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.

Low-cost and high-accessibility solutions for wireless positioning in Africa require access to affordable infrastructure, low-power receivers that can operate with solar-cell batteries, and low-cost chipsets to process data from satellites. With the fast-paced progress in the design of CubeSats and SmallSats, launching and maintaining satellites in Low Earth Orbit (LEO) is becoming in-creasingly affordable. While LEO signals have historically been used for communications and Earth-sensing purposes, recent research has also focused on LEO for Positioning, Navigation, and Timing (PNT) purposes, known as LEO-PNT. Our paper focuses on LEO-PNT design and addresses several parameter-design issues in LEO- PNT, such as orbital altitudes and carrier-frequency choices, with positioning metrics in mind. A comprehensive in-house Matlab-based simulator is used with user-track data collected from Togo, and a performance analysis is conducted under two outdoor Quadriga-based channel models and a free-space path loss model. We find that LEO constellations up to 1300 km orbits and with signals operating below 7 G Hz carrier frequency can be viable solutions for LEO- PNT in Africa, yet further studies are needed to reduce the size of future LEO constellations.

Emerging Extended Reality (XR) applications bring new opportunities for digital healthcare systems, i.e., eHealth. XR-assisted surgery is one of the most outstanding examples of future technology that has a high social impact on the healthcare and medical educational system. The current work presents the intelligent design for remote XR-assisted surgery. The study presents the Field-of-View (FoV)-based viewport model empowered with behavioral data. It applies the viewport prediction model based on the behavioral data by applying Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). In the final analysis, LSTM showed lower errors and a higher coefficient of determination, but ANN performed much faster. Finally, the study defines the dynamic system’s states for adaptive and fast video delivery concerning Quality of Experience (QoE). The presented approach aims to mitigate the delay to ensure smooth playback and display high-quality images. • Explores advanced techniques for mitigating video traffic. • Presents a video delivery model for an advanced dynamic tiled-based video transmission. • Outlines an open-source datasets applicable to XR-related research. • Presents the results on behavioral prediction by applying ANN and LSTM. • Emphasizes the importance of behavioral study and the state of system dynamics in XR.

This research investigates an affordable, energy-efficient direction-of-arrival (DOA)-based localization solution for digital enhanced cordless telecommunications (DECTs) 2020 new radio (NR), a new standard lacking a native positioning feature. This standard enables massive Internet of Things (IoT) networks, a vast 5G network interconnecting an unparalleled number of low-cost and battery-operated smart sensors. However, integrating DOA localization into such networks is challenging due to cost constraints and power limitations. We propose a potentially cost-effective solution using a single radio-frequency (RF) chain for uniform L-shaped antenna arrays. Each antenna takes turns sampling the orthogonal frequency division multiplexing (OFDM) signal via an RF switch, enabled by time-dividing the OFDM signal into sample and switch slots. Further, we introduce a novel DOA method optimized for single Line-of-Sight (LOS) OFDM signals and array sequential sampling. This method leverages the dual shift-invariant properties of L-shaped antenna arrays and the array frequency response to estimate the azimuth and elevation angles. Experiments in an indoor environment reveal that at a signal-to-noise ratio (SNR) of 15 dB, over 50% of data achieve subdegree angular accuracy, increasing to 75% at 20 dB. Thus, over 50% of position estimations fall below the submeter error level at 15 dB SNR, rising to nearly 75% at 25 dB SNR. Our findings also indicate that halving the slot rate by proportionately reducing active subcarriers does not compromise accuracy. Experiments on the nRF52480 system-on-chip show the new DOA method is both fast and energy-efficient, taking only 0.76–2.26 ms and consuming 5.08–15.1 nWh.

This paper proposes to explore the Radio Frequency Fingerprint (RFF) identification with a virtual database generator. RFF is a unique signature created in the emitter transmission chain by hardware flaws. These flaws may be used as a secure identifier as they cannot be easily replicated for spoofing purposes. In recent years, the RFF identification relies mainly on Deep Learning (DL), and large databases are consequently needed to improve identification in different environmental conditions. In this paper, we introduce a virtual database and suggest utilizing it for the examination of three crucial aspects when creating a RFF database: the number of signals required to perform DL classification, the impact of RFF similarities between emitters, and the propagation channel impact in static and dynamic contexts. For instance, such analysis shows that data augmentation with 10 channels improves accuracy classification up to 70% in a scenario where RFFs are close from a transmitter to another.

In modern wireless networks evolving towards 6thgeneration, localization, and sensing in indoor environments play an increasingly critical role in ensuring reliability, security, and control over network users, including vehicular assets. Despite recent advancements in deep learning, using k-Nearest Neighbors (k-NN) as a positioning algorithm in Received Signal Strength Indicator (RSSI) fingerprinting-based localization still provides numerous advantages, including localization accuracy, reliability, and interpretability. In this work, we introduce Intersection over Union (IoU) as a novel similarity metric and introduce κ-enhanced k-NN, which enables dynamic neighbor selection leading to improved performance and generalization capabilities of the positioning algorithm. In the evaluation using 26 publicly available indoor positioning datasets, we clearly show the improvements in localization accuracy of the combined IoU with κ-enhanced k-NN over the relevant baselines.

Ensuring accurate, reliable, and effortless localization capabilities becomes one of the key requirements of the upcoming 6G and beyond wireless networks, while user equipment expands beyond traditional smartphones to countless Internet of Things (IoT) devices, vehicles, or drones. Location awareness becomes a necessity for smooth operation, security, and safety, while fingerprinting-based methods are able to ensure reliability and accuracy. k-Nearest Neighbors (k-NN) remains to this day one of the most popular localization algorithms, while its main drawbacks include increased complexity when operating on voluminous data, and requires exhaustive hyperparameter sweeping to find optimal performance. In this work, we propose a localization system denoted σ-MESS, which reduces the volume of the dataset, accelerates the positioning speed, and improves the positioning performance, while at the same time alleviates the requirement for finding optimal parameters for k-NN. The method is evaluated on 13 openly available indoor positioning datasets, reducing the achieved positioning error by 15% and positioning time by 87.5% on average, when compared to the k-NN with the same hyperparameters. We further compare the achieved results with the ones achieved in recently published papers outperforming numerous solutions.

Emerging communication technologies, such as 5G and beyond, have introduced diverse requirements that demand high performance and energy efficiency at all levels. Furthermore, the real-time requirements of different services vary significantly — increasing the baseband processor design complexity and demand for flexible hardware platforms. This paper identifies the key characteristics of hardware platforms for baseband processing and describes the existing processing limitations in traditional architectures. In this paper, Coarse-Grained Reconfigurable Architecture (CGRA) is examined as a prospective hardware platform and its characteristic features are highlighted as compared to traditionally employed architectures that make it a suitable candidate for incorporation as a domain-specific accelerator in baseband processing applications. We survey various CGRAs from the last two decades (2004-2023) and analyze their distinct architectural features which can serve as a reference while designing CGRAs for baseband processing applications. Moreover, we investigate the existing challenges toward developing CGRAs for baseband processing and explore their potential solutions. We also provide an overview of the emerging research directions for CGRA and how they can contribute toward the development of advanced baseband processors. Lastly, we highlight a conceptual RISC-V+CGRA framework that can serve as a potential direction toward integrating CGRA in future baseband processing systems.

Indoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the device’s location. Reducing the size of the RSS database enables faster processing, and saves storage space and radio resources necessary for the database transfer, thus cutting implementation and operation costs, and increasing the quality of service. In this work, we propose EWOk, an Element-Wise cOmpression using k-means, which reduces the size of the individual radio measurements within the fingerprinting radio map while sustaining or boosting the dataset’s positioning capabilities. We show that the 7-bit representation of measurements is sufficient in positioning scenarios, and reducing the data size further using EWOk results in higher compression and faster data transfer and processing. To eliminate the inherent uncertainty of k-means we propose a data-dependent, non-random initiation scheme to ensure stability and limit variance. We further combine EWOk with principal component analysis to show its applicability in combination with other methods, and to demonstrate the efficiency of the resulting multidimensional compression. We evaluate EWOk on 25 RSS fingerprinting datasets and show that it positively impacts compression efficiency, and positioning performance.

Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with similar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by$\approx 7$% with respect to fingerprinting with the traditional clustering models.

In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor en-vironments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Never-theless, such BLE- and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices' information (i.e., estimated positions and BLE- RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the stand-alone lateration method in terms of positioning accuracy.

Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%.

Broad adoption of resource-constrained devices for medical use has additional limitations in terms of execution of delay-sensitive medical applications. As one of the solutions, new ways of computational offloading could be developed and integrated. The recently emerged Mobile Edge Computing (MEC) and Mobile Cloud Computing (MCC) paradigms attempt to address this problem by offloading tasks to a the resource-rich server. In the context of the availability of eHealth services for all patients, independently of the location, the implementation of MEC and MCC could help ensure a high availability of medical services. Remote medical examination, robotic surgery, and cardiac telemetry require efficient computing solutions. This work discusses three alternative computing models: local computing, MEC, and MCC. We have designed a Matlab-based tool to calculate and compare the response time and energy efficiency. We show that local computing demands 48 times more power than MEC/MCC with increasing packet workload. On the other hand, the throughput of MEC/MCC highly depends on the parameters of the communication channel. Finding an optimal trade-off between the response time and energy consumption is an important research question that could not be solved without investigating the system's bottlenecks.

Ultrawideband (UWB) communications have gained popularity in recent years for being able to provide distance measurements and localization with high accuracy, which can enhance the capabilities of devices in the Internet of Things (IoT). Since energy efficiency is of utmost concern in such applications, in this work, we evaluate the power and energy consumption, distance measurements, and localization performance of two types of UWB physical interfaces (PHYs), which use either a low- or high-rate pulse repetition (LRP and HRP, respectively). The evaluation is done through measurements acquired in identical conditions, which is crucial in order to have a fair comparison between the devices. We performed measurements in typical line-of-sight (LOS) and nonline-of-sight (NLOS) scenarios. Our results suggest that the LRP interface allows a lower power and energy consumption than the HRP one. Both types of devices achieved ranging and localization errors within the same order of magnitude and their performance depended on the type of NLOS obstruction. We propose theoretical models for the distance errors obtained with LRP devices in these situations, which can be used to simulate realistic building deployments and we illustrate such an example. This article, therefore, provides a comprehensive overview of the energy demands, ranging characteristics, and localization performance of state-of-the-art UWB devices.

Wi-Fi fingerprinting is a well-known technique used for indoor positioning. It relies on a pattern recognition method that compares the captured operational fingerprint with a set of previously collected reference samples (radio map) using a similarity function. The matching algorithms suffer from a scalability problem in large deployments with a huge density of fingerprints, where the number of reference samples in the radio map is prohibitively large. This paper presents a comprehensive comparative study of existing methods to reduce the complexity and size of the radio map used at the operational stage. Our empirical results show that most of the methods reduce the computational burden at the expense of a degraded accuracy. Among the studied methods, only$k$-means, affinity propagation, and the rules based on the strongest access point properly balance the positioning accuracy and computational time. In addition to the comparative results, this paper also introduces a new evaluation framework with multiple datasets, aiming at getting more general results and contributing to a better reproducibility of new proposed solutions in the future.

Rapid technology advancement, economic growth, and industrialization have paved the way for developing a new niche of small body-worn personal devices, gathered together under a wearable-technology title. The triggers stimulated by end-users interest have introduced the first generation of mass-consumer wearables in just the past decade. Evidently, the trailblazing ones were not designed with strict energy-consumption restrictions in mind. Thus, wearable-computing-related research remained fragmented. Advanced and sophisticated batteries and communication technologies could be already procurable on devices. Additional solutions for efficient utilization of processing power are still a white spot on the wearable technology roadmap. A-WEAR EU project aims to enhance the understanding of how the superimposition of those technologies would improve wearable devices' energy efficiency, with the research area being far from saturation. We foresee enormous room for research as the Edge computing paradigm is emerging towards hand-held devices.

Non-line-of-sight (NLOS) propagation is one of the main error sources in indoor localization, so a large body of work has been dedicated to identifying and mitigating NLOS errors. The most accurate NLOS detection methods often rely on large training data sets that are time-consuming to obtain and depend on the environment and hardware. We propose a method for detecting NLOS distance measurements without manually collected training data and knowledge of channel statistics. Instead, the algorithm generates LOS/NLOS labels for sets of distance measurements between fixed sensors and the mobile target based on distance residuals. The residual-based detection has 70–80% accuracy but has high complexity and cannot be used with high confidence on all measurements. Therefore, we use the predicted labels and the channel impulse responses of the measurements to train a classifier that achieves over 90% accuracy and can be used on all measurements, with low complexity. After we train the classifier during an initial phase that captures specifics of the devices and of the environment, we can skip the residual-based detection and use only the trained model to classify all measurements. We also propose an NLOS mitigation method that reduces, on average, the mean and standard deviation of the localization error by 2.2 and 5.8 times, respectively.

The evaluation of Indoor Positioning Systems (IPSs) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and features of controlled experiments cannot be generalized since the use of the same sensors or anchors density cannot be guaranteed. The dawn of datasets is pushing IPS evaluation to a similar level as machine-learning models, where new proposals are evaluated over many heterogeneous datasets. This paper proposes a way to evaluate IPSs in multiple scenarios, that is validated with three use cases. The results prove that the proposed aggregation of the evaluation metric values is a useful tool for high-level comparison of IPSs.

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.

Integrating sensing and communications is becoming a rising trend in the architecture design of the foreseeable mobile communications system, which could be driven by multifold applications and scarce spectrum resources. Regarding the demand for the economic surveillance solution in the secondary airports, the inborn imaging function in the 5G networks could be a promising candidate. This paper investigates the feasibility and capability of using 5G uplink and downlink reference signals for imaging purposes. An ambiguity function-based signal processing method is proposed in this paper to elaborate the imaging functionality in the 5G networks. The 5G signal-based imaging idea is validated with a realistic ray-tracing channel model generated from a simulated 3D airport model. Our method empowers the imaging functionality of the wireless communications system solely without the aid of external signal resources. Different from the conventional synthetic-aperture radar processing, our methods are adjusted for unevenly allocated reference signal symbols, which causes mirror images problem. The mirror images are quantified in the simulation result, and the mitigation strategies such as lower flight speed and narrower beam are proposed to resolve the problem.

Technology is continually undergoing a constituent development caused by the appearance of billions new interconnected “things” and their entrenchment in our daily lives. One of the underlying versatile technologies, namely wearables, is able to capture rich contextual information produced by such devices and use it to deliver a legitimately personalized experience. The main aim of this paper is to shed light on the history of wearable devices and provide a state-of-the-art review on the wearable market. Moreover, the paper provides an extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology. Finally, the survey highlights the critical challenges and existing/future solutions.

In the last decade, we observed a constantly growing number of Location-Based Services (LBSs) used in indoor environments, such as for targeted advertising in shopping malls or finding nearby friends. Although privacy-preserving LBSs were addressed in the literature, there was a lack of attention to the problem of enhancing privacy of indoor localization, i.e., the process of obtaining the users' locations indoors and, thus, a prerequisite for any indoor LBS. In this work we present PILOT, the first practically efficient solution for Privacy-Preserving Indoor Localization (PPIL) that was obtained by a synergy of the research areas indoor localization and applied cryptography. We design, implement, and evaluate protocols for Wi-Fi fingerprint-based PPIL that rely on 4 different distance metrics. To save energy and network bandwidth for the mobile end devices in PPIL, we securely outsource the computations to two non-colluding semi-honest parties. Our solution mixes different secure two-party computation protocols and we design size-and depth-optimized circuits for PPIL. We construct efficient circuit building blocks that are of independent interest: Single Instruction Multiple Data (SIMD) capable oblivious access to an array with low circuit depth and selection of the k-Nearest Neighbors with small circuit size. Additionally, we reduce Received Signal Strength (RSS) values from 8 bits to 4 bits without any significant accuracy reduction. Our most efficient PPIL protocol is 553x faster than that of Li et al. (INFOCOM'14) and 500× faster than that of Ziegeldorf et al. (WiSec'14). Our implementation on commodity hardware has practical run-times of less than 1 second even for the most accurate distance metrics that we consider, and it can process more than half a million PPIL queries per day.

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.

Fingerprinting is widely used for indoor positioning, where pattern matching techniques are usually applied to signals from APs or Beacons. However, the real-time monitoring of the emitters is not an easy task in most cases. When an alteration in the emitters is not detected or properly fixed, it might have a severe impact in the accuracy of the indoor positioning algorithm. Simple but common alterations are energy failure, emitter replacement, wrong emitter placement after maintenance and AP displacement. This paper explores how the AP alteration might be automatically detected by computing the average of the RSS distance to the best match over multiple operational points. The experimental setup consider one simulated and two real scenarios to validate the proposed metric for detecting AP alternation. The results show that it is possible to detect AP alteration when it has a considerable impact in the IPS accuracy.

This paper studies the performance of Received Signal Strength (RSS)-based fingerprinting positioning methods under different attack scenarios. We discuss different attack models and we compare the accuracy of a commonly used RSS fingerprinting algorithm with a robust version relying on access node visibility, with respect to those attacks. The results show that the robust fingerprinting method outperforms the traditional fingerprinting method for a particular group of attacks, for two attack types the accuracy improvement, in terms of Root Mean Square Error (RMSE), can yield factor two. RSS-based fingerprinting methods are most susceptible to jamming of access nodes and least vulnerable to random removal of access nodes.

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.

Localization of a user in a wireless network is challenging in the presence of malfunctioning or malicious reference nodes, since if they are not accounted for, large localization errors can ensue. We evaluate three Bayesian methods to statistically identify outliers during localization: an exact method, an expectation maximization (EM) method proposed earlier, and a new method based on Variational Bayesian EM (VBEM). Simulation results indicate similar performance for the latter two schemes, with the VBEM algorithm able to provide a statistical description of the user location, rather than an estimate as in the simpler EM case. In contrast to previous studies, we find that there is a significant gap between the approximate methods and the exact method, the cause of which is discussed.

Our paper analyses the distributions of various parameters of GPS user traces collected in four measurement campaigns during 2008-2016 across Europe and US and including 1036 volunteer users and more than 43 million trajectory points. The aim is to shed new lights on eight selected parameters of user mobility models and to compare the distributions of these parameters between different measurement datasets and existing theoretical models. We find that the user-level based statistics for the angle distributions are rather independent on the environment, while the user-level based statistics for the other parameters such as speed, acceleration, flight and pause times and steps are environment dependent. We also find out that exponential and lognormal distributions are the most encountered distributions that fit best the user traces parameters. We also show that users are usually at maximum 15 Km away from the spatial means of their trajectories.

Performance enhancement in indoor positioning is one of the main concerns in recent days. Seeking such improvements, developments in navigation systems are employing Signals of Opportunity (SoO), meaning signals not originally developed for positioning purposes, such as wireless communication signals and Ultra Wideband (UWB) signals. Cyclostationary methods can provide necessary tools for signal detection for these systems. The detection part is only the first step towards cognitive positioning, and this is the part addressed in this paper. However, this work is not limited to cognitive positioning area, but it can find its usability in cognitive spectrum sensing as well. The aim of this paper is to provide a better understanding of the cyclostationary spectrum sensing properties of the most encountered modulations techniques for the SoO signals, namely CDMA, OFDM and TH-PPM UWB.

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.

It has been commonly recognized that the use of Global Navigation Satellite System (GNSS) signals for indoor positioning is extremely challenging due to the significantly attenuated signal power and the presence of strong multipath components. However, with the advent of new GNSS signals the position accuracy is expected to be improved in outdoor environments and their applicability indoors shall be re-examined. In indoor environments, it is likely that the pure GNSS solution will not be sufficient; assisted-GNSS or any solutions where combined communication and navigation receivers are employed are very promising candidates to solve this problem. One issue in this case is the bandwidth limitation, introduced via the pulse shaping at the transmitter side and/or the bandwidth limiting filters at the receiver side. This is the problem addressed here. More precisely, in this paper we investigate the impact of different pulse shape filters on the tracking accuracy of the future Global Positioning System (GPS) and Galileo signals. The simulation results indicate that Chebyshev and Butterowrth filters are good candidates compared to the infinite bandwidth rectangular pulses and in terms of error variance degradation.

The everlasting public interest on location and positioning services has originated a demand for a high performance Global Navigation Satellite System (GNSS), such as the Global Positioning System or the future European satellite navigation system, Galileo. The performance of GNSS is subject to several errors, such as ionosphere delay, troposphere delay, receiver noise and multipath. Among all these errors, multipath is the main limiting factor in precision-oriented GNSS applications. In order to mitigate the multipath influence on navigation receivers, the multipath problem has been approached from several directions, including the development of novel signal processing techniques. Many of these techniques rely on modifying the tracking loop discriminator in order to make it resistant to multipath. These techniques have proved very efficient against multipath having a medium or large delay with respect to the Line-Of-Sight (LOS) signal. In general, the multipath errors are largely reduced for multipath delays greater than around 0.1 chips (which is about 29.3 meters for Galileo E1 Open Service (OS) signal). Theoretically, this constitutes a remarkable improvement as compared to simpler techniques such as narrow Early-Minus-Late (nEML) tracking loop. However, in practice, most of the multipath signals enter the receiver with short-delay with respect to LOS signal, making most of these mitigation techniques partially ineffective. In this paper, we propose a new multipath estimation technique, namely the Slope-Based Multipath Estimation (SBME), which is capable of mitigating the short-delay multipath (i.e., multipath delays less than 0.35 chips) quite well compared with other state-of-the-art mitigation techniques, such as the nEML and the High Resolution Correlators (HRC). The proposed SBME first derives a multipath estimation equation by utilizing the correlation shape of the ideal normalized correlation function of a Binary Phase Shift Keying (BPSK)- or Multiplexed Binary Offset Carrier (MBOC)-modulated signal, which is then used to compensate for the multipath bias of a nEML tracking loop. It is worth to mention here that the SBME requires an additional correlator at the late side of the correlation function, and it is used in-conjunction with a nEML tracking loop. The multipath performance of the above-mentioned mitigation techniques is presented for Galileo E1 OS and GPS L1 C/A signals from theoretical as well as simulation perspective.

Line-of-sight (LOS) delay estimation with high accuracy is a pre-requisite for reliable location via satellite systems. The future European satellite positioning system, Galileo, uses spread-spectrum signals modulated via binary-offset-carrier (BOC) modulation. The receiver for a BOC-modulated spread spectrum signal has to cope not only with multipath effects, but also with possible lost of lock due to additional peaks in the envelope of the correlation function. Traditionally, code tracking is implemented at the receiver side via feedback delay locked loops. Feedforward methods have also been presented as alternatives for increased delay estimation accuracy, especially for short-delay multipaths. The increase in the delay estimation accuracy is typically counter-balanced by a faster mean time to lose lock (MTLL). In this paper we introduce a new algorithm, namely the peak tracking (PT) algorithm, which combines the feedback technique with the feedforward technique, in such a way that it increases the delay estimation accuracy while preserving a good MTLL.

Abstract An important aspect in designing the modulation scheme for various satellite systems, such as the modernized GPS and Galileo, is to obtain good spectral properties and suitable spectral shaping. For example, in the future satellite navigation systems, some of the main goals are: low interference with the existing GPS signals, good root‐mean‐square (RMS) bandwidth, good time resolution (in order to allow the separation between channel paths and to decrease the synchronization errors) etc. Starting from the recently proposed cosine‐ and sine‐BOC modulation families for GPS and Galileo systems, we introduce a new, generalized family, denoted here by double‐BOC (DBOC) modulation. We derive and analyze the properties of the power spectral densities (PSD) and autocorrelation functions (ACF) of the DBOC modulation with various orders, we show its relationship with BPSK, sine‐ and cosine‐BOC modulations, and we illustrate via several examples how to choose optimally the parameters of this new modulation family, according to different optimization criteria. The examples are targeting at applications such as the design of suitable modulations for Galileo open service (OS) and public regulated service (PRS) signals, but the authors believe that the DBOC concept might be useful to other satellite‐based applications, when the available bandwidth is large enough. Copyright © 2006 John Wiley & Sons, Ltd.

The Binary Offset Carrier (BOC) modulation which has been proposed for future Galileo and GPS M-code signals, provides a higher spectral separation from BPSK-modulated signals, such as GPS C/A code. The absolute value of the auto-correlation function of a BOC signal has a narrower main lobe, which may increase the resolution of delay estimates, but also presents deep fades, which may lead to a higher number of timing hypotheses to acquire the signal. In order to get rid of these ambiguities, several approaches have been proposed in literature, which provide an unambiguous BPSK-like shape of correlation function. In this paper we analyze, compare and develop further two BPSK-like methods which allow to acquire a BOC-signal unambiguously. The focus is on hybrid search, where several time-frequency bins are searched in parallel. We introduce here a modified version of a BPSK-like method which decreases the receiver complexity and is valid for both odd and even BOC orders. We analyze both single-side band (SSB) processing (i.e., only one band is used) and dual-side band (DSB) processing (i.e., upper and lower bands are combined non-coherently). While eliminating the ambiguities in auto-correlation function, both SSB and DSB processing present some performance degradation, induced by the band selection and non-coherent processing. The analysis is done in the presence of multipath fading channels. As a benchmark, we keep also the ambiguous BOC processing. We consider parameters specified in the proposals for Galileo system Open Service (OS), respectively Publicly Regulated Service (PRS).

The computational load and the speed of the code acquisition process of any CDMA system depend on the number of timing hypotheses required to search a given uncertainty window (i.e., the code epoch interval in GPS and Galileo systems). By increasing the spacing between the timing hypotheses (or the time-bin step), this computational load may be reduced and the acquisition speed may be increased. However, the time-bin step is limited by the main lobe width of the correlation function envelope (i.e., the correlation which is performed between the incoming signal and a reference code stored at the receiver). If the width of this main lobe is increased, the time-bin step may be increased as well. Here, a filter-bank based method is proposed, which allows the use of higher time-bin steps than those usually used in GPS/Galileo and, thus, it has the potential of increasing the acquisition speed. Simulation results are shown in comparison with other acquisition methods

In this paper we study the effect of the transition band in designing both FIR- and IIR-types of digital filters, as possible bandwidth-limiting receiver filters, during the CDMA code acquisition of a BOC-modulated and over-sampled Galileo signal. It is shown that using an asymmetric transition band (i.e., the band between the passband and stopband frequencies) with respect to one fourth of the sampling rate, the performance, in terms of root mean square errors (RMSE), can be improved for both FIR and IIR filters compared to the situation with symmetric transition bands. As an optimum bound we have employed the ideal rectangular pulse (i.e. no bandwidth limitation). All investigated filtering methods still suffer from some performance degradation, compared to rectangular shaping. The analysis is done here for both static and fading multipath channels

Wireless positioning has received increased attention during the past few years, where several wireless applications have been envisaged and promoted such as the E-911/E-112 regulations. The positioning needs to be carried out in all the environments covered by the wireless communication services, including the most constraining areas such as dense urban areas and obstructed indoor environments. The most known positioning system is the global navigation satellite system (GNSS), which demonstrated quite reliable positioning capabilities when the receiver is in direct view with the sky. However, in indoor environments the signal characteristics are not well understood yet and positioning capabilities are quite poor. Therefore, understanding the multipath propagation indoors and fading characteristics are quite important to make GNSS works indoors. In this paper, we describe measurement-based modeling results of the level-crossing rate (LCR) and average duration of fades (ADF) with the purpose of giving further insight on the real satellite-to-indoor channel characteristics.

Delay estimation in direct-sequence code-division multiple-access (DS-CDMA) systems is necessary for accurate code synchronization and for applications such as mobile phone positioning. Multipath propagation is among the main sources of error in the DS-CDMA delay estimation process, together with multiple access interference and non-line-of-sight (NLOS) propagation. This paper provides a review of main delay estimation techniques, existing in the literature so far, which are able to cope with multipath propagation, together with our novel delay estimation techniques proposed in the context of DS-CDMA systems. The performance of all these techniques is compared through analysis and simulations, considering also their relative computational complexity and required prior information. Starting from the traditional delay locked loops (DLL) and their improved variants, we discuss several recently introduced delay estimation techniques able to cope with multipath propagation. The characterization of these methods is given in a unified framework, suited for both rectangular and root raised cosine pulse shapes. The main focus in the performance comparison of the algorithms is on the closely-spaced multipath scenario, since this situation is the most challenging for achieving diversity gain with low delay spreads and for estimating LOS component with high accuracy in positioning applications.

In most positioning techniques, two or more non-serving base stations (BS) are involved in the location procedure. The position is usually derived from the estimates of the time of arrival of the line-of-sight (LOS) components from these different BSs. In the downlink transmission, the received signal strength from a remote BS is quite low, especially when the mobile is close to the serving BS, and also it is composed of a sum of multiple propagation paths, which may arrive at sub-chip delay intervals, generating closely spaced multipaths. For accurate location estimates, some measures should be taken to solve the closely spaced paths and diminish the intercell interference. In this paper, we introduce a constrained deconvolution approach aided with an intercell interference estimation scheme to solve the closely spaced paths and to enhance the estimation of the delay of the first arriving path from the desired BS.

The acquisition of CDMA signals in adverse mobile communication channels has been studied for more than five decades. However, the introduction of new standard proposals for the future European satellite system (i.e., Galileo) and for the modernized GPS has triggered new interest in fast and reliable acquisition strategies for CDMA systems with very high spreading factors (e.g., length of 10230 chips or higher). Typically, the double-dwell serial search strategies have been preferred for CDMA signal acquisition. Few papers have addressed also the problem of hybrid and parallel search strategies, but typically, the choice of the best number of dwells has not been discussed. The goal of this paper is to introduce a generic method for the computation of the mean acquisition time (MAT) for multiple-dwell hybrid-search acquisition blocks and to compare the performance of several multiple-dwell structures for CDMA systems with high spreading factors. It will be shown here that increasing the number of dwells does not always increase the performance from the point of view of the MAT. We also discuss the influence of various parameters on the selection of the multiple-dwell strategy and we present simulation results for a realistic Galileo signal.

The problem of closely-spaced paths in DS-CDMA systems is a challenging task at the baseband receiver for applications such as mobile location and RAKE receivers. Previously, we introduced a method based on Teager-Kaiser (1990) operator for resolving paths spaced at less than one chip distance. We compare the performance of TK operator with that of the subspace based MUSIC (multiple signal classification) algorithm. In order to make a fair comparison, an extension of MUSIC algorithm to the systems with long codes is derived and the performance of both algorithms is assessed via simulations.

In this paper, we introduce an efficient and simple technique for estimating closely-spaced multipath delays in an asynchronous multiuser CDMA systems. The subchip resolution is achieved via a nonlinear quadratic operator called Teager-Kaiser operator, which exploits the structure of the cross-correlation function between the received signal and the reference code. Simulation results in the presence of multiple interfering users and Rayleigh fading multipath channels are presented. It is shown that the proposed technique is near-far resistant, and its performance in the presence of closely spaced multipaths is much better compared to the peak tracking with subtraction method. Moreover, it has the advantage of a very simple implementation, compared to other maximum likelihood approaches.

Wideband CDMA systems employ pilot symbols for coherent detection. When the pilot symbols are time-multiplexed with data symbols, their number is usually limited, due to overhead considerations and rate matching requirements. Therefore, channel estimation based only on the pilot symbols is usually not accurate enough. It was shown previously that, in the absence of code synchronization errors, a pilot-aided decision-directed (PADD) approach exhibits better BER performance than pilot-aided estimation over fading channels, because it uses both pilot and data symbols for channel estimation. This paper shows the impact of the delay estimation errors on the PADD algorithm. The multipath delays are estimated using pilot-aided (PA), non-data aided (NDA), or decision-directed (DD) algorithms, and the comparison with ideal channel estimation case is done via simulation. The PADD algorithm in the presence of multipath delay errors is also compared with the pilot-aided plus linear interpolation channel estimation. The simulation results show that the PADD algorithm is not very sensitive to the delay estimation errors when the delays are estimated via an NDA or DD approach, and it exhibits better performance than the traditional pilot-aided methods also when the delay estimation errors are present.

The RAKE receiver is the most common receiver employed in wideband CDMA (W-CDMA) systems. Traditionally, multiple access interference (MAI) in spread spectrum systems is modelled as white Gaussian noise. This presumption is valid when the number of users is large and interference signals have large spreading factors. But the W-CDMA air interface for 3rd generation mobile communication systems supports low spreading factors, high and variable data rates, which make MAI non-Gaussian. We analyze the bit error rate (BER) performance of a downlink receiver in the presence of an interfering user when the Gaussian assumption does not hold any more. The receiver is a maximum ratio combining RAKE receiver. First we study the performance of the ideal RAKE receiver, which has exact estimates of delays, amplitudes and phases of the multipath components. Then we consider the effect of MAI when non-data aided (NDA) and pilot aided decision directed (PADD) algorithms are used for delay and channel coefficients estimation, respectively. It is shown that imperfect power control can significantly affect the BER performance, while practical single-user channel estimation algorithms, such as the NDA-PADD algorithm, behave well also in the presence of non-Gaussian MAI.

We investigate the performance of two delay estimation algorithms, non-data aided (NDA) and decision-directed (DD), in the presence of multipath fading and additive Gaussian noise. The comparison is made assuming perfect knowledge about the amplitudes and phases of channel multipaths, in order to see the maximum achievable performance with these two algorithms. The results indicate that NDA delay estimation offers but little degradation of performance compared to DD algorithm and has the advantage of a reduced complexity and higher spectral efficiency. We also study the impact of data errors on DD delay estimation. The algorithms are applied for a downlink wideband CDMA (WCDMA) system, using Walsh spreading codes and long Gold scrambling codes.

We evaluate three decision-directed algorithms for complex multipath channel tap estimation in the downlink wideband CDMA (WCDMA) system. These algorithms are tested with indoor and vehicular channel models at different mobile velocities and their performance is compared with ideal channel estimation, as well as with estimation based on linear interpolation.