We explore applying group theory principles in designing waveforms for a cognitive radar. By leveraging affine groups, we provide a mathematical framework for designing radar waveforms for a wideband multiple-input multiple-output (MIMO) radar. Prior works in this area have considered this approach for the narrowband or single-antenna radar systems. We first derive a general wideband ambiguity function of a MIMO radar by correlating the signal with its time-dilated, Doppler-shifted, and position-delayed replicas. The ambiguity function is essentially a coefficient function of the unitary representation of an affine group. We then construct complementary waveforms that minimize range sidelobes in the cross-ambiguity matrix, depending on the requirements of cognitive radar. Our numerical experiments demonstrate the effectiveness of group-theory-based complementary waveform designs.

Aerial base stations mounted on unmanned aerial vehicles (UAVs) support next-generation wireless networks in challenging environments such as urban areas, disaster zones, and remote locations. Further, UAV swarms overcome the challenges of limited battery life and other operational constraints of a single UAV. However, tracking mobile users on the ground by each UAV and the corresponding synchronization between the UAVs is a significant issue to be resolved before field deployment. Incorporating additional sensing capabilities to fulfill this requirement introduces significant overhead in terms of hardware, cost, and power to each UAV. To this end, we develop a swarm UAV integrated sensing and communications (ISAC) system using the millimeter-wave IEEE 802.11ad protocol. Our proposed system is capable of five-dimensional (5-D) (range, Doppler velocity, azimuth, elevation, and polarization) ground target sensing in an urban environment. Numerical experiments using realistic models demonstrate and validate the performance of 5-D sensing using our proposed 802.11ad-aided UAV ISAC framework.

The evolution of array signal processing technologies is progressing toward the deployment of compact, densely arranged sensors to form extremely large aperture arrays (ELAA), aiming to significantly improve angular resolution and beamforming gain. In this paper, we propose a wideband near-field sensing system that combines orthogonal frequency-division multiplexing (OFDM) signaling with ELAA technology. The ELAA transmitter emits wideband signals, while the radar receiver processes the echoes to estimate critical target parameters, including location, velocity, and radar cross-section. We then derive the generalized Cramér-Rao lower bound (CRB) to assess the OFDM system’s estimation performance. Numerical experiments reveal that the proposed wideband near-field sensing system outperforms its far-field and narrowband counterparts by achieving lower CRB values.

Reconfigurable intelligent surfaces (RIS) offer unprecedented flexibility for smart wireless channels. Recent research shows that RIS platforms enhance signal quality, coverage, and link capacity in integrated sensing and communication (ISAC) systems. This paper explores the use of fully-connected beyond diagonal RIS (BD-RIS) in ISAC. BD-RIS provides additional degrees of freedom by allowing non-zero off-diagonal elements in the scattering matrix, enhancing functionality and performance. We aim to maximize the weighted sum of the signal-to-noise ratio (SNR) at both the radar receiver and communication users using BD-RIS. Numerical results demonstrate the advantages of BD-RIS in ISAC, significantly improving SNR for both radar and communication users.

Source enumeration typically relies on subspace-based techniques that require accurate separation of signal and noise subspaces. However, prior works do not address coherent sources in small uniform linear arrays, where ambiguities arise in the spatial spectrum. We address this by decomposing the forward-backward smoothed covariance matrix into a sum of a rank-constrained Toeplitz matrix and a diagonal matrix with non-negative entries representing the signal and noise subspaces, respectively. The resulting non-convex optimization problem is solved by proposingToeplitzapproach forrank-based targetestimation (TARgEt) that employs the alternating direction method of multipliers. Numerical results on both synthetic and real-world datasets demonstrate the effectiveness and robustness of TARgEt over the state-of-the-art.

In this study, we develop a holistic framework for space-time adaptive processing (STAP) in connected and automated vehicle (CAV) radar systems. We investigate a CAV system consisting of multiple vehicles that transmit frequency-modulated continuous-waveforms (FMCW), thereby functioning as a multistatic radar. Direct application of STAP in a network of radar systems such as in a CAV may lead to excess interference. We exploit time division multiplexing (TDM) to perform transmitter scheduling over FMCW pulses to achieve high detection performance. The TDM design problem is formulated as a quadratic assignment problem which is tackled by power method-like iterations and applying the Hungarian algorithm for linear assignment in each iteration. Numerical experiments confirm that the optimized TDM is successful in enhancing the target detection performance.

Physical layer security is a common concern in dual-function radar communications (DFRC) because of sharing of information between different emitters. We study covert communications between a DFRC unit and multiple legitimate users, with assistance from an intelligent reflecting surface (IRS). The system has multiple targets that need to be detected, and each target is collocated with a warden trying to detect the ongoing communication. We seek to maximize the worst-case data rate across users under radar detection constraint and covertness constraint. To this end, we superpose artificial noise with our message signal so that the wardens’ received signal statistics do not change significantly if communications suddenly starts. We formulate a highly non-convex optimization problem to determine the passive beamforming scheme for the IRS and active precoding scheme at the transmitter, and solve it using a combination of auxiliary matrices, alternating optimization, and a variant of stochastic gradient descent. Finally, we validate the proposed algorithm numerically.

Recent developments in spectrum-sharing technologies include integrated sensing and communications (ISAC) systems to save resources, cost, and power. In this paper, we consider a co-existence topology with n-tuple radar and communications transmitters, wherein neither the transmitted signal nor the channels are known. Estimating these unknown quantities is modeled as a n-tuple blind deconvolution problem (NTBD). The receiver is considered to be a uniform linear antenna array. Thus, the channels are modeled as continuous-valued time delay, Doppler modulation, and direction of arrival (DoA). Also, harnessing the sparse nature of the channels and their continuousvalued parametrization, we propose a 3D n-tuple atomic norm minimization (NANM). Casting the NANM problem to its corresponding dual optimization problem, and employing the theory of positive trigonometric polynomial, we formulate a semidefinite program for the estimation of the unknown channel parameters. Performance guarantees of the proposed algorithm are provided in terms of the minimum number of samples required for exact recovery. Finally, numerical simulations validate our theoretical insights.

In this paper, we propose a method to repurpose the multi-user MIMO downlink transmission for joint wireless communication and imaging. The key idea is to introduce the concept of virtual users in the communication coverage area and use the existing MUMIMO beamforming methods to jointly beamform towards real and virtual users. The virtual users are placed to complement the locations of actual users, with the objective to illuminate the scene as uniformly as possible. We study a single-parameter tradeoff, introduced by a power split parameter between real and virtual users. We demonstrate via simulated examples that the virtual user concept is effective in providing a scalable imaging and communications performance tradeoff for cases where the real users are clustered in small geographical areas.

To accommodate extremely high data rates, provide high reliability, improve coverage, and meet traffic demands in future wireless communication networks, novel technologies have emerged that exploit electromagnetic waves, large multiple-antenna systems, intelligent reflective surfaces, hardware innovations, new network architectures, and higher frequency bands. Considering advances in information theory and devices, fundamental questions arise for system designers on how to develop synergies between theory and practice. Current design and analysis methods are predominantly based on scalar-quantity, far-field, planar-wavefront, monochromatic, and other non-physically consistent assumptions, which can lead to significant mismatches with systems designed based on realistic propagation models.

Signal processing over hypercomplex numbers arises in many optical imaging applications. In particular, spectral image or color stereo data are often processed using octonion algebra. Recently, the eight-band multispectral image phase recovery has gained salience, wherein it is desired to recover the eight bands from the phaseless measurements. In this letter, we tackle this hitherto unaddressed hypercomplex variant of the popular phase retrieval (PR) problem. We propose octonion Wirtinger flow (OWF) to recover an octonion signal from its intensity-only observation. However, contrary to the complex-valued Wirtinger flow, the non-associative nature of octonion algebra and the consequent lack of octonion derivatives make the extension to OWF non-trivial. We resolve this using the pseudo-real-matrix representation of octonion to perform the derivatives in each OWF update. We demonstrate that our approach recovers the octonion signal up to a right-octonion phase factor. Numerical experiments validate OWF-based PR with high accuracy under both noiseless and noisy measurements.

As the demand for wireless connectivity continues to soar, the fifth generation and beyond wireless networks are exploring new ways to efficiently utilize the wireless spectrum and reduce hardware costs. One such approach is the integration of sensing and communications (ISAC) paradigms to jointly access the spectrum. Recent ISAC studies have focused on upper millimeter-wave and low terahertz bands to exploit ultrawide bandwidths. At these frequencies, hybrid beamformers that employ fewer radio-frequency chains are employed to offset expensive hardware but at the cost of lower multiplexing gains. Wideband hybrid beamforming also suffers from the beam-split effect arising from the subcarrier-independent (SI) analog beamformers. To overcome these limitations, we introduce a spatial path index modulation (SPIM) ISAC architecture, which transmits additional information bits via modulating the spatial paths between the base station and communications users. We design the SPIM-ISAC beamformers by estimating both radar and communications parameters through our proposed beam-split-aware algorithms. We then develop a family of hybrid beamforming techniques – hybrid, SI, subcarrier-dependent analog-only, and beam-split-aware beamformers – for SPIM-ISAC. Numerical experiments demonstrate that the proposed approach exhibits significantly improved spectral efficiency performance in the presence of beam-split when compared with even fully digital non-SPIM beamformers.

Automotive radars at the Terahertz (THz) frequency band have the potential to be compact and lightweight while providing high (nearly-optical) angular resolution. In this paper, we propose a bistatic THz automotive radar that employs the recently proposed orthogonal chirp division multiplexing (OCDM) multi-carrier waveform. As a standalone communications waveform, OCDM has been investigated for robustness against interference in time-frequency selective channels. The THz-band path loss, and, hence, radar signal bandwidth, are range-dependent. We address this unique feature through a multicarrier wideband OCDM sensing transceiver that exploits the coherence bandwidth of the THz channel. We develop an optimal scheme to combine the returns at different ranges/bandwidths by assigning weights based on the Cramér-Rao lower bound on the range and velocity estimates. Numerical experiments demonstrate improved target estimates that can be obtained using our proposed combined estimation from measurements with varied attenuation at THz bands.

The sixth-generation networks envision the terahertz (THz) band as one of the key enabling technologies because of its ultrawide bandwidth. To combat the severe attenuation, the THz wireless systems employ large arrays, wherein the near-field beam-split (NB) severely degrades the accuracy of channel acquisition. Contrary to prior works that examine only either narrowband beamforming or far-field models, we estimate the wideband THz channel via an NB-aware orthogonal matching pursuit (NBA-OMP) approach. We design an NBA dictionary of near-field steering vectors by exploiting the corresponding angular and range deviation. Our OMP algorithm accounts for this deviation thereby ipso facto mitigating the effect of NB. Numerical experiments demonstrate the effectiveness of the proposed channel estimation technique for wideband THz systems.

Intelligent reflecting surface (IRS) technology has recently attracted a significant interest in non-light-of-sight radar remote sensing. Prior works have largely focused on designing single IRS beamformers for this problem. For the first time in the literature, this paper considers multi-IRS-aided multiple-input multiple-output (MIMO) radar and jointly designs the transmit unimodular waveforms and optimal IRS beamformers. To this end, we derive the Cramér-Rao lower bound (CRLB) of target direction-of-arrival (DoA) as a performance metric. Unimodular transmit sequences are the preferred waveforms from a hardware perspective. We show that, through suitable transformations, the joint design problem can be reformulated as two uni-modular quadratic programs (UQP). To deal with the NP-hard nature of both UQPs, we propose unimodular waveform and beamforming design for multi-IRS radar (UBeR) algorithm that takes advantage of the low-cost power method-like iterations. Numerical experiments illustrate that the MIMO waveforms and phase shifts obtained from our UBeR algorithm are effective in improving the CRLB of DoA estimation.

Retrieving a signal from its triple correlation spectrum, also called bispectrum, arises in a wide range of signal processing problems. Conventional methods do not provide an accurate inversion of bispectrum to the underlying signal. In this paper, we present an approach that uniquely recovers signals with finite spectral support (band-limited signals) from at least 3B measurements of its bispectrum function (BF), where B is the signal’s bandwidth. Our approach also extends to time-limited signals. We propose a two-step trust region algorithm that minimizes a non-convex objective function. First, we approximate the signal by a spectral algorithm and then refine the attained initialization based on a sequence of gradient iterations. Numerical experiments suggest that our proposed algorithm is able to estimate band-/time-limited signals from its BF for both complete and undersampled observations.

Remote vibrometry using wireless signals is a recently introduced novel technique with a wide range of applications such as remote microphones and structural health monitoring. These use cases require high sensitivity and coherence in the sensing system. In this context, radar is a suitable all-weather sensor compared to conventional acoustic acquisition. We investigate human vocal sound and heart sound detection and separation using a single millimeter-wave radar sensor and advanced array processing techniques to achieve superior motion sensitivity.

Rydberg-aided atomic electrometry has recently garnered significant research interest for detecting external electric fields. However, the inability of Rydberg probes to detect phase remains a serious impediment to their realistic deployment. In phased array or synthetic aperture applications, if a Rydberg atom probe is used in place of an antenna, then measurements of only electric field intensity are possible at each spatial sample. In this paper, we cast the extraction of useful information from these Rydberg probe measurements as a novel phase retrieval problem. The resulting optimization is non-convex, which we address by developing a three-stage alternating projections algorithm. Our numerical experiments demonstrate the effectiveness of the proposed algorithm in terms of the beamformed array output.

Dual-function radar-communications (DFRC) systems generally employ reconfigurable intelligent surface (RIS) as a reflector in the wireless media to enable non-line-of-sight (NLoS) sensing and communications. Different from RIS, reconfigurable holographic surface (RHS) are the surfaces with an embedded feed. These surfaces are deployed at the transceiver thereby leading to a lightweight design and greater control of the radiation amplitude. In this paper, we propose a novel frequency-selective RIS-assisted wideband DFRC system that is also equipped with a RHS at the transceiver. Our goal is to jointly design the digital, holographic, and passive beam-formers to maximize the radar signal-to-interference-plus-noise ratio (SINR) while ensuring the communication SINR among all users. The resulting nonconvex optimization problem involves maximin objective and difference of convex constraint. We develop an alternating maximization framework to decouple and iteratively solve these subproblems. Numerical experiments demonstrate that the proposed method achieves better radar performance than non-RHS, non-RIS, and randomly-configured RIS-aided DFRC systems.

A high peak-to-average power ratio (PAPR) is a major disadvantage of orthogonal frequency division multiplexing (OFDM) communications systems. In this letter, we present a graph-theoretic heuristic to mitigate high PAPR. In particular, we focus on searching for an optimal Gray-coded mapping to encode user messages such that minimum PAPR is obtained for a given message sequence in an$M$-ary quadrature amplitude modulation (QAM). We exploit the bijection between vertex-weighted lattice constellations andhypercube graphsto formulate the OFDM PAPR optimization as a computationally efficient integer linear program (ILP) through the application ofBirkhoff's theoremtodoubly stochastic matrices. Our numerical experiments show an average PAPR reduction of 9–10 dB using the hypercube-graph-based constellation map over the worst map while still within 0.5 dB of the brute-force method.

In this paper, we address two crucial challenges in the design of cell-free (CF) systems: degradation in the performance of CF systems by imperfect channel state information at the transmitter (CSIT) and high computational/signaling loads arising from the increasing number of distributed antennas and parameters to be exchanged. To mitigate the effects of imperfect CSIT, we employ rate-splitting (RS) multiple-access, which separates the messages into common and private streams. Unlike prior works, we present a clustered CF multi-user multiple-antenna framework with RS, which groups the transmit antennas in several clusters to reduce the computational and signaling loads. The proposed RS-CF system employs one common stream per cluster to exploit the network diversity. Furthermore, we propose new cluster-based linear precoders for this framework. We then devise a power allocation strategy for the common and private streams within clusters and derive closed-form expressions for the sum-rate performance of the proposed cluster-based RS-CF system. Numerical results show that the proposed clustered RS-CF system and algorithms outperform existing approaches.

Intelligent reflecting surface (IRS) is recognized as an enabler of future dual-function radar-communications (DFRC) by improving spectral efficiency, coverage, parameter estimation, and interference suppression. Prior studies on IRS-aided DFRC focus either on narrowband processing, single-IRS deployment, static targets, non-clutter scenario, or on the under-utilized line-of-sight (LoS) and non-line-of-sight (NLoS) paths. In this paper, we address the aforementioned shortcomings by optimizing a wideband DFRC system comprising multiple IRSs and a dual-function base station that jointly processes the LoS and NLoS wideband multi-carrier signals to improve both the communications SINR and the radar SINR in the presence of a moving target and clutter. We formulate the transmit, receive and IRS beamformer design as the maximization of the worst-case radar signal-to-interference-plus-noise ratio (SINR) subject to transmit power and communications SINR. We tackle this nonconvex problem under the alternating optimization framework, where the subproblems are solved by a combination of Dinkelbach algorithm, consensus alternating direction method of multipliers, and Riemannian steepest decent. Our numerical experiments show that the proposed multi-IRS-aided wideband DFRC provides over 4 dB radar SINR and 31.7% improvement in target detection over a single-IRS system.

Recent research in ultra-reliable and low latency communications (URLLC) for future wireless systems has spurred interest in short block-length codes. In this context, we analyze arbitrary harmonic bandwidth (BW) expansions for a class of high-dimension constant curvature curve codes for analog error correction of independent continuous-alphabet uniform sources. In particular, we employ the circumradius function from knot theory to prescribe insulating tubes about the centerline of constant curvature curves. We then use tube packing density within a hypersphere to optimize the curve parameters. The resulting constant curvature curve tube (C3T) codes possess the smallest possible latency, i.e., block-length is unity under BW expansion mapping. Further, the codes perform within 5 dB signal-to-distortion ratio of the optimal performance theoretically achievable at a signal-to-noise ratio (SNR)$ < -5$dB for BW expansion factor$n \leq 10$. Furthermore, we propose a neural-network-based method to decode C3T codes. We show that, at low SNR, the neural-network-based C3T decoder outperforms the maximum likelihood and minimum mean-squared error decoders for all$n$. The best possible digital codes require two to three orders of magnitude higher latency compared to C3T codes, thereby demonstrating the latter’s utility for URLLC.

In-band full-duplex (IBFD) transmission enables simultaneous transmission and reception of signals over the same frequency, thereby doubling spectral efficiency. This technique has the potential to be the backbone of next-generation wireless networks. At the same time, a continuous up-scaling of wireless network carrier frequencies arising from ever-increasing data traffic is driving research on integrated sensing and communications (ISAC) systems. This paper considers hitherto unexamined ISAC system comprising IBFD multi-user (MU) multiple-input-multiple-output (MU-MIMO) communications and a distributed MIMO radar. In particular, we propose a transceiver co-design mechanism for a joint distributed MIMO radar and an IBFD MU-MIMO communications system in the presence of a moving radar target. We leverage the relationship between the achievable rate and the weighted minimum mean squared error for the co-designed transceiver. We solve the resulting non-convex optimization using the block coordinate descent algorithm to sequentially obtain the MIMO radar waveform matrix, the downlink, and uplink precoders of the IBFD MU-MIMO, and linear receive filters subject to the power and quality-of-service constraints. Numerical experiments show that our proposed WMMSE-based method for distributed ISAC design achieves monotonic convergence within finite steps with a much-improved signal-to-noise ratio for radar and communications functions.

We consider a bistatic vehicular integrated sensing and communications (ISAC) system that employs the recently proposed orthogonal chirp division multiplexing (OCDM) multicarrier waveform. As a stand-alone communications waveform, OCDM has been shown to be robust against the interference in time-frequency selective channels. In a bistatic ISAC, we exploit this property to develop efficient receive processing algorithms that achieve high target resolution as well as high communications rate. We derive statistical bounds for our proposed Sequential symbol decoding and radar parameter estimation (SUNDAE) algorithm and compare its competitive performance with other multicarrier waveforms through numerical experiments.

With the use of common signaling methods for dual-function radar-communications (DFRC) systems, the susceptibility of eavesdropping on messages aimed at legitimate users has worsened. For DFRC systems, the radar target may act as an eavesdropper (ED) that receives a high-energy signal thereby leading to additional challenges. Unlike prior works, we consider a multicast multi-antenna DFRC system with multiple EDs. We then propose a physical layer design approach to maximize the secrecy rate by installing intelligent reflecting surfaces in the radar channels. Our optimization of multiple ED multicast multi-antenna DFRC secrecy rate (OptM3Sec) approach solves this highly nonconvex problem with respect to the precoding matrices. Our numerical experiments demonstrate the feasibility of our algorithm in maximizing the secrecy rate in this DFRC setup.

We consider a general spectral coexistence scenario, wherein the channels and transmit signals of both radar and communications systems are unknown at the receiver. In this dual-blind deconvolution (DBD) problem, a common receiver admits the multi-carrier wireless communications signal that is overlaid with the radar signal reflected-off multiple targets. When the radar receiver is not collocated with the transmitter, such as in passive or multistatic radars, the transmitted signal is also unknown apart from the target parameters. Similarly, apart from the transmitted messages, the communications channel may also be unknown in dynamic environments such as vehicular networks. As a result, the estimation of unknown target and communications parameters in a DBD scenario is highly challenging. In this work, we exploit the sparsity of the channel to solve DBD by casting it as an atomic norm minimization problem. Our theoretical analyses and numerical experiments demonstrate perfect recovery of continuous-valued range-time and Doppler velocities of multiple targets as well as delay-Doppler communications channel parameters using uniformly-spaced time samples in the dual-blind receiver.

We present a precoder design algorithm for an in-band full-duplex (IBFD) multi-user multiple-input-multiple-output (MU-MIMO) communications system concurrently operating within the same frequency band with a widely-distributed MIMO radar (WDMR). Prior works on joint radar communications (JRC) problems primarily focus on colocated MIMO radars and half-duplex/single-user MIMO communications. We maximize the weighted sum rate (WSR) of the joint radar-communications system subjected to UL/DL power budgets and quality of service. We solve the WSR maximization problem as a weighted minimum mean-squared-error (WMMSE) minimization problem by exploiting the relationship between achievable rate and MSE. A block coordinate descent (BCD) algorithm is resorted to finding the precoders for all UEs sequentially. Our numerical experiments demonstrate that the proposed precoder design scheme outperforms conventional precoding strategies in the presence of a WDMR.

We investigate the theory of affine groups in the context of designing radar waveforms that obey the desired wideband ambiguity function (WAF). The WAF is obtained by correlating the signal with its time-dilated, Doppler-shifted, and delayed replicas. We consider the WAF definition as a coefficient function of the unitary representation of the group a • x + b. This is essentially an algebraic problem applied to the radar waveform design. Prior works on this subject largely analyzed narrow-band ambiguity functions. Here, we show that when the underlying wideband signal of interest is a pulse or pulse train, a tight frame can be built to design that waveform. Specifically, we design the radar signals by minimizing the ratio of bounding constants of the frame in order to obtain lower sidelobes in the WAF. This minimization is performed by building a codebook based on difference sets in order to achieve the Welch bound. We show that the tight frame so obtained is connected with the wavelet transform that defines the WAF.

Optimal allocation of shared resources is key to deliver the promise of jointly operating radar and communications systems. In this paper, unlike prior works which examine synergistic access to resources in colocated joint radar-communications or among identical systems, we investigate this problem for a distributed system comprising heterogeneous radars and multi-tier communications. In particular, we focus on resource allocation in the context of multi-target tracking (MTT) while maintaining stable communications connections. By simultaneously allocating the available power, dwell time and shared bandwidth, we improve the MTT performance under a Bayesian tracking framework and guarantee the communications throughput. Our${a}$lter${n}$ating allo${c}$ation of${h}$eterogene${o}$us${r}$esources (ANCHOR) approach solves the resulting non-convex problem based on the alternating optimization method that monotonically improves the Bayesian Cramér-Rao bound. Numerical experiments demonstrate that ANCHOR significantly improves the tracking error over two baseline allocations and stability under different target scenarios and radar-communications network distributions.

Wireless sensor networks (WSNs) are vulnerable to eavesdropping as the sensor nodes (SNs) communicate over an open radio channel. Intelligent reflecting surface (IRS) technology can be leveraged for physical layer security in WSNs. In this letter, we propose a joint transmit and reflective beamformer (JTRB) design for secure parameter estimation at the fusion center (FC) in the presence of an eavesdropper (ED) in a WSN. We develop a semidefinite relaxation (SDR)-based iterative algorithm, which alternately yields the transmit beamformer at each SN and the corresponding reflection phases at the IRS, to achieve the minimum mean-squared error (MSE) parameter estimate at the FC, subject to transmit power and ED signal-to-noise ratio constraints. Our simulation results demonstrate robust MSE and security performance of the proposed IRS-based JTRB technique.

Intelligent reflecting surface (IRS) is a rapidly emerging paradigm to enable non-line-of-sight (NLoS) wireless transmission. In this paper, we focus on IRS-aided radar estimation performance of a moving hidden or NLoS target. Unlike prior works that employ a single IRS, we investigate this problem using multiple IRS platforms and assess the estimation performance by deriving the associated Cramér-Rao lower bound (CRLB). We then design Doppler-aware IRS phase shifts by minimizing the scalar A-optimality measure of the joint parameter CRLB matrix. The resulting optimization problem is non-convex, and is thus tackled via an alternating optimization framework. Numerical results demonstrate that the deployment of multiple IRS platforms with our proposed optimized phase shifts leads to a higher estimation accuracy compared to non-IRS and single-IRS alternatives.

This paper shows the enhancement in detection performance in an automotive scenario by leveraging the backscattered communication signals from vehicles at the target scene. A sensor fusion algorithm is proposed to benefit from the information from radar and communication to improve the final range estimates. We demonstrate theoretically and illustrate through simulation that our proposed scheme enhances the radar detection performance. Thus the proposed scheme offers a solution for augmenting existing sensing capabilities to enhance detecting capabilities in a dynamic automotive scenario.

Millimeter wave multiple-input multiple-output (mmWave-MIMO) systems with small number of radio-frequency (RF) chains have limited multiplexing gain. Spatial path index modulation (SPIM) is helpful in improving this gain by utilizing additional signal bits modulated by the indices of spatial paths. In this paper, we introduce model-based and model-free frameworks for beamformer design in multi-user SPIM-MIMO systems. We first design the beamformers via model-based manifold optimization algorithm. Then, we leverage federated learning (FL) with dropout learning (DL) to train a learning model on the local dataset of users, who estimate the beamformers by feeding the model with their channel data. The DL randomly selects different set of model parameters during training, thereby further reducing the transmission overhead compared to conventional FL. Numerical experiments show that the proposed framework exhibits higher spectral efficiency than the state-of-the-art SPIM-MIMO methods and mmWave-MIMO, which relies on the strongest propagation path. Furthermore, the proposed FL approach provides at least 10 times lower transmission overhead than the centralized learning techniques.

Higher spectral and energy efficiencies are the envisioned defining characteristics of next-generation high data-rate sixth-generation (6G) wireless networks. One of the enabling technologies to meet these requirements is index modulation (IM), which transmits information through permutations of indices of spatial, frequency, or temporal media. In this paper, we propose novel electromagnetics-compliant designs of reconfigurable intelligent surface (RIS) apertures for realizing IM in 6G transceivers. We consider RIS modeling and implementation of spatial and subcarrier IMs, including beam steering, spatial multiplexing, and phase modulation capabilities. Numerical experiments for our proposed implementations show that the bit-error-rates obtained via RIS-aided IM outperform traditional implementations. We further establish the programmable ability of these transceivers to vary the reflection phase and generate frequency harmonics for IM through full-wave electromagnetic analyses of a specific reflect-array metasurface implementation.

This paper presents a uniqueness result which states that a band- limited signal can be recovered from at least 3B measurements where B is the bandwidth from the radar ambiguity function (AF). This function is a two-dimensional mapping of the propagation delay and Doppler frequency. This formal model represents the distortion of a returned pulse due to the receiver matched filter. To estimate a time/band-limited signal from its radar AF, a trust region algorithm that minimizes a smoothed non-convex least-squares objective function is proposed. The method consists of two steps. First, we approximate the signal by an iterative spectral algorithm. Then, the attained initialization is refined based upon a sequence of gradient iterations. To the best of our knowledge this work is seminal in the sense of solving the radar phase retrieval problem for band-limited signals. Simulations results suggest that the proposed algorithm is able to estimate band-limited signals from its radar AF for both complete and incomplete radar cases. The AF is incomplete when only few shifts are considered. Numerical results show that the proposed algorithm estimates the signal with mean-square-error of 5 × 10-2for both complete and incomplete noisy cases.

We consider the recovery of a complex band-limited radar waveform from the magnitude of the fractional Fourier transform (FrFT) formulation of its ambiguity function (AF). This is essentially a phase retrieval (PR) problem applied to radar waveform design. The FrFT-based AF is mathematically obtained by correlating the signal with its frequency-rotated, Doppler-shifted, and delayed replicas. It completely characterizes the radar's capability to discriminate closely-spaced targets in the delay-Doppler plane. Unlike prior works which largely involved analytical approaches, our method WaveMax formulates the recovery of the waveform via the FrFT-based AF PR as a convex optimization problem. Specifically, we retrieve the signal by solving a basis pursuit that requires a designed approximation of the radar signal obtained by extracting the leading eigenvector of a matrix depending on the AF. Our theoretical analysis shows that unique waveform reconstruction is possible using signal samples no more than thrice the number of signal frequencies or time samples. Numerical experiments demonstrate that our method recovers band-limited signals from both even-sparse and random samples of the AFs with a mean squared error of$1\times 10^{-6}$and$5\times 10^{-2}$for full noiseless samples and sparse noisy samples, respectively.

Efficient second-order probabilistic inference in uncertain Bayesian networks was recently introduced. However, such second -order inference methods presume training over complete training data. While the expectation-maximization framework is well-established for learning Bayesian network parameters for incomplete training data, the framework does not determine the covariance of the parameters. This paper introduces two methods to compute the covariances for the parameters of Bayesian networks or Markov random fields due to incomplete data for two-node networks. The first method computes the covariances directly from the posterior distribution of parameters, and the second method more efficiently estimates the covariances from the Fisher information matrix. Finally, the implications and effectiveness of these covariances is theoretically and empirically evaluated.

Single waveform design for automotive joint radar-communications (JRC) is being increasingly considered recently, as it addresses the problem of spectrum sharing between the two systems. The paper addresses the challenge of designing a waveform in MIMO-radar MIMO-communications (MRMC) set-up in a broadcast environment to ensure certain performance of the two systems is guaranteed. It develops an optimization problem to enhance mutual information metrics for radar and communications considering the worst case Doppler/angle at a range bin. The intractable optimization problem is decomposed and relaxed into two convex sub-problems, which are subsequently solved through an iterative method. The benefits of the proposed waveform are illustrated through numerical simulations.

We investigate waveform design for coexistence between a multiple-input multiple-output (MIMO) radar and MIMO communications (MRMC), with a radar-centric criterion that leads to a minimal interference in the communications system. The communications use the traditional mode of operation in Long Term Evolution (LTE)/Advanced (FDD), where we formulate the design problem based on information-theoretic criterion with the discrete phase constraint at the design stage. The optimization problem, is non-convex, multi-objective and multi-variable, where we propose an efficient algorithm based on the coordinate descent (CD) framework to simultaneously improve radar target detection performance and the communications rate. The numerical results indicate the effectiveness of the proposed algorithm in designing discrete phase set of sequences, potentially binary.

While the use of weather radars to continuously monitor the spatiotemporal dynamics of precipitation has grown in recent years, these systems are expensive and sparsely deployed across the world. In this context, densely located ground-based terminals for interactive satellite services have the potential for dual-use as weather sensors because they measure rain-attenuated power of the downlink signal. Although in the millimeter-wave regime, the rain rate has almost a linear relationship with specific attenuation, lack of other weather radar observables at satellite terminals imposes a daunting task of extracting rainfall rate from these highly attenuated signals. We address this problem by designing a deep convolutional neural network (CNN) that learns the relationship between the signal attenuation and rainfall rate observed by weather radars and rain gauges at a given location. During the prediction stage, the CNN accepts downlink attenuation as input and classifies the rain intensity which is then used to apply an appropriate rainfall estimator. Our experiments with real data show that, despite severe attenuation, CNN-based downlink rainfall accumulations closely follow the nearest C-band German weather service Deutscher Wetterdienst (DWD) radar.

In automotive radar imaging, displaced sensors offer improvement in localization accuracy by jointly processing the data acquired from multiple radar units, each of which may have limited individual resources. In this paper, we derive performance bounds on the estimation error of target parameters processed by displaced sensors that correspond to several independent radars mounted at different locations on the same vehicle. Unlike previous studies, we do not assume a very accurate time synchronization among the sensors. Instead, we consider only frame-level time alignment which is more common and practical in modern automotive sensor networks. We first develop a displaced multiple-input multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radar signal model under coarse synchronization and then propose processing models relevant to modern automotive radars such as point-cloud-based fusion and raw signal imaging. Contrary to earlier works based on deterministic Cramér-Rao lower bound, our displaced sensors framework is Bayesian. Numerical experiments with our proposed non-coherent processing of displaced MIMO FMCW radars show an order of performance improvement in position estimation over the conventional point-cloud fusion.

In millimeter-wave communications, multiple-input-multiple-output (MIMO) systems use large antenna arrays to achieve high gain and spectral efficiency. These massive MIMO systems employ hybrid beamformers to reduce power consumption associated with fully digital beamforming in large arrays. Further savings in cost and power are possible through the use of subarrays. Unlike prior works that resort to large latency methods such as optimization and greedy search for subarray selection, we propose a deep-learning-based approach in order to overcome the complexity issue without causing significant performance loss. We formulate antenna selection and hybrid beamformer design as a classification/prediction problem for convolutional neural networks (CNNs). For antenna selection, the CNN accepts the channel matrix as input and outputs a subarray with optimal spectral efficiency. The resultant subarray channel matrix is then again fed to a CNN to obtain analog and baseband beamformers. We train the CNNs with several noisy channel matrices that have different channel statistics in order to achieve a robust performance at the network output. Numerical experiments show that our CNN framework provides an order better spectral efficiency and is 10 times faster than the conventional techniques. Further investigations with quantized-CNNs show that the proposed network, saved in no more than 5 bits, is also suited for digital mobile devices.

We propose tensor-based four-dimensional sub-Nyquist radar that samples in spectral, spatial, Doppler, and temporal domains at sub-Nyquist rates while simultaneously recovering the target's direction, Doppler velocity, and range without loss of native resolutions. We formulate the radar signal model wherein the received echo samples are represented by a partial third-order tensor. We then apply compressed sensing in the tensor domain and use our tensor-orthogonal matching pursuit (OMP) and tensor completion algorithms for signal recovery. Our numerical experiments demonstrate joint estimation of all three target parameters at the same native resolutions as a conventional radar but with reduced measurements. Furthermore, tensor completion methods show enhanced performance in off-grid target recovery with respect to tensor-OMP.

Ground-penetrating radar (GPR) target detection and classification is a challenging task. Here, we consider online dictionary learning (DL) methods to obtain sparse representations (SR) of the GPR data to enhance feature extraction for target classification via support vector machines. Online methods are preferred because traditional batch DL like K-times singular value decomposition (K-SVD) is not scalable to high-dimensional training sets and infeasible for real-time operation. We also develop Drop-Off MINi-batch Online Dictionary Learning (DOMINODL), which exploits the fact that a lot of the training data may be correlated. The DOMINODL algorithm iteratively considers elements of the training set in small batches and drops off samples which become less relevant. For the case of abandoned anti-personnel landmines classification, we compare the performance of K-SVD with three online algorithms: classical online dictionary learning (ODL), its correlation-based variant, and DOMINODL. Our experiments with real data from L-band GPR show that online DL methods reduce learning time by 36%-93% and increase mine detection by 4%-28% over K-SVD. Our DOMINODL is the fastest and retains similar classification performance as the other two online DL approaches. We use a Kolmogorov-Smirnoff test distance and the Dvoretzky-Kiefer-Wolfowitz inequality for the selection of DL input parameters leading to enhanced classification results. To further compare with the state-of-the-art classification approaches, we evaluate a convolutional neural network (CNN) classifier, which performs worse than the proposed approach. Moreover, when the acquired samples are randomly reduced by 25%, 50%, and 75%, sparse decomposition-based classification with DL remains robust while the CNN accuracy is drastically compromised.

Information geometry describes a framework where probability densities can be viewed as differential geometry structures. This approach has shown that the geometry in the space of probability distributions that are parameterized by their covariance matrices is linked to the fundamental concepts of estimation theory. In particular, prior work proposes a Riemannian metric - the distance between the parameterized probability distributions - that is equivalent to the Fisher Information Matrix, and helpful in obtaining the deterministic Cramér-Rao lower bound (CRLB). Recent work in this framework has led to establishing links with several practical applications. However, classical CRLB is useful only for unbiased estimators and inaccurately predicts the mean square error in low signal-to-noise (SNR) scenarios. In this paper, we propose a general Riemannian metric that, at once, is used to obtain both Bayesian CRLB and deterministic CRLB along with their vector parameter extensions. We also extend our results to the Barankin bound, thereby enhancing their applicability to low SNR situations.

We present a composite suite of technologies for spectral coexistence of existing communication and radar systems using the Xampling framework. For a stand-alone communication system, we consider a cognitive radio (CRo) that receives multiband signals with unknown carrier frequencies and directions of arrival, and demonstrate joint spectrum sensing via CompreSsed CArrier and Direction-ofarrival Estimation (CaSCADE) with an L-shaped configuration of two uniform linear arrays. For radars operating in bands with widespread spectral interference, we present an X-band prototype of cognitive sub-Nyquist multiple input multiple output (MIMO) radar (SUMMeR). The prototype allows sampling in both spatial and spectral domains at sub-Nyquist rates and cognitively transmits over multiple narrow subbands. Finally, we demonstrate Spectral Coexistence via Xampling (SpeCX) technology that shows joint operation of both - cognitive radio and cognitive monostatic radar - over a common spectrum. Our solutions to individual and joint operation of communication and radar systems supersede existing spectrum sharing technologies that require a compromise over performance of one of the systems.

A cognitive radar adapts the transmit waveform in response to changes in the radar and target environment. In this work, we analyze the recently proposed sub-Nyquist cognitive radar wherein the total transmit power in a multi-band cognitive waveform remains the same as its full-band conventional counterpart. For such a system, we derive lower bounds on the mean-squared-error (MSE) of a single-target time delay estimate. We formulate a procedure to select the optimal bands, and recommend distribution of the total power in different bands to enhance the accuracy of delay estimation. In particular, using Cramér-Rao bounds, we show that equi-width subbands in cognitive radar always have better delay estimation than the conventional radar. Further analysis using Ziv-Zakai bound reveals that cognitive radar performs well in low signal-to-noise (SNR) regions.

Sparse decomposition of ground penetration radar (GPR) signals facilitates the use of compressed sensing techniques for faster data acquisition and enhanced feature extraction for target classification. In this paper, we investigate use of an online dictionary learning (ODL) technique in the context of GPR to bring down the learning time as well as improve identification of abandoned anti-personnel landmines. Our experimental results using real data from an L-band GPR for PMN/PMA2, ERA and T72 mines show that ODL reduces learning time by 94% and increases clutter detection by 10% over the classical K-SVD algorithm. Moreover, our methods could be helpful in cognitive operation of the GPR where the system adapts the range sampling based on the learned dictionary.

Automated driving has become increasingly viable through the deployment of a number of sensing technologies on vehicles. These intelligent transportation systems (ITS) employ sensors such as radar, camera and lidars for collision avoidance and vehicle-to-everything (V2X) communication links for active environment sensing. Since the available spectrum for vehicular systems is limited, spectrum sharing in ITS sensors is a subject of active investigation. This paper presents a spectrum sharing technology that enables interference-free operation of an automotive radar and a V2X communication system within a common spectrum. Both systems dynamically share information with each other and optimize their spectral resources to the changing RF environment. The V2X system is a cognitive radio that is capable of blind sensing its spectrum using very low sampling and processing rates. The radar system is also modeled as a cognitive system that employs a Xampling-based sub-Nyquist receiver and transmits in several narrow bands that occupy a fraction of the conventional radar bandwidth. We present a hardware realization of this vehicular spectrum sharing technology and demonstrate spectral coexistence through real-time experiments.

We propose novel algorithms that enhance the performance of recovering unknown continuous-valued frequencies from undersampled signals. Our iterative reweighted frequency recovery algorithms employ the support knowledge gained from earlier steps of our algorithms as block prior information to enhance frequency recovery. Our methods improve the performance of the atomic norm minimization which is a useful heuristic in recovering continuous-valued frequency contents. Numerical results demonstrate that our block iterative reweighted methods provide both better recovery performance and faster speed than other known methods.

Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. In this paper, we extend off-the-grid CS to applications where some prior information about spectrally sparse signal is known. We specifically consider cases where a few contributing frequencies or poles, but not their amplitudes or phases, are known a priori. Our results show that equipping off-the-grid CS with the known-poles algorithm can increase the probability of recovering all the frequency components.

We propose an innovative meteorological radar, which uses reduced number of spatiotemporal samples without compromising the accuracy of target information. Our approach extends recent research on compressed sensing (CS) for radar remote sensing of hard point scatterers to volumetric targets. The previously published CS-based radar techniques are not applicable for sampling weather since the precipitation echoes lack sparsity in both range-time and Doppler domains. We propose an alternative approach by adopting the latest advances in matrix completion algorithms to demonstrate the sparse sensing of weather echoes. We use Iowa X-band Polarimetric (XPOL) radar data to test and illustrate our algorithms.

The sun is a convenient and frequently employed external radiation source for calibrating weather radar antenna and receiver characteristics. However, changes in solar activity can be a major source of error in interpreting the results of solar calibration for lower frequency bands. The cross-correlation of horizontal and vertical polarization signals, which is zero for perfectly unpolarized electromagnetic radiation, could give non-zero estimates if a quiet sun is not observed by the radar. In this paper, solar scan measurements are made at X-band to detect the effect of solar activity on this cross-correlation coefficient. To facilitate mitigation of instrument-wide errors, we employ multiple XPOL radars to simultaneously observe the sun. Though our experiments during a limited period show that the cross-channel correlation estimates obtained by X-band weather radars remain relatively unaffected by the variations in solar flux, the paper makes suggestions on improving the results.

Dual wavelength precipitation radar (DPR) is planned to be deployed in the GPM core satellite. The DPR is expected to provide improved characterization of the raindrop size distribution ( DSD), as well as rainfall rate estimation from a combination of Ku band and Ka band radar measurement [1]. The Ku band radar is nearly same as the TRMM Precipitation radar. The Ka band provides higher sensitivity and can be useful in the measurement of snow and light rain. In contrast to TRMM the dual wavelength retrieval methods will use two DSD parameters to characterize the precipitation medium. The underlying precipitation structures, hydrometeors and DSDs dictate the type of models or retrieval algorithms that can be used to estimate precipitation. Having dual wavelength radar on the ground, with the potential for in-situ observations, or coordinated observations provide excellent opportunity to develop microphysical and system models for retrievals. Therefore a beam aligned dual-wavelength system consisting of Ku and Ka bands can be very useful as a ground validation tool. In addition if these systems can be dual-polarized, then these can be self-consistent cross validation tools. This paper describes the NASA Dual polarized, dual frequency Doppler radar, developed for the ground validation program.

NASA Dual-Frequency Dual-Polarized Doppler Radar (D3R) is a ground-based system developed to enable ground validation for the Global Precipitation Measurement (GPM) Mission. The radar makes measurements at both Ku- and Ka-band frequencies in order to gain higher sensitivity towards light rain, drizzle and snow. D3R capitalizes on a solid-state transceiver which can considerably enhance the sensitivity of the radar by allowing implementation of pulse compression waveforms. However, the use of pulse compression techniques is accompanied by challenges to mitigate the blind zone, suppress range side-lobes and unavailability of wider bandwidth. D3R therefore employs a programmable wideband multi-channel digital receiver which implements a novel waveform to check the undesired consequences of pulse compression and meet unique requirements of D3R system. This paper discusses various considerations and challenges for design and realization of system requirements for the D3R system by employing several novel radar signal processing algorithms.

This paper summarizes part of the work performed on the calibration and characterization of the Dual-frequency, Dual-polarized, Doppler Radar (D3R) system. The D3R makes use of pulse compression combined with a three pulse frequency separated waveform to achieve it's sensitivity while still maintaining a blind range similar to that of a single pulse weather radar. A slightly modified receiver calibration procedure to the one used on conventional weather radar was required to accommodate the use of multiple sub-channels. The use of the calibration loop in both transceivers is described and transmitter output power is shown for a twenty four hour period. Antenna beam (Ku to Ka) co-alignment verification using solar scans is also presented followed by sphere calibration results for the Ku-band radar. Finally, Ku-band reflectivity plots for two events during May 6th, 2012 and May 7th, 2012 are presented.

As an integral part of Global Precipitation Measurement (GPM) mission, Ground Validation (GV) program proposes to establish an independent global cross-validation process to characterize errors and quantify uncertainties in the precipitation measurements of the GPM program. A ground-based Dual-Frequency Dual-Polarized Doppler Radar (D3R) that will provide measurements at the two broadly separated frequencies (Ku- and Ka-band) is currently being developed to enable GPM ground validation, enhance understanding of the microphysical interpretation of precipitation and facilitate improvement of retrieval algorithms. The first generation D3R design will comprise of two separate co-aligned single-frequency antenna units mounted on a common pedestal with dual-frequency dual-polarized solid-state transmitter. This paper describes the salient features of this radar, the system concept and its engineering design challenges.

Lately, the continuing expansion of wind energy industry has led to the installation of several wind farms which are often in the vicinity of the weather radars. This is a source of growing concern for the weather radar community since wind turbines interfere with the normal operation of the weather radars. The wind turbine tower can drive the receivers into saturation and the Doppler shift from the moving blades can introduce errors in the estimation of wind speed, reflectivity and rainfall rates. The radar cross-section of the wind turbines has a large temporal and spatial variation which poses additional difficulties for traditional clutter filtering algorithms. This paper presents a first-order theoretical model of the radar signature of a wind turbine that can be helpful in deducing its unique features to be incorporated in filtering out the wind turbine clutter. A comparison with the observations from an S-band radar is made later in the paper.

This paper describes some of the novel technologies adopted in the realization of the NASA Dual-frequency Dual-polarized Doppler Radar (D3R) system for to be used by the GPM ground validation program. A description of the transceivers and major trades that lead to a solid-state architecture is presented. Other aspects enabling the design such as the waveform design and generation and the digital receiver is also described. Data measured from a similar power amplifier was used to estimate the expected range side lobe performance. An estimate of the expected sensitivity based on the transceiver parameters also presented.

Adequate sensitivity of weather radars using low-powered solid-state transmitter is achieved by using pulse compression waveforms. However, pulse compression waveforms have drawbacks of blind zone and range side lobes. In this paper, we present a methodology to address the major challenges in designing the waveforms for an X-band dual polarization Doppler radar operating with a solid-state transmitter. Here, frequency diversity wideband waveforms are proposed to mitigate low sensitivity of solid-state transmitters and the range eclipsing problem associated with pulse compression. An analysis of the performance of pulse compression using mismatched compression filters is reported. The performance of the proposed system is also quantified using signal and system simulations.