transmitted by a terminal define the channel seen by the base station. This gives the terminal some degree of freedom selecting an uplink pilot transmission strategy. In this paper, we investigate the benefit of different pilot transmission strategies when increasing the number of antennas in the terminal. Building on previous work on a simulation framework for Multiple-antenna terminals in 5G massive MIMO systems, this paper presents simulated performance results for various transmission schemes. The results are calibrated to reflect a real communication situation in a large auditorium. Emulating the measurement set-up, we show that the framework can be tuned to generate channel distributions that match measured data. Under generalized conditions, we perform simulations for different terminal transmission-strategies, both related to single stream and multiple streams. All evaluations are based on terminals with four antennas integrated into real Sony Xperia smartphone-chassis, tuned to 3.7 GHz. The measurements are conducted by using the Lund University Massive MIMO testbed with its 100 antennas. The results clearly show the advantage of increasing the antenna-count also at the terminal side in massive MIMO systems.

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. 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, as well as possibilities for practically realizable system solutions across all layers of the Open Systems Interconnection stack. 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 eco-system 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, as well as 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, as well as real-time signal processing. We rigorously discuss the fundamental changes required in the core networks of the future that serves as a major source of latency for time-sensitive applications. While evaluating the strengths and weaknesses of key 6G technologies, we differentiate what may be achievable over the next decade, relative to what is possible., Comment: Accepted for Publication into the Proceedings of the IEEE; 32 pages, 10 figures, 5 tables