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.

As the need for more accurate and reliable positioning systems grows, satellite-based navigation techniques are gaining significant attention, particularly those utilizing Doppler shifts from Low Earth Orbit (LEO) satellites. Traditional Doppler positioning systems often suffer from errors induced by atmospheric disturbances, satellite clock biases, and other signal impairments, especially in dynamic environments. This has motivated the exploration of differential Doppler positioning as a promising solution to mitigate these common-mode errors. This paper explores the theoretical limits of differential Doppler positioning, focusing on Doppler-only methods where position and velocity estimates are derived from Doppler measurements without relying on time-of-arrival (TOA) measurements. By leveraging the Cramér-Rao lower bound (CRLB), we provide a theoretical performance benchmark for the accuracy of position, velocity, and frequency bias estimation. Furthermore, we present a correlation model for atmospheric effects to demonstrate the impact of baseline distance on the estimation performance of differential Doppler positioning. The results show that differential Doppler positioning notably outperforms traditional non-differential Doppler positioning, particularly in low-SNR environments, with substantial gains in frequency bias, 3D velocity, and 3D position estimation accuracy.

As we move towards 6G, the demand for high-precision, cost-effective positioning solutions becomes increasingly critical. Single-anchor positioning offers a promising alternative to traditional multi-anchor approaches, particularly in complex propagation environments where infrastructure costs and deployment constraints present significant challenges. This paper provides a comprehensive evaluation of key algorithmic choices in the development of a single-anchor 5G uplink positioning testbed. Our developed testbed uses angle of arrival (AoA) estimation combined with range measurements from an ultra-wideband pair, to derive the position. The simulations conducted assess the impact of the selected algorithms on channel order and AoA estimation, while the influence of antenna calibration errors on AoA estimation is also examined. Finally, we compare simulations and results obtained from our developed platform.

The carrier phase of cellular signals can be utilized for highly accurate positioning, with the potential for orders-of-magnitude performance improvements compared to standard time-difference-of-arrival positioning. Due to the integer ambiguities, standard performance evaluation tools such as the Cramér-Rao bound (CRB) are overly optimistic. In this paper, a new performance bound, called the mixed-integer CRB (MICRB) is introduced that explicitly accounts for this integer ambiguity. While computationally more complex than the standard CRB, the MICRB can accurately predict positioning performance, as verified by numerical simulations, and hence it serves as a useful guide to choose the system parameters that facilitate carrier phase positioning.

Reconfigurable intelligent surfaces (RISs) are considered as an enabling technology for the upcoming sixth generation of wireless systems, exhibiting significant potential for radio localization and sensing. An RIS is usually treated as an anchor point with known position and orientation when deployed to offer user localization. However, it can also be attached to a user to enable its localization in a semi-passive manner. In this paper, we consider a static user equipped with an RIS and study the RIS localization problem (i.e., joint three-dimensional position and orientation estimation), when operating in a system comprising a single-antenna transmitter and multiple synchronized single-antenna receivers with known locations. We present a multi-stage estimator using time-of-arrival and spatial frequency measurements, and derive the Cramér-Rao lower bounds for the estimated parameters to validate the estimator's performance. Our simulation results demonstrate the efficiency of the proposed RIS state estimation approach under various system operation parameters.

In the recent years there have been intense research efforts to protect users of Global Navigation Satellite Systems (GNSS) from spoofing attacks that aim to mislead the user’s navigation solution by means of counterfeit signals. Galileo, the European GNSS, has been at the forefront of such efforts and implemented already its so-called Open Service Navigation Message Authentication (OSNMA) protocol, intended to authenticate the navigation message and thus provide a protection layer not existing before in any other GNSS open signal. Rather than message authentication, this paper focuses on a novel technique for detecting the presence of potential spoofers by taking advantage of the unpredictability of some of the OSNMA data. Such opportunistic method is based on processing short sequences of received unpredictable symbols, sending them to a remote trusted server where access to the authentic unpredictable symbols is available, and then comparing both the received and the expected sequences for detecting potential mismatches. This approach is well-suited for receivers operating in snapshot mode, which can only gather and process a short piece of received signal due to their limited wireless connectivity, as in many Internet-of-Things (IoT) devices with low-power constraints. Interestingly, the problem addressed herein has some similarities with frame synchronization in digital networks. At the same time, though, it poses new challenges such as the presence of propagation errors, time uncertainty and different propagation times among different satellites, thus deserving a dedicated study.

Reconfigurable intelligent surfaces (RISs) are one of the most promising technological enablers of the next (6th) generation of wireless systems. In this paper, we introduce a novel use-case of the RIS technology in radio localization, which is enabling the user to estimate its own position via transmitting orthogonal frequency-division multiplexing (OFDM) pilots and processing the signal reflected from the RIS. We demonstrate that user localization in this scenario is possible by deriving Cramér-Rao lower bounds on the positioning error and devising a low-complexity position estimation algorithm. We consider random and directional RIS phase profiles and apply a specific temporal coding to them, such that the reflected signal from the RIS can be separated from the uncontrolled multipath. Finally, we assess the performance of our position estimator for an example system, and show that the proposed algorithm can attain the derived bound at high signal-to-noise ratio values.

The development of an integrated satellite-terrestrial communication network has become one of the focuses in both academic and industry in order to provide genuine seamless coverage. For the integrated satellite and terrestrial 5G commu-nication systems, positioning information of user terminals (UTs) can be beneficial in addressing several challenges. In this paper, we propose to utilize 5G new radio synchronization signals to perform positioning. To simultaneously guarantee synchronization and positioning performances for UTs in any place of a cell coverage, we investigate the precoding design at the satellite side for joint synchronization and positioning (JSP) in 5G integrated satellite-terrestrial networks. By considering the missed detection probabilities and angle of departure estimation for the UTs, we provide the precoding design criteria for synchronization and positioning, respectively. Then we introduce the constraint of equal transmit power on every antenna. Based on the criteria and constraint, we formulate the optimization problem for JSP and exploit the conjugate gradient algorithm under the manifold op-timization framework to design the precoder. Simulation results show that the proposed precoder can ensure that JSP achieves satisfactory performances within the whole cell coverage.

MmWave multiple-input single-output (MISO) systems using a single-antenna receiver are regarded as a promising solution for the near future, before the full-fledged 5G MIMO will be widespread. However, for MISO systems synchronization cannot be performed jointly with user localization unless two-way transmissions are used. In this paper we show that thanks to the use of a reconfigurable intelligent surface (RIS), joint localization and synchronization is possible with only downlink MISO transmissions. The direct maximum likelihood (ML) estimator for the position and clock offset is derived. To obtain a good initialization for the ML optimization, a decoupled, relaxed estimator of position and delays is also devised, which does not require knowledge of the clock offset. Results show that the proposed approach attains the Cramér-Rao lower bound even for moderate values of the system parameters.

Exploiting wavefront curvature enables localization with limited infrastructure and hardware complexity. With the introduction of reconfigurable intelligent surfaces (RISs), new opportunities arise, in particular when the RIS is functioning as a lens receiver. We investigate the localization of a transmitter using a RIS-based lens in close proximity to a single receive antenna element attached to reception radio frequency chain. We perform a Fisher information analysis, evaluate the impact of different lens configurations, and propose a two-stage localization algorithm. Our results indicate that positional beamforming can lead to better performance when a priori location information is available, while random beamforming is preferred when a priori information is lacking. Our simulation results for a moderate size lens operating at 28 GHz showcased that decimeter-level accuracy can be attained within 3 meters to the lens.

We consider the problem of joint three-dimensional localization and synchronization for a single-input single-output (SISO) multi-carrier system in the presence of a reconfigurable intelligent surface (RIS), equipped with a uniform planar array. First, we derive the Cramér-Rao bounds (CRBs) on the estimation error of the channel parameters, namely, the angle-of-departure (AOD), composed of azimuth and elevation, from RIS to the user equipment (UE) and times-of-arrival (TOAs) for the path from the base station (BS) to UE and BS-RISUE reflection. In order to avoid high-dimensional search over the parameter space, we devise a low-complexity estimation algorithm that performs two 1D searches over the TOAs and one 2D search over the AODs. Simulation results demonstrate that the considered RIS-aided wireless system can provide submeter-level positioning and synchronization accuracy, materializing the positioning capability of Beyond 5G networks even with single-antenna BS and UE. Furthermore, the proposed estimator is shown to attain the CRB at a wide interval of distances between UE and RIS. Finally, we also investigate the scaling of the position error bound with the number of RIS elements.

We consider the problem of estimating the 3D orientation of a user, using the downlink mmWave signals received from multiple base stations. We show that the received signals from several base stations, having known positions, can be used to estimate the unknown orientation of the user. We formulate the estimation problem as a maximum likelihood estimation in the manifold of rotation matrices. In order to provide an initial estimate to solve our non-linear non-convex optimization problem, we resort to a least squares estimation that exploits the underlying geometry. Our numerical results show that the problem of orientation estimation can be solved when the signals from at least two base stations are received. We also provide the orientation lower error bound, showing a narrow gap between the performance of the proposed estimators and the bound.

Positioning methods relying on cellular signals are subject to severe degradation errors due to the inherent harsh working conditions of urban scenarios. In contrast, emerging applications are gradually requesting a more accurate and reliable positioning solution, thus requiring the implementation of alternative measures to minimize such degradation. This paper describes a method for detecting faulty measurements from base station (BS) affected by non-line-of-sight (NLOS) propagation. The method monitors the residuals resulting from the least-squares positioning solution, inspired by the approach implemented by Receiver Autonomous Integrity Monitoring (RAIM) techniques in Global Navigation Satellite Systems (GNSS) receivers. The method has been tested through simulations based on a deep urban deployment map, which comes with an experimental data file of user's position. Positioning Reference Signal (PRS) of 5G New Radio (NR) operating in the centimeter-wave (cmWave) band is used. Results confirm the utility of implementing NLOS monitoring method to achieve a better performance assessment under realistic assumptions while using 5G positioning signals.

Power-Based Capon beamforming has recently been proposed to avoid the well-known cancellation effects between direct signal and correlated multipaths of the Capon beamformer. This novel technique exploits the fact that in some applications the power of the direct signal can be known at the receiver, in addition to the more usual assumptions of known spatial and temporal signatures. At the implementation stage, however, it involves solving a given two-dimensional rank minimization problem, which must be approximated to avoid inherent numerical limitations. In this work, we present an equivalent minimization problem that overcomes such limitations, and therefore leads to alternative implementations that can provide more precise and reliable results. The new approach is justified mathematically, and also supported by several simulations results.

5G mmWave MIMO systems enable accurate estimation of the user position and mapping of the radio environment using a single snapshot when both the base station (BS) and user are equipped with large antenna arrays. However, massive arrays are initially expected only at the BS side, likely leaving users with one or very few antennas. In this paper, we propose a novel method for single-snapshot localization and mapping in the more challenging case of a user equipped with a single-antenna receiver. The joint maximum likelihood (ML) estimation problem is formulated and its solution formally derived. To avoid the burden of a full-dimensional search over the space of the unknown parameters, we present a novel practical approach that exploits the sparsity of mmWave channels to compute an approximate joint ML estimate. A thorough analysis, including the derivation of the Cramér-Rao lower bounds, reveals that accurate localization and mapping can be achieved also in a MISO setup even when the direct line-of-sight path between the BS and the user is severely attenuated.

Integrated satellite-terrestrial communications networks aim to exploit both the satellite and the ground mobile communications and thus provide genuine ubiquitous coverage. For 5G integrated low earth orbit (LEO) satellite communication (SatCom) systems, the timing advance (TA) is required to be estimated in the initial random access procedure of communications in order to facilitate the uplink frame alignment among different users. However, due to the inherent characteristics of LEO SatCom systems, the existing 5G terrestrial uplink TA scheme is not applicable in the satellite networks. In this paper, we investigate location-based TA estimation for 5G integrated LEO SatCom systems. We propose to take the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements obtained in the downlink timing and frequency synchronization phase for geographical location estimation, which are made from the satellite at different time instants. The location estimation is then formulated as a quadratic optimization problem. We propose an approximation method based on iteratively performing a linearization procedure on the quadratic equality constraints to solve this problem. Numerical results show that the proposed method can effectively assure uplink frame alignment among different users in typical LEO SatCom systems.

The problem of position estimation of a mobile user equipped with a single antenna receiver using downlink transmissions is addressed. The advantages of this setup compared to the classical MIMO and uplink scenarios are analyzed in terms of achievable theoretical performance (Cramér-Rao bounds) considering a realistic power budget. Based on this analysis, a low-complexity two-step algorithm with improved localization performance is proposed, which first performs a (coarse) angle of departure estimation and then precodes the down-link signal to introduce beamforming towards the user direction. Results demonstrate that position estimation in downlink can be potentially much more accurate than in uplink, even in presence of multiple users in the system.

The combination of fifth generation (5G) cellular technologies and Global Navigation Satellite Systems (GNSS) is envisaged to pave the way of fulfilling high-accuracy positioning requirements in future use cases. However, these positioning technologies are typically evaluated with independent simulations of statistical channel models for satellite and terrestrial links, which limit the applicability of the performance results. To circumvent this limitation, the proposed simulation method is based on using three-dimensional (3D) city maps to coherently determine the line-of-sight (LoS) conditions of the available satellite and cellular links. These consistent LoS measurements are then considered to assess a hybrid 5G round-trip time (RTT) and multi-constellation GNSS solution in a deep urban canyon. The combination of only one 5G RTT measurement with the GNSS observables significantly improves stand-alone GNSS solutions in terms of horizontal positioning accuracy and availability, achieving below 10 m in 80% of cases over deep urban conditions.

Cellular localization generally relies on time-difference-of-arrival (TDOA) measurements. In this paper, we investigate a novel scenario where the mobile user estimates its own position by jointly exploiting TDOA and angle of departure (AOD) measurements, which are estimated from downlink transmissions in a millimeter-wave (mmWave) multiple-input single-output (MISO) setup. We first perform a Fisher information analysis to derive the lower bounds on the estimation accuracy, and then propose a novel localization algorithm, which is able to provide improved performance also with few transmit antennas and limited bandwidth.

We examine the uplink spectral efficiency of a massive MIMO base station employing a one-bit Sigma-Delta (ΣΔ) sampling scheme implemented in the spatial rather than the temporal domain. Using spatial rather than temporal oversampling, and feedback of the quantization error between adjacent antennas, the method shapes the spatial spectrum of the quantization noise away from an angular sector where the signals of interest are assumed to lie. It is shown that, while a direct Bussgang analysis of the ΣΔ approach is not suitable, an alternative equivalent linear model can be formulated to facilitate an analysis of the system performance. The theoretical properties of the spatial quantization noise power spectrum are derived for the ΣΔ array, as well as an expression for the spectral efficiency of maximum ratio combining (MRC). Simulations verify the theoretical results and illustrate the significant performance gains offered by the ΣΔ approach for both MRC and zero-forcing receivers.

In recent years, the Kalman filter has become the prime approach for estimating parameters that evolve following some dynamic model and prior statistics. In addition, recent contributions are introducing the use of autoregressive models in the state-space formulation to deal with correlated Gaussian-distributed magnitudes. However, the derivation of closed-form expressions for predicting their performance during the design stage is still an open problem. In that regard, in this letter we derive novel approximate closed-form upper bounds to characterize the convergence time of autoregressive Kalman filters. To this end, we extend a batch mode-based approach previously proposed in the literature that reveals the need for a dedicated dual-asymptotic analysis for this kind of techniques. Simulations are provided to show the goodness of the derived results.

In this work, we study optimal transmit strategies for minimizing the positioning error bound in a line-of-sight scenario, under different levels of prior knowledge of the channel parameters. For the case of perfect prior knowledge, we prove that two beams are optimal, and determine their beam directions and optimal power allocation. For the imperfect prior knowledge case, we compute the optimal power allocation among the beams of a codebook for two different robustness-related objectives, namely average or maximum squared position error bound minimization. Our numerical results show that our low-complexity approach can outperform existing methods that entail higher signaling and computational overhead.

Hybridization of Global Navigation Satellite Systems (GNSS) and fifth generation (5G) cellular positioning is foreseen as a key solution to fulfill high-accuracy positioning requirements in future use cases, such as autonomous vehicles. The evaluation of the hybrid positioning capabilities implies the physical-layer simulation of observables from both GNSS and 5G technologies. In order to ease the complexity of the resulting system-level simulations, a physical-layer abstraction of GNSS and 5G ranging observables is here proposed. The abstraction of GNSS ranging observables is based on a Gaussian-distributed model of the errors sources, while the abstraction of 5G ranging observables is based on the interpolation of the cumulative density function (CDF) of the ranging errors for certain propagation conditions and signal-to-noise (SNR) levels. Thanks to the exploitation of the proposed physical-layer abstraction, low-complexity system- level simulations are performed to assess the positioning capabilities of GNSS and 5G downlink time-difference of arrival (DL-TDoA) in urban macro-cell (UMa) environments. The simulation results indicate the need to adopt hybrid solutions based on multiple GNSS constellations and 5G DL-TDoA with 100-MHz bandwidth, in order to ensure a horizontal positioning accuracy below 5 m for 95% of cases in outdoor urban environments.

Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. Despite their benefits for achieving high data rate communications, their potential advantages for tracking of the location and rotation angle of the user terminals are not well investigated. A joint heuristic beam selection and user position and orientation tracking approach is proposed. First, the user location is tracked in the uplink by joint beam selection together with time-of-arrival (TOA) and angle-of-arrival (AOA) tracking at the base station (BS). Then, the user rotation angle is obtained using the location information by joint beam selection and tracking at the mobile station (MS). The beam selection, TOA and AOA tracking, at the BS and MS are performed during the data transmission phase. Numerical results demonstrate that the proposed method performs close to the estimated position and rotation angle in the training phase with reduced complexity and reduced number of required pilots for the estimation.

This paper addresses the problem of determining the unknown position of a mobile station for a mmWave multiple-input single-output (MISO) system. This setup is motivated by the fact that massive arrays will be initially implemented only on 5G base stations, likely leaving mobile stations with one antenna. The maximum likelihood solution to this problem is devised based on the time of flight and angle of departure of received downlink signals. While positioning in the uplink would rely on angle of arrival, it presents scalability limitations that are avoided in the downlink. To circumvent the multidimensional optimization of the optimal joint estimator, we propose two novel approaches amenable to practical implementation thanks to their reduced complexity. A thorough analysis, which includes the derivation of relevant Cramér-Rao lower bounds, shows that it is possible to achieve quasi-optimal performance even in presence of few transmissions, low signal-to-noise ratio (SNRs), and multipath propagation effects.

5G new radio (NR) provides new opportunities for accurate positioning from a single reference station: large bandwidth combined with multiple antennas, at both the base station and user sides, allows for unparalleled angle and delay resolution. Nevertheless, positioning quality is affected by multipath and clock biases. We study, in terms of performance bounds and algorithms, the ability to localize a vehicle in the presence of multipath and unknown user clock bias. We find that when a sufficient number of paths is present, a vehicle can still be localized thanks to redundancy in the geometric constraints. Moreover, the 5G NR signals enable a vehicle to build up a map of the environment.

The fifth generation of mobile communications (5G) is expected to exploit the concept of location-aware communication systems. Therefore, there is a need to understand the localization limits in these networks, particularly, using millimeter-wave technology (mmWave). Contributing to this understanding, we consider single-anchor localization limits in terms of 3D position and orientation error bounds for mmWave multipath channels, for both the uplink and downlink. It is found that uplink localization is sensitive to the orientation angle of the user equipment (UE), whereas downlink is not. Moreover, in the considered outdoor scenarios, reflected and scattered paths generally improve localization. Finally, using detailed numerical simulations, we show that mmWave systems are in theory capable of localizing a UE with sub-meter position error, and sub-degree orientation error.

We consider the beam-training procedure in the future 5G millimeter-wave systems and collect position information based on the received signals. We analyze the degradation due to beam misalignment on the achievable rate and on the amount of information available for positioning. We evaluate the performance of two beam-training strategies, namely, exhaustive and hierarchical. Our results reveal new insights on the trade-off between positioning and communication performance.

The Kalman filter is adopted in a myriad of applications for providing the minimum mean square error estimation of time-varying parameters in a simple and systematic manner. However, determining the Kalman filter performance is not so straightforward, particularly when process noise is present. In that case, one must often resort to numerical evaluations of the recursive Bayesian Cramér-Rao bound, or alternatively to implement the filter and assess the performance through Montecarlo simulations. This letter is intended to circumvent this limitation. It proposes a closed-form approximation for the steady-state performance of a Kalman filter based on a second-order dynamic model, while at the same time providing a novel closed-form upper bound for the convergence time. These two results are obtained by reformulating the Kalman filter in batch mode and analyzing the inner structure of the Bayesian information matrix. Simulation results are provided to illustrate the goodness of the proposed approach.

The Kalman filter is used in a myriad of applications for estimating a set of time-varying parameters in the minimum mean square error sense. When designing the filter, one of the points of most relevant interest is predicting its estimation performance. However, it is often difficult to find expressions in closed form that allow obtaining this information in a straightforward manner. As a consequence, the designer usually requires resorting to the numerical evaluation of the Bayesian Cramér-Rao bound or the implementation and assessment of the filter through Monte Carlo simulations. In this regard, this letter intends to contribute with a novel closed-form upper bound for the convergence time of a Kalman filter. To this end, the Kalman filtering problem is reformulated in batch mode, and the corresponding Fisher information matrix is analyzed. The contribution presented herein is based on a generic dynamic model and is not restricted to any specific order, in contrast to existing contributions. Simulation results are provided to illustrate the goodness of the proposed approach.

Location-aware communication systems are expected to play a pivotal part in the next generation of mobile communication networks. Therefore, there is a need to understand the localization limits in these networks, particularly, using millimeter-wave technology (mm-wave). Towards that, we address the uplink and downlink localization limits in terms of 3D position and orientation error bounds for mm-wave multipath channels. We also carry out a detailed analysis of the dependence of the bounds on different system parameters. Our key findings indicate that the uplink and downlink behave differently in two distinct ways. First of all, the error bounds have different scaling factors with respect to the number of antennas in the uplink and downlink. Secondly, uplink localization is sensitive to the orientation angle of the user equipment (UE), whereas downlink is not. Moreover, in the considered outdoor scenarios, the non-line-of-sight paths generally improve localization when a line-of-sight path exists. Finally, our numerical results show that mm-wave systems are capable of localizing a UE with sub-meter position error, and sub-degree orientation error.

Emerging wireless communication systems will be characterized by a tight coupling between communication and positioning. This is particularly apparent in millimeter-wave (mm-wave) communications, where devices use a large number of antennas, and the propagation is well described by geometric channel models. For mm-wave communications, initial access, consisting in the beam selection and alignment of two devices, is challenging and time consuming in the absence of location information. Conversely, accurate positioning relies on high-quality communication links with proper beam alignment. This paper studies this interaction and proposes a new position-aided transmitter beam selection protocol, which considers the problem of joint communication and positioning in scenarios with direct line-of-sight and scattering. Simulation results show significant reductions in latency with respect to a standard protocol.

Millimeter-wave (mm-wave) signals and large antenna arrays are considered enabling technologies for future 5G networks. While their benefits for achieving high-data rate communications are well-known, their potential advantages for accurate positioning are largely undiscovered. We derive the Cramér-Rao bound (CRB) on position and rotation angle estimation uncertainty from mm-wave signals from a single transmitter, in the presence of scatterers. We also present a novel two-stage algorithm for position and rotation angle estimation that attains the CRB for average to high signal-to-noise ratio. The algorithm is based on multiple measurement vectors matching pursuit for coarse estimation, followed by a refinement stage based on the space-alternating generalized expectation maximization algorithm. We find that accurate position and rotation angle estimation is possible using signals from a single transmitter, in either line-of-sight, non-line-of-sight, or obstructed-line-of-sight conditions.

In location-aware wireless networks, mobile nodes (agents) can obtain their positions using range measurements to other nodes with known positions (anchors). Optimal subcarrier power allocation at the anchors reduces positioning error and improves network lifetime and throughput. We present an optimization framework for subcarrier power allocations in network localization with the imperfect knowledge of network parameters based on the fundamental statistical limits. Power allocations with expectation and robustness constraints are obtained using semidefinite optimization problems in non-iterative and iterative forms with both unicast and multicast transmissions. Results show that the allocations provide more accurate localization than non-robust designs under channel and agents positions uncertainty.

The utilization of the millimeter-wave frequency band (mm-wave) in the fifth generation ({5G}) of mobile communication is a highly-debated current topic. Mm-wave MIMO systems will use arrays with large number of antennas at the transmitter and the receiver, implemented on a relatively small area. With the inherent high directivity of these arrays, algorithms to help the user equipment find the base station and establish a communication link should be carefully designed. Towards that, we examine two beamforming schemes, namely, random-phase beamforming (RPBF) and directional beamforming (DBF), and test their impact on the Cram\'er-Rao lower bounds (CRB) of jointly estimating the direction-of-arrival, direction-of-departure, time-of-arrival, and the complex channel gain, under the line-of-sight channel model. The results show that the application of RPBF is more appropriate in the considered scenario as it attains a lower CRB with fewer beams compared to DBF.

In indoor and dense urban environments, Global Navigation Satellite System (GNSS) receivers have to cope with extremely low received power levels. In these circumstances, GNSS receivers become vulnerable to adverse propagation effects such as near-far interference, where acquisition is affected by large cross-correlation peaks because signals from different satellites experience very different attenuation patterns. This translates into a degraded pseudoranges performance, and enters into conflict with the demand for improved positioning accuracy and integrity motivated by the widespread use of GNSS receivers in these scenarios. This paper presents novel low-complexity techniques for near-far detection in high-sensitivity GNSS receivers. These techniques exploit the statistical differences of the acquisition measurements in the presence and in the absence of near-far, and they outperform previously proposed detectors in terms of detection probability. A method to determine the detection threshold for a given probability of false alarm is also presented. Simulations for Galileo E1C signals are used to illustrate the enhanced performance of the proposed algorithms.

One of the difficulties faced by high-sensitivity GNSS receivers is the so-called near-far problem, where the acquisition of weak signals is hampered by the presence of more powerful signals. If countermeasures are not implemented, the presence of near-far may result in loss or false acquisition of weak signals, and consequently the user's position may exhibit a huge error. In this sense, subspace projection techniques become an attractive choice for near-far mitigation purposes due to their effectiveness and low-complexity. However, many questions still remain open to make the techniques implementable in real handheld receivers such as mobile phones, which have not yet been addressed in the literature. This paper contributes with an analysis of the robustness of projection-based mitigation techniques when the synchronization parameters or the data bits of the interferences are not perfectly estimated. On the other hand, the paper also analyses the impact that signal filtering and quantization at the receiver front-end may also have on the near-far mitigation performance. The approaches presented in this paper give an actual idea of what would happen in practice in a real GNSS receiver. To analyse these effects, an extensive simulation campaign was conducted for Galileo E1 signals.

Localization accuracy of trilateration methods in long term evolution (LTE) cellular networks, which are based on time-of-arrival, may be highly degraded due to multipath and non-line of sight conditions in urban and indoor environments. Multipath mitigation techniques usually involve a high computational burden and require wideband signals to be effective, which limit their adoption in certain low-cost and low-power mobile applications using narrow-band signals. As an alternative to these conventional techniques, this paper analyzes an expectation maximization (EM) localization algorithm that considers the skewness introduced by multipath in the LTE ranging error distribution. The EM algorithm is extensively studied with realistic emulated LTE signals of 1.4-MHz bandwidth. The EM method is compared with a standard nonlinear least squares (NLS) algorithm under ideal simulated conditions and using realistic outdoor measurements from a laboratory testbed. The EM method outperforms the NLS method when the ranging errors in the training and test stages have similar distributions.

Recently, there has been an increasing interest in GNSS-based safety and liability applications. These applications, which are often associated to urban environments or in general to vehicular applications, have very stringent requirements in terms of accuracy, continuity and integrity of the provided position solution. In this paper, we present simple quickest threat detectors for being used in low-cost GNSS receivers. The aim is to detect the presence of interference and multipath as soon as posible in order to improve GNSS integrity.

In this paper, we assess the physical-layer secrecy performance of the amplify-and-forward compressed sensing (AF-CS) framework when malicious eavesdropping nodes are listening. In particular, we investigate the robustness of the AF-CS scheme in the presence of a group of coordinated eavesdropping nodes under the assumption that they have corrupted channel state information. In order to fulfil this assumption, we propose a channel estimation technique based on pseudorandom pilots. This technique introduces extra uncertainty only in the channel estimation of the eavesdroppers. Our simulation results evaluate the physical-layer protection as a function of the total number of coordinated eavesdroppers and the level of channel estimation distortion of the eavesdroppers. We demonstrate that a small number of eavesdroppers (small being defined later on) has a zero probability of recovering the intended signal. We also show that a very large number of eavesdropping nodes are required to perfectly recover the signal in comparison with other distributed compressed sensing schemes in the literature.

This paper addresses the problem of multipath mitigation with GNSS antenna arrays. A beamformer that is able to cancel the multipath components regardless of their relative delay and directions of arrival is proposed. The weights are obtained from a set of spatial correlation matrices that allows us to estimate the multipath subspace. These matrices are generated after a FIR filter that reduces the correlation between the multipath components and the line-of-sight signal, and it is only used for spatial processing. Some representative simulation results show the multipath attenuation provided by the proposed method under different conditions.

The design of pilot and data power allocations for multicarrier OFDM signals is a key aspect in the development of combined positioning and high-data-rate communications systems. In this paper, we investigate capacity-maximizing pilot and data power allocations when a certain positioning accuracy is required. We consider a formulation based on the Expected Crámer-Rao Bound of the joint time-delay and channel estimation and the ergodic capacity, modeling the channel impulse response as a random vector. We compare the performance of capacity-maximizing pilot and data power distributions with respect to distributions that use equi-spaced and equi-powered pilot structures, shown by previous work to be optimal in terms of channel estimation. Numerical results show that the restriction to the use of equi-spaced and equi-powered pilot structures has an important impact on both the achievable capacity and the positioning capabilities of the designed signals.

This contribution deals with robust carrier phase tracking in Global Navigation Satellite Systems, where the ultimate goal is to obtain accurate and robust phase estimates under non-nominal conditions, such as high dynamics, strong fading and ionospheric scintillation. Within this framework, an Interacting Multiple Model approach, using a bank of parallel Kalman-based filters with different dynamic state models, is proposed to cope with signals corrupted by severe ionospheric scintillation. In the proposed formulation, the time-varying and correlated scintillation phase is introduced into the dynamic system using an AR(1) model. Simulation results are provided to show the enhanced robustness and improved accuracy of the proposed approach, with respect to state-of-the-art carrier phase tracking techniques.

Orthogonal frequency division multiplexing (OFDM) communication systems require accurate estimation of timing offset and channel impulse response in order to achieve desirable performance. In this paper, we consider the optimal placement of pilot symbols over the OFDM subcarriers in order to minimize a function of the Cramer-Rao bound on these parameters. Previous work has investigated this problem for channel estimation only, and found that equi-spaced, equi-powered pilots are optimal. We show that when the time-delay must be simultaneously estimated, the optimal pilot distribution is often quite different, with more pilot energy distributed to the edges of the signal bandwidth. Upper and lower bounds for the required number of optimal pilots are also presented for the case where the variance on the time-delay estimate is minimized.

This paper analyzes the problem of distributed composite signal detection in a sensor-to-sensor (S2S) scenario. Based on the classical Generalized Likelihood Ratio Test (GLRT) and Bayesian approaches, some insights are provided for extending classical detection theory to cooperative environments. As a result, innovative decision rules are proposed by taking advantage of prior information from neighboring sensors (for instance, using maximum likelihood estimates of the unknown parameters). Simulation results are provided confirming the outperforming behavior of the proposed collaborative techniques.

In this paper, we consider a cooperative system based on relay selection in a scenario where the available channel state information (CSI) is subject to delays. In order to exploit the selection diversity gains of the system while providing robustness against CSI inaccuracy, we propose a robust relay selection strategy based on a minimum mean square error (MMSE) Bayesian estimator. As shown in the paper, the proposed robust strategy provides significant gains in scenarios with different levels of CSI inaccuracy.

In this paper we analyze the feasibility of performing Quantum Key Distribution (QKD), in Earth-satellite up and downlinks and in intersatellite links, with two quantum cryptography protocols: BB84 and SARG04, and with two implementation options: with and without decoy states. As real measurements in these scenarios are not possible yet, the objective is to obtain results as realistic as possible to support the design of future satellite missions performing QKD. Therefore, we use realistic values for the optical hardware and take into account usual atmospheric conditions. In the same line, we assume specific types of attacks, namely the photon number splitting and the intercept-resend with unambiguous discrimination attacks, which could likely be the main threat to the first satellite-based QKD applications. A lower bound on the key generation rate of SARG04 with two decoy states is presented. The optimum signal- and decoy-states mean photon numbers for each protocol and each distance are also computed. The resulting values for the signal-state are larger than those often employed. We show that it may be possible to establish QKD with LEO (Low Earth Orbit) and, under certain circumstances, with MEO (Medium Earth Orbit) satellites, but not with GEO (Geostationary) ones. Furthermore, we obtain that the optimum signal-state mean photon number for SARG04 with two decoy states is almost independent of the link distance, which greatly facilitates its use in a real scenario.

In this paper, we analyze the outage probability and diversity order of opportunistic relay selection in a scenario based on decode and forward and where the available channel state information (CSI) is outdated. The study is conducted analytically by obtaining a closed-form expression for the outage probability, which is defined as the probability that the instantaneous capacity is below a target value. We derive high-SNR approximations for the outage probability. By doing so, we demonstrate that the diversity order of the system is reduced to 1 when CSI is outdated, being this behavior independent of the level of CSI accuracy. A physical explanation for this extreme loss of diversity is provided along with numerical results to support the analytical study.

This work proposes an algorithm to perform the resource allocation in the uplink of an IEEE802.16 standard-based system. The approach is valid for point to multi-point (PMP) and also for tree-deployed mesh networks, already defined for the Worldwide Interoperability for Microwave Access (WiMax). Our solution is based on a proportionally fair distribution of resources and it is formulated using the network utility maximization (NUM) framework. Thanks to convex decomposition techniques, we derive a novel way of solving the NUM problem in a distributed manner. The goal is to attain the global optimal scheduling at the subscriber stations (SS) without the need of gathering information at a central node in the network. The results show significant gains in the time required to reach the optimal resource allocation for a given set of demands.

This paper deals with the joint estimation of temporal (time- delay, Doppler frequency) and spatial (direction-of-arrival, DOA) parameters of several replicas of a known signal in an unknown spatially correlated field. Unstructured and structured models have been proposed in the literature. The former suffers from a severe performance degradation in some scenarios, whereas the latter involves huge complexity. It is shown how the extended invariance principle (EXIP) can be applied to obtain estimates with the quality of those of the structured model, but with the complexity of the unstructured one. We present a method to improve the quality of the time- delay and Doppler estimates obtained with an unstructured spatial model when an estimate of the DOAs is available. Exemplarily, simulation results for time-delay estimation for GPS (global positioning system) are included and confirm that our proposal approaches the Cramer-Rao lower bound (CRLB) of the structured model even when suboptimal DOA estimates obtained by ESPRIT are introduced.

The problem of dynamic bandwidth allocation (DBA) is inherent to systems that employ bandwidth on demand (BoD). An important issue in such systems is to be able to react efficiently to the always-changing traffic requests of users. Moreover, it is realistic to assume large populations sharing system resources and thus efficient methods to distribute bandwidth are mandatory. Further desirable system features include guarantees on fairness and on quality of service (QoS). Actual trends propose to reach convergence among networks at IP-level. This encourages the design of algorithms that sustain IP-defined QoS (e.g. in DiffServ) and forces to exchange information between layers. We talk then about cross-layer designs. In this paper, we propose a novel method to compute the allocation accomplishing the previous requirements of fairness, QoS and time efficiency. Our work departs from known results on decomposition techniques (primal and dual) and combines these in a novel, interleaved and coupled fashion. In the dual decomposition technique, the subgradient method is typically used to adaptively compute the price the resource is charging to the users. In our approach, the price is selected taking into account the value that users are willing to pay, which comes from the primal decomposition. The method is compared to the well-known bisection one and results effectively demonstrate superior performance in terms of convergence speed and computational complexity.

This paper presents the architecture of a GPS receiver suitable for indoor operation with extremely weak signals. The receiver works in acquisition-only mode, and combines coherent and long non-coherent correlations in order to achieve low sensitivity. It is confirmed by processing simulated and live GPS signals that this kind of receivers must have the capability to detect and mitigate near-far interference. Novel near-far detection and mitigation techniques are proposed. The new detector is based on the different statistics of the two largest cross-correlation peaks in the presence and in the absence of near-far interference, and it outperforms previously proposed detectors in terms of (mis)detection probability and complexity. The mitigation technique is based on the concepts of successive interference cancellation and subspace projection. It is also novel the fact that, according to the proposed receiver architecture, the near-far mitigation technique is only applied selectively, when the detector has found some interfered signals and the interfering signals have been identified. This reduces the computational complexity without any performance penalty, compared to previously proposed techniques that eliminate the contribution of all potentially (but not necessarily) interfering signals.

This paper focuses on the broadcast channel of an interactive multibeam broadband satellite (MBS) system with a transparent architecture. In particular, a cross-layer design is proposed for the packet scheduling on a forward link that implements adaptive coding and modulation (ACM). A cross-layer approach is considered whereby the physical and medium access control (MAC) layers share knowledge of the channel dynamics in presence of ACM. Transmission power and symbol rate are assumed to be constant, and hence the bit rate is time and space dependant according to the channel conditions. An architecture is proposed which relies on the physical characteristics of the Ka-band satellite propagation channel and the definition of "correlated areas". The stable throughput region has been derived assuming full-queue traffic conditions. Moreover, the proposed architecture is simple but flexible enough to allow the implementation of different scheduling policies like the proportionally fair or opportunistic ones. Finally, a new time-fair policy appropriate for wet seasons is proposed. It has the property of isolating users in clear-sky conditions from the effects of the reduced transmission rate experienced by users under a rain fade

This paper introduces a novel operational framework for the problem of time slot assignment in a digital video broadcast-return channel via satellite (DVB-RCS) system. The approach is compliant with the latest technical specifications emitted by the European telecommunications standards institute (ETSI) about quality of service (QoS) in satellite earth stations and systems (SES). It is a cross-layer MAC-PHY optimization approach sustained by the powerful framework of convex optimization. The paper proposes a hierarchical dynamic bandwidth allocation approach, which is motivated by the computational complexity of the single-step solution. More specifically, we obtain and analyze the optimal time duration of the time slots and jointly, we make a fair allocation of slots to areas, which is the highest level in the bandwidth allocation hierarchy. Results show up to a 10% increase in transported capacity.

This paper presents a general framework and the corresponding solution for the problem of fair resource allocation among entities with absolute and relative QoS requirements. It is described how the framework can be applied to a variety of scenarios, in particular to the scheduling of the TDM transmission in DVB-S2 and to the dynamic bandwidth allocation needed in DVB-RCS systems. A usual need in this type of problems is that the allocation has to be computed in (almost) real-time even when the number of entities is very large. The paper proposes a low-complexity algorithm. The algorithm provides the exact solution and numerical simulations shows its low computation time.

This paper considers the problem of estimating the time delays of multiple replicas of a known signal received by an array of antennas. Under the assumptions that the noise and co-channel interference (CCI) are spatially colored Gaussian processes and that the spatial signatures are arbitrary, the maximum likelihood (ML) solution to the general time delay estimation problem is derived. The resulting criterion for the delays yields consistent and asymptotically efficient estimates. However, the criterion is highly non-linear, and not conducive to simple minimization procedures. We propose a new cost function that is shown to provide asymptotically efficient delay estimates. We also outline a heuristic way of deriving this cost function. The form of this new estimator lends itself to minimization by the computationally attractive iterative quadratic maximum likelihood (IQML) algorithm. The existence of simple yet accurate initialization schemes based on ESPRIT and identity weightings makes the approach viable for practical implementation.

The problem of estimating the propagation-delay of a desired signal in the presence of interferences and multipath propagation is addressed. This paper presents the maximum likelihood (ML) propagation-delay estimator for a signal arriving at a sensor array. The novel characteristic is that the desired signal impinges on the array with a known steering vector. This fact allows to assume an unknown and arbitrary spatially colored noise. The Cramer-Rao bound (CRB) for the problem is derived and numerically compared with the variance of the MLE. The MLE is applied to the Global Navigation Satellite Systems, in order to reduce the serious performance deterioration that the interferences ad the multipath propagation produce. We show that in presence of coherent reflections of the desired signal the presented estimator is no longer the MLE and becomes biased. However, its bias is much lower than that of other conventional estimators.