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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 3.7GHz. In the measured scenario, the proposed pipeline can achieve centimeter-level accuracy by combining delay and angular information., Comment: 6 pages, 8 figures

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., Comment: This paper has been accepted by IEEE Vehicular Technology Conference

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., Comment: 6 pages, 8 figures, Accepted to be published in IEEE International Conference on Communications 2023, Rome, Italy

Positioning with 5G signals generally requires connection to several base stations (BSs), which makes positioning more demanding in terms of infrastructure than communications. To address this issue, there have been several theoretical studies on single BS positioning, leveraging high-resolution angle and delay estimation and multipath exploitation possibilities at mmWave frequencies. This paper presents the first realistic experimental validation of such studies, involving a commercial 5G mmWave BS and a user equipment (UE) development kit mounted on a test vehicle. We present the relevant signal models, signal processing methods (including channel parameter estimation and position estimation), and validate these based on real data collected in an outdoor science park environment. Our results indicate that positioning is possible, but the performance with a single BS is limited by the knowledge of the position and orientation of the infrastructure and the multipath visibility and diversity.

This paper deals with propagation and channel modeling for physically large arrays. The focus lies on acquiring a spatially consistent model, which is essential, especially for positioning and sensing applications. Ultra-wideband, synthetic array measurement data have been acquired with large positioning devices to support this research. We present a modified multipath channel model that accounts for a varying visibility of multipath components along a large array. Based on a geometric model of the measurement environment, we analyze the visibility of specular components. We show that, depending on the size of the reflecting surface, geometric visibility and amplitude estimates obtained with a super-resolution channel estimation algorithm show a strong correspondence. Furthermore, we highlight the capabilities of the developed synthetic array measurement system., Comment: 6 pages, 6 figures, submitted to EuCNC-2023

6G systems, such as Large Intelligent Surfaces, will require distributed, complex, and coordinated decisions throughout a very heterogeneous and cell free infrastructure. This will require a fundamentally redesigned software infrastructure accompanied by massively distributed and heterogeneous computing resources, vastly different from current wireless networks.To address these challenges, in this paper, we propose and motivate the concept of a 6G Computing Continuum (6GCC) and two research testbeds, to advance the rate and quality of research. 6G Computing Continuum is an end-to-end computeand software platform for realizing large intelligent surfaces and its tenant users and applications. One for addressing the challenges or orchestrating shared computational resources in the wireless domain, implemented on a Large Intelligent Surfaces testbed. Another simulation-based testbed is intended to address scalability and global-scale orchestration challenges.

Accurate understanding of electromagnetic propagation properties in real environments is necessary for efficient design and deployment of cellular systems. In this paper, we show a method to estimate high-resolution channel parameters with a massive antenna array in real network deployments. An antenna array mounted on a vehicle is used to receive downlink long-term evolution (LTE) reference signals from neighboring base stations (BS) with mutual interference. Delay and angular information of multipath components is estimated with a novel inter-cell interference cancellation algorithm and an extension of the RIMAX algorithm. The estimated high-resolution channel parameters are consistent with the movement pattern of the vehicle and the geometry of the environment and allow for refined channel modeling and precise cellular positioning.

This article aims at providing insights for a comprehensive understanding of terahertz (THz) propagation channels. Specifically, we discuss essential THz channel characteristics to be well understood for the success of THz communications. The methodology of establishing realistic and 6G-compliant THz channel models based on measurements is then elaborated on, followed by a discussion on existing THz channel measurements in the literature. Finally, future research directions, challenges and measures to enrich the understanding of THz channels are discussed., Comment: The final version can be found in IEEE Communications Magazine

Modern wireless communication systems operating at high carrier frequencies are characterized by a high dimensionality of the underlying parameter space (including channel gains, angles, delays, and possibly Doppler shifts). Estimating these parameters is valuable for communication purposes, but also for localization and sensing, making channel estimation a critical component in any joint communication and localization or sensing application. The high dimensionality make it difficult to use search-based methods such as maximum likelihood. Search-free methods such as ESPRIT provide an attractive alternative, but require a complex decomposition step in both the tensor and matrix version of ESPRIT. To mitigate this, we propose, develop, and analyze a reduced complexity beamspace ESPRIT method. Complexity is reduced both by beampace processing as well as low-complex implementation of the singular value decomposition. A novel perturbation analysis provides important insights for both channel estimation and localization performance. The proposed method is compared to the tensor ESPRIT method, in terms of channel estimation, communication, localization, and sensing performance, further validating the perturbation analysis., Comment: 17 pages, 7 figures

This paper presents LuMaMi28, a real-time 28 GHz massive 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) massive MIMO performance in different real-life scenarios with static and mobile UEs. We explore the potential benefit of the mmWave massive MIMO systems with antenna selection based on measured channel data, and discuss the performance results through real-time measurements., Comment: 14 pages, 17 figures

The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We first overview key concepts about mmWave signal propagation and system design. Then, we provide a detailed account of approaches and algorithms for localization and sensing enabled by mmWaves. We consider several dimensions in our analysis, including the main objectives, techniques, and performance of each work, whether each research reached some degree of implementation, and which hardware platforms were used for this purpose. We conclude by discussing that better algorithms for consumer-grade devices, data fusion methods for dense deployments, as well as an educated application of machine learning methods are promising, relevant and timely research directions., Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys & Tutorials (IEEE COMST)

The premise of massive multiple-input multiple-output (MIMO) is based around coherent transmission and detection. Majority of the vast literature on massive MIMO presents performance evaluations over simplified statistical propagation models. All such models are drop-based and do not ensure continuity of channel parameters. In this paper, we quantify the impact of spatially consistent (SC) models on beamforming for massive MIMO systems. We focus on the downlink of a 28GHz multiuser urban microcellular scenario. Using the recently standardized Third Generation Partnership Project 38.901 SC-I procedure, we evaluate the signal-to-interference-plus-noise ratio of a user equipment and the system ergodic sum spectral efficiency with zero-forcing, block diagonalization, and signal-to-leakage-plus-noise ratio beamforming. Our results disclose that at practical signal-to-noise ratio levels, SC channels yield a significant performance loss relative to the case without SC due to substantial spatial correlation across the channel parameters., Comment: Invited Paper in the Proceedings of EuCAP 2020

We present a new type of wireless access infras-tructure consisting of a fabric of dispersed electronic circuitsand antennas that collectively function as a massive, distributed antenna array. We have chosen to name this new wireless infrastructure 'RadioWeaves' and anticipate they can be integrated into indoor and outdoor walls, furniture, and otherobjects, rendering them a natural part of the environment. Technologically, RadioWeaves will deploy distributed arrays to create both favorable propagation and antenna array interaction. The technology leverages on the ideas of large-scale intelligent surfaces and cell-free wireless access. Offering close to the service connectivity and computing, new grades in energy efficiency,reliability, and low latency can be reached. The new concept moreover can be scaled up easily to offer a very high capacity inspecific areas demanding so. In this paper we anticipate how two different demanding use cases can be served well by a dedicated RadioWeaves deployment: a crowd scenario and a highly reflective factory environment. A practical approach towards a RadioWeaves prototype, integrating dispersed electronics invisibly in a room environment, is introduced. We outline diverse R\&D challenges that need to be addressed to realize the great potential of the RadioWeaves technology., Presented at Asilomar 2019, publication in conference proceedings accepted

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., Comment: 12 pages, 13 figures, Preprint; submitted to IEEE Transactions on Vehicular Technology

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 Ricean 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 bi-modal Gaussian mixture model, that allows to capture the $K$-factor variability in time for safety-relevant ITS scenarios., Comment: 26 pages, 12 figures, submitted to IEEE Transactions on Intelligent Transportation Systems for possible publication

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 2006

This thesis treats of channel models for polarized multi-antenna wireless systems. Polarized multi-antenna systems are systems that use perpendicularly polarized, co-located antennas at the base station and at the mobile terminal, in order to benefit from the so-called polarization diversity. Such systems benefit from the advantages of MIMO systems while still maintaining a compact equipment size. Two models will be presented in this thesis. The first one is the Polarized-Input Polarized-Output (PIPO) channel model, the second one is the Polarized-Diffuse-Directional channel model. The PIPO model is a statistical channel model for tri-polarized to tri-polarized communication systems. A tri-polarized antenna system is a tranceiver using three perpendicular antennas. The aim of the PIPO channel model is to have a model that has a simple mathematical structure, so it can be used for solving precoding equations or capacity calculations. Although the PIPO model has a very simple structure, it takes the following parameters into account: coherent channel component, cross-polar channel power imbalance, inter-channel correlation, short- and long-scale time variance. Experimental measurements are used to parameterize the model. It is shown how the model parameters are extracted from experimental measurements, and the results are analyzed to allow further simplification of the model. The PDD model, on the other hand, is a geometry-based stochastic channel model. It models the channel as a sum of clusters, where each cluster consists of groups of multipath components (MPCs). The PDD model includes two novelties that will be developed in detail in this thesis. - The model considers polarization on a per-cluster basis. This permits to have a more accurate description of the polar-angular spectrum. - The diffuse multipath component (DMC) is included by considering a diffuse component for each cluster. The diffuse cluster component is then modeled as the sum of a set of diffuse MPCs. The model is specified in detail, and it is shown how the model can be generated. Experimental measurements were carried out to parameterize the model. A new extraction technique for extracting the specular-diffuse clusters from the measurements is proposed. This technique is based on joint clustering of the specular MPCs and the bins of the diffuse component. The experimental results are analyzed, and superimposed with environment information to gain further insight into the physical aspects of clustered propagation. Finally, both models are validated. Several validation metrics are introduced, and their pertinence in the context of polarized MIMO systems is highlighted. Both models are successfully validated, and the advantages and limitations of each models are investigated. Cette thèse traite des modèles de canal pour systèmes sans-fils multi-antennes polarisés. Des systèmes multi-antennes polarisés sont des systèmes qui utilisent des antennes polarisées perpendiculairement co-localisées à la station de base et au terminal mobile, dans le but de bénéficier de la diversité de polarisation. De tels systèmes peuvent bénéficier des avantages des systèmes MIMO tout en diminuant l'encombrement des équipements. Deux modèles seront présentés dans cette thèse. Le premier est le modèle Polarized-Input Polarized-Output (PIPO), le second est le modèle Polarized-Diffuse-Directional (PDD). Le modèle PIPO est un modèle statistique pour des systèmes de communication tri-polaire à tri-polaire. Un système tri-polaire est un émetteur ou un récepteur qui utilise trois antennes perpendiculaires. Le but du modèle de canal PIPO est d'avoir un modèle qui a une structure mathématique simple, afin qu'il puisse être utilisé pour résoudre des équations de précodage ou des calculs de capacité. Malgré la structure simple du modèle PIPO, il tient compte des paramètres suivants: la composante cohérente du canal, les différences de puissance entre canaux cross-polaires, la corrélation entre canaux, les variations à courte et à longue échelle de temps. Des mesures expérimentales ont été réalisées afin de paramétriser le modèle. Les techniques pour extraire les paramètres du modèle des mesures expérimentales sont présentées, et les résultats sont analysés afin de permettre une simplification supplémentaire du modèle. Le modèle PDD, quant à lui, est un modèle de canal stochasique-géométrique. Il modélise le canal comme une somme de clusters, où chaque clusters est composé d'un groupe de chemins multi-trajets. Le modèle PDD inclut les deux nouveautés suivantes qui seront développées en détail dans cette thèse. - Le modèle considère une polarisation par cluster. Ceci permet d'avoir une description plus exacte du spectre angulaire-polaire. - La composante diffuse est prise en compte en incluant une composante diffuse pour chaque cluster. La composante diffuse d'un cluster est alors modelisée comme une somme de multi-trajets diffus. Le modèle est spécifié en détail, et il est présenté comment le modèle peut être généré. Des mesures expérimentales ont été faites afin de paramétriser le modèle. Une nouvelle technique d'extraction est proposée pour extraire les clusters spéculaires-diffus. Cette technique est basée sur le clustering conjoint des multi-trajets spéculaires et des "bins" de la composante diffuse. Les résultats expérimentaux sont analysés, et superposés avec l'information de l'environnement de mesure afin d'avoir une connaissance accrue des aspects physiques de la propagation par clusters. Finalement, les deux modèles sont validés. Plusieurs métriques de validations sont introduites, et leur pertinence dans le cadre des systèmes MIMO polarisés est mis en avant. Les deux modèles sont validés avec succès, et les avantages et limitations de chaque modèle sont investigués., Doctorat en Sciences de l'ingénieur, info:eu-repo/semantics/published