Carrier phase positioning (CPP) is widely used in satellite system applications, enabling centimeter-level localization accuracy. Recently, CPP is gaining attraction also in terrestrial mobile networks, particularly in 5G New Radio (NR) evolution toward 6G. One key challenge is to resolve the so-called integer ambiguity problem, as carrier phase provides only relative position information. This work introduces and studies a multi-band CPP scenario with intra- and inter-band carrier aggregation (CA) opportunities across FR1, mmWave-FR2, and emerging 6G FR3 bands. Specifically, we derive multi-band CPP performance bounds, showcasing the superiority of multi-band CPP for high-precision localization in current and future mobile networks. A wide collection of numerical results is provided, covering the impacts of the available carrier bandwidth, number of aggregated carriers, transmit power, and the number of network nodes. The offered results highlight that only two carriers need to be aggregated to substantially facilitate resolving the integer ambiguity problem.

In addition to satellite systems, carrier phase positioning (CPP) is gaining attraction also in terrestrial mobile networks, particularly in 5G New Radio (NR) evolution toward 6G. One key challenge is to resolve the so-called integer ambiguity problem, as the carrier phase provides only relative position information. This work introduces and studies a multi-band CPP scenario with intra- and inter-band carrier aggregation (CA) opportunities across FR1, mmWave-FR2, and emerging 6G FR3 bands. Specifically, we derive multi-band CPP performance bounds, showcasing the superiority of multi-band CPP for high-precision localization in current and future mobile networks, while noting also practical imperfections such as clock offsets between the user equipment (UE) and the network as well as mutual clock imperfections between the network nodes. A wide collection of numerical results is provided, covering the impacts of the available carrier bandwidth, number of aggregated carriers, transmit power, and the number of network nodes or base stations. The offered results highlight that only two carriers suffice to substantially facilitate resolving the integer ambiguity problem while also largely enhancing the robustness of positioning against imperfections imposed by the network-side clocks and multi-path propagation. In addition, we also propose a two-stage practical estimator framework that achieves the derived bounds under all realistic bandwidth and transmit power conditions. Furthermore, we show that with an additional search-based refinement step, the proposed estimator becomes particularly suitable for narrowband Internet of Things (IoT) applications operating efficiently even under narrow carrier bandwidths. Finally, both the derived bounds and the proposed estimators are extended to scenarios where the bands assigned to each base station are nonuniform or fully disjoint, enhancing the practical deployment flexibility.

Abstract The aim of this paper is to define new uncorrelated propagation channels for Multiple-Input-Multiple-Output (MIMO) purposes by producing angular phase differences in the transmission. The phase difference can be created such that the radiation power of the signal does not change when selecting correct values for real and imaginary parts of the complex value $$a+jb$$ . This results in the same signal amplitudes, i.e., coverage area or antenna power radiation pattern, but multi path signal components are received with phase differences. These phase differences then allow uncorrelated propagation channels to be used in MIMO N $$\times $$ N. In 5G/6G mobile networks, mostly only line-of-sight (LOS) connections between base stations and mobiles are available at very high frequencies, and small cell implementations with MIMO are needed to maximize the system capacity. However, MIMO has performance limitations in LOS environments due to lacking the required phase difference between MIMO channels when traditional antenna space separation is used. Thus, these new uncorrelated channels are especially needed in high-capacity small cell implementations but also in nearly LOS-type environments, as on highways. Ray-tracing simulations were done at 28 GHz frequency in a small cell LOS environment to show the impact of angular phase differences. Fast fading statistics and signal cross-correlations were analyzed to see the potential of utilizing MIMO N $$\times $$ N in LOS propagation paths. MIMO system capacities were calculated for different MIMO configurations, and these results were compared with the results of traditional spatially separated configurations. The simulated results showed that sufficient angular phase difference offers independent propagation channels in a small cell LOS environment with cross-correlation factors of 0.01–0.44. These low correlated propagation channels yielded a capacity increase of 3.44–3.88 times from SISO to MIMO 4 $$\times $$ 4 with one polarization and with different directional antennas. This capacity was even 95 % higher than the capacity with only spatially separated directional antennas.

The concept of 6G distributed integrated sensing and communications (DISAC) builds upon the functionality of integrated sensing and communications (ISAC) by integrating distributed architectures, significantly enhancing both sensing and communication coverage and performance. In 6G DISAC systems, tracking target trajectories requires base stations (BSs) to hand over their tracked targets to neighboring BSs. Determining what information to share, where, how, and when is critical to effective handover. This paper addresses the target handover challenge in DISAC systems and introduces a method enabling BSs to share essential target trajectory information at appropriate time steps, facilitating seamless handovers to other BSs. The target tracking problem is tackled using the standard trajectory Poisson multi-Bernoulli mixture (TPMBM) filter, enhanced with the proposed handover algorithm. Simulation results confirm the effectiveness of the implemented tracking solution.

The recent development of mobile communication systems has introduced a myriad of new use cases from XR headsets to industrial automation, where high-precision 3D position and orientation information together with low latency operation, is of paramount importance. In this paper, we propose a novel high-precision 3D position and 3D orientation tracking scheme with per-antenna millimeter-wave delay-Doppler measurements, while considering a quaternion-based representation for the device orientation. Compared to representing the orientation with conventional yaw, pitch and roll angles, quaternion-based approach avoids problematic singular points and angle discontinuities, and provides stable tracking with all possible device orientations. The proposed tracking scheme is founded on extended Kalman filter, for which we derive and express all the needed processing steps for prediction and update stages. The numerical results show that the proposed approach is able to avoid the singular point issue faced with the conventional tracking of yaw, pitch and roll angles, while reaching the accuracy of a benchmark carrier phase based ranging method. Furthermore, by exploiting Doppler measurements’ capability to directly measure a device velocity and an angular velocity of device rotation, millimeter-level positioning accuracy and degree-level orientation estimation accuracy is reached in the considered tracking scenario.

High-Precision localization turns into a crucial added value and asset for next-generation wireless systems. Carrier phase positioning (CPP) enables sub-meter to centimeter-level accuracy and is gaining interest in 5G-Advanced standardization. While CPP typically complements time-of-arrival (ToA) measurements, recent literature has introduced a phase-only positioning approach in a distributed antenna/MIMO system context with minimal bandwidth requirements, using deep learning (DL) when operating under ideal hardware assumptions. In more practical scenarios, however, antenna failures can largely degrade the performance. In this paper, we address the challenging phase-only positioning task, and propose a new DL-based localization approach harnessing the so-called hyperbola intersection principle, clearly outperforming the previous methods. Additionally, we consider and propose a processing and learning mechanism that is robust to antenna element failures. Our results show that the proposed DL model achieves robust and accurate positioning despite antenna impairments, demonstrating the viability of data-driven, impairment-tolerant phase-only positioning mechanisms. Comprehensive set of numerical results demonstrates large improvements in localization accuracy against the prior art methods.

This paper investigates the uplink carrier phase positioning (CPP) in cell-free (CF) or distributed-antenna-system context, assuming a challenging case where only the phase measurements are utilized as observations. In general, CPP can achieve sub-meter to centimeter-level accuracy but it is challenged by the integer ambiguity problem. In this work, we propose two deep learning approaches for phase-only positioning, overcoming the integer ambiguity challenge. The first one directly uses the phase measurements, while the second one first estimates the integer ambiguities and then it integrates them with the phase measurements for improved accuracy. Our numerical results demonstrate that an inference complexity reduction of two to three orders of magnitude is achieved, compared to the maximum likelihood baseline solution, depending on the approach and on the parameter configuration. This emphasizes the potential of the developed deep learning solutions for efficient and precise positioning in future CF 6G systems.

The coexistence of radar and communications in wireless systems marks a paradigm shift for the sixth-generation (6G) networks. As 6G systems are expected to operate at higher frequencies and employ larger antenna arrays than fifth-generation (5G) systems, they can also enable more accurate sensing capabilities. To this end, the integrated sensing and communication (ISAC) paradigm aims to unify the physical and radio frequency (RF) domains by introducing the sensing functionality into the communication network. However, the clutter poses a challenge, as it can significantly degrade the sensing accuracy in ISAC systems. This paper presents a novel two-dimensional root multiple signal classification (2D-rootMUSIC)-based algorithm for static background clutter suppression. Computer simulation results indicate that the proposed method effectively mitigates the strong background clutter, yields accurate parameter estimation performance, and offers a notable improvement in the signal-to-clutter-and-noise ratio (SCNR), while outperforming the prior-art benchmark methods.

High peak-to-average-power ratio (PAPR) is an inevitable challenge in orthogonal frequency-division multiplexing (OFDM) based networks, known to be particularly harmful to efficient utilization of practical power amplifiers (PAs). To preserve the waveform quality, PA back-off can be introduced which, however, directly limits the potential uplink (UL) coverage. This paper proposes a novel PAPR reduction method, by transmitting a peak-cancellation signal (PCS) spatially-precoded to the frequency resources within the operating channel, without introducing any overheads or receiver side interference. The proposed approach can be applied to codebook-based and non-codebook-based transmissions, while also allows for extending the PCS frequency allocation towards neighboring physical resource blocks (PRBs) without interference to other users. Extensive numerical results are provided, conforming with the 3GPP 5G NR transmitter requirements, while also incorporating realistic uplink PA models. The obtained numerical results at FR1 reveals up to 2.6 dB net gain in the effective uplink link budget via using the novel PCS, compared to the plain legacy OFDM signal. Such link budget gains translate to substantial uplink coverage improvements, which can be one major asset in future network deployments towards the 6G era.

Positioning is a vital capability in different radio systems for extracting situational awareness, with path loss (PL)-based positioning playing a crucial role due to its widespread use in wireless standards. In this work, we propose anidle-modePL-based positioning approach without line-of-sight (LOS) detection that is suitable for millimeter-wave (mmWave) urban networks with directive beams in the base stations (BSs). With the beam gain significantly affecting the observed PLs, we divide the data and models relative to the beam direction rather than to LOS and non-line-of-sight (NLOS) BSs. Different approaches for obtaining the PL model parameter estimates are proposed, including model fitting taking into account the influence of the noise limit and direct optimization based on positioning accuracy using the training data set. In addition to the PLs, azimuth- and elevation-of-departure (AoD and EoD) are estimated, modeled, and used in the positioning calculations, building on maximum likelihood (ML) estimation. Comprehensive numerical results and performance assessments are provided, harnessing ray-tracing (RT) data in a 28GHz urban microcellular environment. The demonstrated median positioning error is under 20m reflecting an improvement of 50%-70% compared to the classical CellID method. Additionally, the results show that the proposed method outperforms machine learning based reference solutions. Finally, the methods and the resulting positioning performance are shown to be robust against variations in the underlying technical parameters, such as the BS transmit beam-width, as well as errors or imperfections in the assumed BS locations and BS orientation information.

High-level synthesis (HLS) promises to increase the design and verification productivity for digital hardware systems. However, the industry still predominantly uses more time-consuming manual register-transfer level techniques instead of HLS. To accelerate the adoption of HLS, it is vital to explore if it is possible to achieve competitive results with this method. To that end, this paper demonstrates an HLS implementation of the well-known MUSIC algorithm for estimating the direction of arrival of a radio signal. We use as a receiver a four-antenna uniform linear array with one signal source and a resolution of one degree. For the computationally heavy eigenvalue decomposition within MUSIC, we employ the iterative Jacobi algorithm. We target two different Virtex FPGAs for synthesis and obtain results faring well in comparison to the previous literature, with$5.0\ \mu \mathrm{s}$microseconds latency, high accuracy, and low resource consumption. The results show that HLS is suitable for implementing these kinds of algorithms on FPGA.

Wireless localization and sensing are increasingly important capabilities when the networks are evolving towards the $6^{t h}$ generation era. While the physics-inspired geometrical models are known to perform well in line-of-sight (LoS) dominant scenarios, harnessing the power of artificial intelligence (AI) to improve robustness, efficiency, and performance in more complex propagation scenarios is an intriguing prospect. To this end, the hypernetwork (HN) is an emerging neural network (NN) architecture, where one model is used to parameterize the weights of the other, promising dynamic weight adaptation among other performance improvements. In this work, we propose the concept of Hypernetwork Localization (HypLoc) - a hybrid HN-based architecture for localization in beamforming millimeter-wave (mmWave) networks, while combining angle-of-arrival (AoA), time-of-flight (ToF), and received power (RP) as representative measurements. Considering a realistic urban vehicular environment, we first demonstrate the baseline effectiveness of HypLoc with a fixed and known gNodeB (gNB) deployment scenario. We then also study a scenario where the factory pre-training covers multiple different gNB deployment constellations and show that the proposed HypLoc clearly outperforms the traditional NNs. Finally, we also show that the HypLoc adapts faster and requires less training data when adapting to a previously unseen deployment scenario. Overall, the proposed approach facilitates efficient factory pre-training when operating under multiple different gNB deployment options.

Improving the performance of Artificial Neural Network (ANN) regression models on small or scarce datasets, such as wireless network positioning data, can be realized by simplifying the task. One such approach includes implementing the regression model as a classifier, followed by a probabilistic mapping algorithm that transforms class probabilities into the multi-dimensional regression output. In this work, we propose the so-called c2r, a novel ANN-based architecture that transforms the classification model into a robust regressor, while enabling end-to-end training. The proposed solution can remove the impact of less likely classes from the probabilistic mapping by implementing a novel, trainable differential thresholded Rectified Linear Unit layer. The proposed solution is introduced and evaluated in the indoor positioning application domain, using 23 real-world, openly available positioning datasets. The proposed C2R model is shown to achieve significant improvements over the numerous benchmark methods in terms of positioning accuracy. Specifically, when averaged across the 23 datasets, the proposed c2r improves the mean positioning error by 7.9% compared to weighted knn with k=3, from 5.43 m to 5.00 m, and by 15.4% compared to a dense neural network (DNN), from 5.91 m to 5.00 m, while adapting the learned threshold. Finally, the proposed method adds only a single training parameter to the ann, thus as shown through analytical and empirical means in the article, there is no significant increase in the computational complexity.

In this article, we address the timely topic of cellular bistatic simultaneous localization and mapping (SLAM) with specific focus on end-to-end processing solutions, from raw I/Q samples, via channel parameter estimation to user equipment (UE) and landmark location information in millimeter-wave (mmWave) networks, with minimal prior knowledge. Firstly, we propose a new multipath channel parameter estimation solution that operates directly with beam reference signal received power (BRSRP) measurements, alleviating the need to know the true antenna beampatterns or the underlying beamforming weights. Additionally, the method has built-in robustness against unavoidable antenna sidelobes. Secondly, we propose new snapshot SLAM algorithms that have increased robustness and identifiability compared to prior art, in practical built environments with complex clutter and multi-bounce propagation scenarios, and do not rely on any a priori motion model. The performance of the proposed methods is assessed at the 60GHz mmWave band, via both realistic ray-tracing evaluations as well as true experimental measurements, in an indoor environment. A wide set of offered results demonstrate the improved performance, compared to the relevant prior art, in terms of the channel parameter estimation as well as the end-to-end SLAM performance. Finally, the article provides the measured 60GHz data openly available for the research community, facilitating results reproducibility as well as further algorithm development.

One of the most fundamental problems in simultaneous localization and mapping (SLAM) is the ability to take into account data association (DA) uncertainties. In this paper, this problem is addressed by proposing a multi-hypotheses sampling distribution for particle filtering-based SLAM algorithms. By modeling the measurements and landmarks as random finite sets, an importance density approximation that incorporates DA uncertainties is derived. Then, a tractable Gaussian mixture model approximation of the multi-hypotheses importance density is proposed in which each mixture component represents a different DA. Finally, an iterative method for approximating the mixture components of the sampling distribution is utilized and a partitioned update strategy is developed. Using synthetic and experimental data, it is demonstrated that the proposed importance density improves the accuracy and robustness of landmark-based SLAM in cluttered scenarios over state-of-the-art methods. At the same time, the partitioned update strategy makes it possible to include multiple DA hypotheses in the importance density approximation, leading to a favorable linear complexity scaling, in terms of the number of landmarks in the field-of-view.

This paper investigates the potential to leverage existing 5G NR signals for network-side integrated sensing and communications (ISAC). In general, the synchronization signal block (SSB) is a suitable candidate for always-on downlink sensing, due to its frequent periodical availability and because of its beam-sweeping nature. However, as this work demonstrates, using only the SSB has challenges related to radar ambiguity while being also limited in both distance and velocity resolution due to limited bandwidth and per-beam time duration, respectively. A novel solution is then introduced by combining SSB with downlink control information (DCI) and system information block 1 (SIB1) symbols. The corresponding implications and variants how SIB1 is optimized and configured are discussed, covering both 5G evolution and potential 6G solutions. The performance of the proposed approach is also assessed through realistic numerical evaluations at both 3.5 GHz and 28 GHz network deployments, and shown to yield up to 25 dB suppression in radar peak sidelobe level (PSL) compared to SSB-only based range-velocity profile. Also considerable improvements in the sensing resolution in the order of 120–190% are demonstrated.

In this article, we propose multiple machine learning (ML) based physical-layer receiver solutions for demodulating orthogonal frequency-division multiplexing (OFDM) signals that are subject to high level of nonlinear distortion. Specifically, three novel deep learning based convolutional neural network receivers are devised, containing layers in time- and/or frequency-domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Applicable training procedures are also described, such that the learned layers in the receiver processing properly generalize over different nonlinear distortion and multipath channel characteristics. Extensive set of numerical results is provided, in the context of 5G NR uplink (UL) incorporating also measured terminal power amplifier (PA) characteristics. The obtained results show that the proposed receiver systems are able to clearly outperform the classical linear minimum mean-squared error (LMMSE) receiver as well as the existing ML receiver approaches, especially when the EVM is high compared to modulation order. This is particularly so when the devised ML receiver is of hybrid nature with layers both in time and frequency. The proposed ML receivers can thus facilitate pushing the terminal PA systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage. Through combining the obtained radio link performance results with link budget calculations, all carried out at the 28 GHz mmWave band, it is shown that the proposed ML receivers can enhance the network coverage in terms of maximum UL link distances by close to 100%, when compared to classical LMMSE receiver based networks.

Networks in 5G and beyond utilize millimeter wave (mmWave) radio signals, large bandwidths, and large antenna arrays, which bring opportunities in jointly localizing the user equipment and mapping the propagation environment, termed as simultaneous localization and mapping (SLAM). Existing approaches mainly rely on delays and angles, and ignore the Doppler, although it contains geometric information. In this paper, we study the benefits of exploiting Doppler in SLAM through deriving the posterior Cramér-Rao bounds (PCRBs) and formulating the extended Kalman-Poisson multi-Bernoulli sequential filtering solution with Doppler as one of the involved measurements. Both theoretical PCRB analysis and simulation results demonstrate the efficacy of utilizing Doppler.

5G millimeter wave (mmWave) signals have inherent geometric connections to the propagation channel and the propagation environment. Thus, they can be used to jointly localize the receiver and map the propagation environment, which is termed as simultaneous localization and mapping (SLAM). One of the most important tasks in the 5G SLAM is to deal with the nonlinearity of the measurement model. To solve this problem, existing 5G SLAM approaches rely on sigma-point or extended Kalman filters, linearizing the measurement function with respect to the prior probability density function (PDF). In this paper, we study the linearization of the measurement function with respect to the posterior PDF, and implement the iterated posterior linearization filter into the Poisson multi-Bernoulli SLAM filter. Simulation results demonstrate the accuracy and precision improvements of the resulting SLAM filter.

Reconfigurable intelligent surfaces (RISs) are expected to be a key component enabling the mobile network evolution towards a flexible and intelligent 6G wireless platform. In most of the research works so far, RIS has been treated as a passive base station (BS) with a known state, in terms of its location and orientation, to boost the communication and/or terminal positioning performance. However, such performance gains cannot be guaranteed anymore when the RIS state is not perfectly known. In this paper, by taking the RIS state uncertainty into account, we formulate and study the performance of a joint RIS calibration and user positioning (JrCUP) scheme. From the Fisher information perspective, we formulate the JrCUP problem in a network-centric single-input multiple-output (SIMO) scenario with a single BS, and derive the analytical lower bound for the states of both user and RIS. We also demonstrate the geometric impact of different user locations on the JrCUP performance while also characterizing the performance under different RIS sizes. Finally, the study is extended to a multiuser scenario, shown to further improve the state estimation performance.

Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment and the propagation channel. To solve the SLAM problem, existing approaches rely on sigma-point or particle-based approximations, leading to high computational complexity, precluding real-time execution. We propose a novel low-complexity SLAM filter, based on the Poisson multi-Bernoulli mixture (PMBM) filter. It utilizes the extended Kalman (EK) first-order Taylor series based Gaussian approximation of the filtering distribution, and applies the track-oriented marginal multi-Bernoulli/Poisson (TOMB/P) algorithm to approximate the resulting PMBM as a Poisson multi-Bernoulli (PMB). The filter can account for different landmark types in radio SLAM and multiple data association hypotheses. Hence, it has an adjustable complexity/performance trade-off. Simulation results show that the developed SLAM filter can greatly reduce the computational cost, while it keeps the good performance of mapping and user state estimation.

Since the beginning of the fifth generation (5G) standardization process, positioning has been considered as a key element in future cellular networks. In order to perform accurate positioning, solutions for estimating and processing location-related measurements such as direction of arrival (DoA) and time of arrival (ToA) for various use-cases need to be developed. In this paper, building on the existing 5G new radio (NR) specifications and millimeter wave frequencies, we propose a novel estimation and tracking solution of the DoA and ToA such that only analog/radio frequency (RF) beamforming-based observations are utilized. In addition to the proposed extended Kalman filter (EKF)-based estimation and tracking approach, we derive Cramér-Rao lower bounds (CRLBs) for the considered RF multi-beam system, and propose an information-based criterion for selecting the necessary beams for the estimation process in order to provide highly accurate performance with feasible computational complexity. The performance of the proposed method is evaluated using extensive ray-tracing simulations and numerical evaluations, and the results are compared with other estimation and beam-selection approaches. Based on the obtained results, beam-selection at the receiver can have a significant impact on the DoA and ToA estimation performance as well as on the subsequent positioning accuracy. Finally, we demonstrate the highly accurate performance of the methods when extended to joint multi-receiver-based device positioning and clock synchronization.

Fingerprinting is a widely used technique in indoor positioning, mainly due to its simplicity. Usually, this technique is used with the deterministic k - Nearest Neighbors (k-NN) algorithm. Utilizing a neural network model for fingerprinting positioning purposes can greatly improve the prediction speed compared to the k-NN approach, but requires a voluminous training dataset to achieve comparable performance. In many indoor positioning datasets, the number of samples is only at a level of hundreds, which results in poor performance of the neural network solution. In this work, we develop a novel algorithm based on a transfer learning approach, which combines samples from 15 different Wi-Fi RSS indoor positioning datasets, to train a single convolutional neural network model, which learns the common patterns in the combined data. The proposed model is then fine-tuned to optimally fit the individual databases. We show that the proposed solution reduces the positioning error by up to 25% compared to the benchmark model while reducing the number of outlier predictions.

In this paper, we propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals that are subject to a high level of nonlinear distortion. Specifically, a novel deep learning based convolutional neural network receiver is devised, containing layers in both time- and frequency domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Extensive set of numerical results is provided, in the context of 5G NR uplink incorporating also measured terminal power amplifier characteristics. The obtained results show that the proposed receiver system is able to clearly outperform classical linear receivers as well as existing ML receiver approaches, especially when the EVM is high in comparison with modulation order. The proposed ML receiver can thus facilitate pushing the terminal power amplifier (PA) systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage.

Integrated communications, localization and sensing is one of the most addressed technologies considered for future mobile communications systems. In this context, a user equipment (UE)-centric mobile radar has been proposed to introduce improved situational awareness, and consequently potential improvement in network performance. In this paper, we derive an extended Kalman probability hypothesis density (EK-PHD) filter with a novel feature model, for a mobile radar based environment mapping, where range-angle detections are used to track map objects over time for dynamic map construction. In order to evaluate the performance of the proposed filtering approach, we employ a realistic ray-tracing-based simulation setup, which models the full transmission chain from the transmitted IQ-samples to mapping results. Besides this, a simplified measurement model considering solely single-bounce specular reflections is exploited for providing further insight into the filter performance. The obtained results show that the proposed EK-PHD filter is able to provide high-quality mapping results, reaching around 10 cm landmark estimation accuracy in the considered millimeter wave simulation setup.

The millimeter wave (mmWave) device-to-device air interface not only supports a direct wireless connectivity among devices, but it also offers an improved beamforming capability to obtain the direction information among the vehicles and devices for positioning. Both features serve as the key physical layer components for communications and positioning in the industrial Internet of things (IIoT) systems. Exploiting both accurate beamforming and wide bandwidth in a mmWave network, high-accuracy positioning is achievable, which can be then facilitated for location-aware communications, for instance. However, the uncertainty of anchors' locations in the industrial environment highly degrades the achievable positioning accuracy if left without proper consideration. In order to resolve such challenge, this paper presents a cooperative positioning system (CPS), where the locations of all the vehicles and anchors can be jointly estimated based on acquired location-related measurements (LRMs). Furthermore, the positioning performance is evaluated under random trajectories and different geometric relationships between the vehicles and the anchors. We show that, the proposed positioning solution is capable of resolving the aforementioned challenge by simultaneously tracking the mobile vehicles while mapping the locations of the static anchors. Utilizing the LRMs from both time and angular domains, the achieved positioning accuracy in both 2D and vertical plane is demonstrated based on extensive numerical simulations. Last but not least, the impact of different numbers of the mobile vehicles on the overall positioning performance is also investigated.

The 5th generation mobile networks introduce large bandwidths with extended beamforming capabilities, which results in increased spatial selectivity of received channel state information. A channel chart is a map of the radio geometry that surrounds the base station and it can be generated in an unsupervised manner from the received channel state information without any knowledge of actual measurement locations. In this work, we generate channel charts for multiple base stations using multidimensional scaling and combine them for a better shape of the radio geometry. The combined chart cannot be directly applied for absolute positioning, but it can be extended. Extension is performed with affine and conformal mappings to the charts. The method for generating and combining the charts as well the method of extension to absolute positioning is explained. Evaluations are performed for two different scenarios, one of which is an open space scenario, and the other utilizes ray-tracing data. Finally, the charts are presented and analyzed together with positioning estimation results for the considered scenarios.

With the emergence of new 5G radio networks, high-accuracy positioning solutions are becoming extensively more important for numerous 5G-enabled applications and radio resource management tasks. In this paper, we focus on 5G mm-wave systems, and propose a method for high-accuracy estimation of the User Equipment (UE) position and antenna orientation. Based on the sparsity of the mm-wave channel, we utilize a compressive sensing approach for estimating the departure and arrival angles as well as the time-of-arrival for each observed radio propagation path. After this, in order to obtain statistical descriptions of the unknown parameters, we analytically derive a set of sampling distributions, which enable utilization of an iterative Gibbs sampling method. As shown by the obtained simulation results, the proposed method is able to achieve centimeter-level positioning accuracy with degree-level orientation accuracy, even in the absence of a line-of-sight path.

High accuracy positioning enabled by 5G cellular networks will play a crucial role in the robot-based industrial applications, where the vertical accuracy will be as significant as the 3D accuracy. Aiming at target applications relying on flying robots in industrial environments, this paper presents and formulates two positioning algorithms when the location uncertainty of the access nodes (ANs) is taken into consideration. The first algorithm is a low-complexity geometry-based 3D positioning algorithm that utilizes both time-of-arrival and angle-of-arrival measurements. The second algorithm relies on extended Kalman Filter (EKF)-based positioning, by mapping the ANs' location uncertainty into the measurement noise statistics. The performance of the two proposed method is studied in terms of 3D and vertical positioning accuracy, sensitivity to location uncertainty of the ANs, and computational complexity in indoor scenarios. Based on the conducted complexity analysis, the proposed geometry-based algorithm is computationally more efficient than the EKF-based algorithm. In addition, the proposed geometry-based positioning method demonstrates a higher robustness against a high location uncertainty of ANs than the considered EKF-based method.

Low-power and low-latency communication features are vital to extend 5G mobile devices functionalities beyond those of the current networks, and to introduce innovative services and applications. On the other hand, limitations of state-of-the-art cellular modules prevent designing and facilitating such features based on current power saving mechanisms alone. In this paper, a new wake-up signaling for 5G control plane is introduced, aiming to reduce energy consumption of cellular module in downlink. Performance of the proposed scheme in terms of false alarm and misdetection rates are investigated and evaluated. The obtained numerical results show that such a signaling can reduce power consumption of discontinuous reception (DRX) by up to 30%, at the cost of negligible increase in signaling overhead.

The expected fifth generation (5G) networks allow for highly accurate direction of arrival and time of arrival (ToA) estimation, thus providing a convenient environment for device positioning, if designed properly. However, utilizing ToA measurements for positioning requires a tight synchronization not only between the target devices but also among the network elements. In this paper, we propose a joint positioning and synchronization solution building on the premises of the envisioned cmWave-based 5G ultra-dense networks and time-varying clock models. In addition to device location estimates, also relative clock offsets and skews are estimated and tracked within the proposed extended Kalman filter based solutions, which can be further used by a network operator in synchronizing the active network elements and devices within the network. Based on extensive simulations and numerical evaluations, accurate positioning performance can be achieved while tracking the clock parameters under time-varying clock errors.

We study positioning of high-speed trains in 5G new radio (NR) networks by utilizing specific NR synchronization signals. The studies are based on simulations with 3GPP-specified radio channel models including path loss, shadowing and fast fading effects. The considered positioning approach exploits measurement of Time-Of-Arrival (TOA) and Angle-Of-Departure (AOD), which are estimated from beamformed NR synchronization signals. Based on the given measurements and the assumed train movement model, the train position is tracked by using an Extended Kalman Filter (EKF), which is able to handle the non-linear relationship between the TOA and AOD measurements, and the estimated train position parameters. It is shown that in the considered scenario the TOA measurements are able to achieve better accuracy compared to the AOD measurements. However, as shown by the results, the best tracking performance is achieved, when both of the measurements are considered. In this case, a very high, sub-meter, tracking accuracy can be achieved for most (>75%) of the tracking time, thus achieving the positioning accuracy requirements envisioned for the 5G NR. The pursued high-accuracy and high-availability positioning technology is considered to be in a key role in several envisioned HST use cases, such as mission-critical autonomous train systems.

Large databases with Received Signal Strength (RSS) measurements are essential for various use cases in mobile wireless communications and navigation, including radio resource management algorithms and network-based localization. Because of the constantly increasing number of radio transmitters with various wireless technologies and with the advent of 5G cloud computing and Internet of Things (IoT), the required size of the RSS databases are becoming unmanageably large. Thus, the requirements for the bandwidth and data rates for accessing the memory might become too costly. Therefore, in order to reduce the size of the RSS database, while maintaining the data quality, we have previously proposed the method of spectrally compressed RSS images, which are able to achieve considerable data compression of up to 70 percent. In this paper, we deeply analyze the process of spectral compression and introduce error sources, which affect the compression performance. Based on the analysis, we propose a novel theoretical framework and methods to optimize the performance of the spectral compression. In addition, we derive the Cramer-Rao Lower Bound (CRLB) for the RSS-based localization error and compare the CRLB between separate baseline localization approaches. The theoretical analysis is justified and compared with experimental RSS measurements taken from several multi-storey buildings.

The upcoming fifth generation (5G) radio networks will be the game changer of future societies. In addition to obvious improvements in wireless communications, 5G enables also highly accurate user equipment (UE) positioning that is carried out on the network side. Such a solution provides ubiquitous positioning services without draining the batteries of the UEs. In this paper, we concentrate on positioning methods that suits the future needs of automotive transportation and intelligent transportation system (ITS). In particular, we demonstrate how the location estimates can be obtained in 5G ultra-dense networks (UDNs) efficiently and even in a proactive manner where the UE locations can be predicted to some extent. Numerical performance analysis will then illustrate that the proposed 5G-based network-centric positioning solutions are well-suited for car and traffic applications, providing even sub-meter range positioning accuracy.

In this paper, we address the prospects and key enabling technologies for highly efficient and accurate device positioning and tracking in fifth generation (5G) radio access networks. Building on the premises of ultra-dense networks as well as on the adoption of multicarrier waveforms and antenna arrays in the access nodes (ANs), we first formulate extended Kalman filter (EKF)-based solutions for computationally efficient joint estimation and tracking of the time of arrival (ToA) and direction of arrival (DoA) of the user nodes (UNs) using uplink reference signals. Then, a second EKF stage is proposed in order to fuse the individual DoA and ToA estimates from one or several ANs into a UN position estimate. Since all the processing takes place at the network side, the computing complexity and energy consumption at the UN side are kept to a minimum. The cascaded EKFs proposed in this article also take into account the unavoidable relative clock offsets between UNs and ANs, such that reliable clock synchronization of the access-link is obtained as a valuable by-product. The proposed cascaded EKF scheme is then revised and extended to more general and challenging scenarios where not only the UNs have clock offsets against the network time, but also the ANs themselves are not mutually synchronized in time. Finally, comprehensive performance evaluations of the proposed solutions on a realistic 5G network setup, building on the METIS project based outdoor Madrid map model together with complete ray tracing based propagation modeling, are provided. The obtained results clearly demonstrate that by using the developed methods, sub-meter scale positioning and tracking accuracy of moving devices is indeed technically feasible in future 5G radio access networks operating at sub-6 GHz frequencies, despite the realistic assumptions related to clock offsets and potentially even under unsynchronized network elements.

Internet of Things (IoT) and Machine-to-Machine (M2M) applications are typically characterized by moderate investment costs to the M2M devices and infrastructure, in addition to the high reliability and energy efficiency requirements. The new Sub-1 GHz WiFi standard, namely the IEEE802.11ah, is being introduced to address these requirements deploying its recently specified MAC and PHY features and mechanisms. In this paper, we present an extensive analysis office 802.11ah network performance by means of realistic system level simulations. In particular, we focus on realistic performance evaluation and enhancement study of the IEEE 802.11ah network when multi-access points (multi-APs) with relatively high number of associated stations (STAs) are considered. The performance evaluation of the multi-AP IEEE 802.11ah network considers one of the main proposed MAC enhancement schemes for collision reduction, namely, the Restricted Access Window (RAW) mechanism. The analysis results confirm the importance of this novel mechanism to improve substantially the overall system performance from both network throughput and energy efficiency perspectives. Overall, the technical findings reported in this article strengthen the prospects of IEEE 802.11ah as one of the key enabling technologies for wide-scale low-cost and energy-efficient M2M deployments and IoT applications in the future.

In this paper we introduce a new flexible time division duplexing based radio interface for future 5G small cell communications. We describe the benefits of the new design to achieve high energy efficiency, high spectral efficiency and low latency at the same time. We also compare our reference design against LTE-A assuming rank 8 DL SU-MIMO transmission and show that we our design can achieve more than 29% lower total overhead and up to 90% lower round trip time.

A Bayesian method for dynamical off-line estimation of the position and path loss model parameters of a WLAN access point is presented. Two versions of three different on-line positioning methods are tested using real data. The tests show that the methods that use the estimated path loss parameter distributions with finite precisions outperform the methods that only use point estimates for the path loss parameters. They also outperform the coverage area based positioning method and are comparable in accuracy with the fingerprinting method. Taking the uncertainties into account is computationally demanding, but the Gauss-Newton optimization method is shown to provide a good approximation with computational load that is reasonable for many real-time solutions.