Vital information wireless sensing is an interesting alternative to conventional techniques based on contact sensors. The evolution of radar technologies enables the proposal of cost-effective and compact radar sensors designed for diverse health monitoring, diagnostics, and Internet of Medical Things (IoMT) applications. It is possible to observe respiratory rate (RR), heart rate (HR), cardiac rhythm, blood pressure waveform (BPW), and emerging biometrics from the phase of radar echo signals. However, recognition and separation of the signals of interest from other interfering motion is a challenge, taking into account the diversity of signal patterns depending on subjects, health and observation conditions. In this context, we propose a novel wireless vital sensing system with a self-designed 120 GHz Frequency-Modulated Continuous Wave (FMCW) Radar. The developed sensor, based on a Radar System-on-Chip (RSoC), uses a real-time Repetitive Waveform Adaptive Matched Filter (RWAMF) maximizing Signal-to-Interference-plus-Noise Ratio (SINR) for reliable cardiac rhythm and BPW observations. The radar design has been optimized for vital sensing with a very short wavelength providing excellent sensitivity to micro-motion and a centimetric range and transversal resolutions to reject clutter and unwanted motions. The superior performance of our solution has been validated and evaluated experimentally on different subjects and measurement conditions with our own acquired vital signal database.

The precise and accurate indoor positioning using cellular communication technology remains to be a prerequisite for several industrial applications, including the emergence of a new topic of Integrated Sensing and Communication (ISAC). However, the frequently occurring Non-Line-of-Sight (NLoS) conditions in a heavy multipath dominant industrial scenario challenge the wireless signal propagation, leading to abnormal estimation errors (outliers) in the signal measurements taken at the receiver. In this paper, we investigate the iterative positioning scheme that is robust to the outliers in the Time of Arrival (ToA) measurements. The Iteratively Reweighted Least Squares (IRLS) positioning scheme formulated on the Least Squares (LS) is implemented to reject the outlier measurements and reweight the available ToA samples based on their confidence. Our positioning scheme is validated under 5G frequency bands, including the C-band (3.7 GHz) and the mmWave-band (26.8 GHz) in a Ray-Tracing enabled industrial scenario with different emulation setups.

This article addresses near-field localization using Reconfigurable Intelligent Surfaces (RIS) in 5G systems, where Line-of-Sight (LOS) between the base station and the user is obstructed. We propose a RIS phase optimization method based on the minimization of the Cramér-Rao Lower Bound (CRLB) for position estimation. The main contributions of the article are: (1) the derivation of the CRLB to optimize the RIS configuration; and (2) the application of the mentioned framework in nearfield considering reflective RIS. The proposed method is validated with simulations, showing an accuracy improvement of RIS phase optimization with respect to the state-of-the-art methods.

This paper deals with the multiple relay selection problem in two-hop wireless cooperative networks with individual power constraints at the relays. In particular, it addresses the problem of selecting the best subset of K cooperative nodes and their corresponding beamforming weights so that the signal-to-noise ratio (SNR) is maximized at the destination. This problem is computationally demanding and requires an exhaustive search over all the possible combinations. In order to reduce the complexity, a new suboptimal method is proposed. This technique exhibits a near-optimal performance with a computational burden that is far less than the one needed in the combinatorial search. The proposed method is based on the use of the l1-norm squared and the Charnes-Cooper transformation and naturally leads to a semidefinite programming relaxation with an affordable computational cost. Contrary to other approaches in the literature, the technique exposed herein is based on the knowledge of the second-order statistics of the channels and the relays are not limited to cooperate with full power.

Compressive sensing (CS) has been successfully applied to alleviate the sampling bottleneck in wideband spectrum sensing leveraging the sparsity described by the low spectral occupancy of the licensed radios. However, the existence of interferences emanating from low-regulated transmissions, which cannot be taken into account in the CS model because of their nonregulated nature, greatly degrade the identification of licensed activity. This paper presents a feature-based technique for primary user's spectrum identification with interference immunity which works with a reduced amount of data. The proposed method not only detects which frequencies are occupied by primary users' but also identifies the primary users' transmitted power. The basic strategy is to compare the apriori known spectral shape of the primary user with the power spectral density of the received signal. This comparison is made in terms of autocorrelation by means of a correlation matching, thus avoiding the computation of the power spectral density of the received signal. The essence of the novel interference rejection mechanism lies in preserving the positive semidefinite character of the residual correlation, which is inserted by means of a weighted formulation of the l1-minimization. Simulation results show the effectiveness of the technique for interference suppression and primary user detection.

In cognitive radio (CR), the problem of limited spectral resources is solved by enabling unlicensed systems to opportunistically utilize the unused licensed bands. Compressive Sensing (CS) has been successfully applied to alleviate the sampling bottleneck in wideband spectrum sensing leveraging the sparseness of the signal spectrum in open-access networks. This has inspired the design of a number of techniques that identify spectrum holes from sub-Nyquist samples. However, the existence of interference emanating from low-regulated transmissions, which cannot be taken into account in the CS model because of their non-regulated nature, greatly degrades the identification of licensed activity. Capitalizing on the sparsity described by licensed users, this paper introduces a feature-based technique for primary user's spectrum identification with interference immunity which works with a reduced amount of data. The proposed method detects which channels are occupied by primary users' and also identify the primary users transmission powers without ever reconstructing the signals involved. Simulation results show the effectiveness of the proposed technique for interference suppression and primary user detection.

We address the problem of detecting building dominant scatterers using a reduced number of measurements with applications to through-the-wall radar (TWR) and urban sensing. We consider oblique illumination, which specially enhances the radar returns from the corners formed by the orthogonal intersection of two walls. This letter uses a novel type of image descriptor, named correlogram, which encodes information about spatial correlation of complex amplitudes of each TWR image pixel. The proposed technique compares the known correlogram of the scattering response of an isolated canonical corner reflector with the correlogram of the received radar signal. The feature-based nature of the proposed detector enables corner separation from other indoor scatterers, such as humans.

Compressive sensing (CS) for urban operations and through-the-wall radar imaging has been shown to be successful in fast data acquisition and moving target localizations. The research in this area thus far has assumed effective removal of wall electromagnetic backscatterings prior to CS application. Wall clutter mitigation can be achieved using full data volume which is, however, in contradiction with the underlying premise of CS. In this paper, we enable joint wall clutter mitigation and CS application using a reduced set of spatial-frequency observations in stepped frequency radar platforms. Specifically, we demonstrate that wall mitigation techniques, such as spatial filtering and subspace projection, can proceed using fewer measurements. We consider both cases of having the same reduced set of frequencies at each of the available antenna locations and also when different frequency measurements are employed at different antenna locations. The latter casts a more challenging problem, as it is not amenable to wall removal using direct implementation of filtering or projection techniques. In this case, we apply CS at each antenna individually to recover the corresponding range profile and estimate the scene response at all frequencies. In applying CS, we use prior knowledge of the wall standoff distance to speed up the convergence of the orthogonal matching pursuit for sparse data reconstruction. Real data are used for validation of the proposed approach.

This paper addresses the problem of TOA and DOA estimation in IR-UWB systems. It considers a frequency domain approach for joint high resolution estimation of Time Of Arrival (TOA) and Direction Of Arrival (DOA). The proposed scheme performs timing acquisition following a two step approach: a first stage where coarse symbol timing is achieved by means of chip energy estimation and a minimum distance criterion based on the time-hopping sequence knowledge, followed by a reduced complexity high resolution TOA estimator that provides fine timing acquisition. The proposed algorithm can resolve time synchronization blindly, without the need for channel estimation and can operate on a single symbol basis. DOA estimation is then obtained from TOA estimates at each array element applying a linear estimator. The joint estimation of TOA and DOA provides robustness and potentially allows positioning with a single reference node. Furthermore, the paper derives the Cramer-Rao Lower Bound from a frequency domain signal model for a multipath channel where no assumptions are made with respect to the paths, which results in a compact closed form.

This paper addresses the energy efficiency analysis of the relay channel under additive white Gaussian noise. We consider the rate bounds given by decode and forward and the cut set bound and assume that resources are optimally allocated to maximize the spectral efficiency according to the channel information and the sum network energy. The low energy analysis tools are used to compute the maximum rate per energy (RPE) and the slope of the spectral efficiency as a function of the energy per bit. Using these metrics, the energy efficiency benefit of several capabilities at terminals is investigated. Specifically, we take into account: (i) the phase synchronization between transmitters, (ii) the full duplex capability at the relay and (iii) the channel access via superposition.

Positioning estimation based on time of arrival (TOA) estimation needs high accurate first arrival detector. High-resolution TOA estimators based on minimum variance (MV) and normalized minimum variance (NMV) provide an accurate estimation of the first arrival even in high multipath environments at the expenses of a high computational cost. The aim of this paper is to reduce the computational burden of high resolution TOA estimators. First, reduced complexity MV and NMV implementations based on the FFT are presented. Next, polynomial root versions of both estimators are proposed yielding an improvement in positioning accuracy. Finally, a near TOA maximum likelihood (ML) estimator is proposed providing a good trade off between complexity and accuracy.

This paper addresses the problem of TOA estimation for ranging applications in IR-UWB. It considers a low complexity frequency-domain approach for the estimation of the TOA that allows sub-Nyquist sampling rate receivers. Two diversity schemes are investigated in combination with the TOA estimation algorithm and are evaluated over realistic channel models, yielding accurate ranging estimation.

The main impairment for the location of terminals in a wireless communications system is the non-line-of-sight (NLOS) condition which is mainly due to blocking of the transmitted signal by obstacles. It is well known that NLOS biases time-of-arrival (TOA) and time-difference-of-arrival (TDOA) estimates, therefore providing a biased position. The paper analyzes the improvements in positioning accuracy by tracking the bias of the time measures with the Kalman filter proposed for location estimation. The evaluation of the approach has been carried out with measures taken in real scenarios, for positioning techniques based on TDOA and TOA+AOA (angle of arrival) measurements.

This paper deals with the problem of nonline-of-sight (NLOS) in wireless communications systems devoted to location purposes. It is well known that NLOS biases time of arrival (TOA) and time difference of arrival (TDOA) estimates thus reducing accuracy of positioning algorithms. In order to achieve positioning error reduction the Kalman filter proposed for location estimation includes the time measurement bias for each base station (BS) as additional parameters to be estimated. The use of the Kalman filter allows then tracking, not only the position and speed of the mobile, but also the bias due to NLOS, yielding an accurate location prediction algorithm.

A potentiality of the location problem in wireless networks that has not been explored enough is the mobility of the user equipment (UE). This paper deals with UMTS mobile location tracking using Kalman filtering based on TDOA (time difference of arrival) measurements. The performance of the proposed approach is compared with the CRB (Cramer Rao bound) for the static location estimator.

The problem of joint beamforming and channel estimation for multi-rate multi-code systems is addressed. Usual schemes perform this filtering/estimation operation making use of a training sequence time-multiplexed with the transmitted data. However if pilot and traffic signals are transmitted simultaneously using distinct code allocation-as it is the case in recent standards such as cdma2000 or WCDMA-these schemes tend to fail. This paper proposes semi-blind techniques to overcome the uplink auto-interfering effects of such systems. It is shown that the semi-blind approach yields substantially better performance results thanks to the implicit modeling of the unknown traffic data.

This paper addresses a new architecture for blind adaptive beamforming when dealing with frequency hopping (FH) modulation in cellular mobile communications systems. The proposed code reference beamformer (CRB) takes advantage of the inherent frequency diversity to estimate beforehand the noise plus interference correlation matrix, which is employed as the first part of the framework. Then, a second stage is adaptively obtained without any a priori knowledge of either the direction of arrival or the array manifold. Using this information, the first stage is in turn readjusted and, as a result, the scheme is able to track non-stationary scenarios following the channel variations with no previous references.

A new architecture for adaptive arrays using frequency hopping modulation is addressed. The resolution of the array and the interference rejection increase substantially applying random processing to the carrier frequency of the signals. The proposed framework is composed of two different stages. The anticipative stage, devoted to minimize the noise and fixed interferences contribution and the GSLC stage which provides cancellation of follower jammers and solves the multiuser collision problem. The developed system requires neither temporal nor spatial reference for its implementation, only the frequency sequence must be known. An adaptive approach has been implemented, allowing a fast convergence to the optimal behavior.

This paper deals with the general problem of separating two independent wideband sources when they are mixed by unknown filters. In order to solve this problem, a backward framework is proposed which is composed of two different stages. The first one consists of two linear predictors devoted to improve the source separation, whitening the input signals. Their coefficients are calculated applying the LMS algorithm, minimizing the mean squared errors between the predicted signals and the output of the separation network. The second stage is formed by decoupling filters that have to be blindly estimated imposing an independence criterion to the outputs.>