The time-modulated array is a simple array architecture in which each antenna is connected to an RF switch that serves as a modulator. The phase shift is achieved by digitally controlling the relative delay between the periodic modulating sequences of the antennas. Two factors limit the practical use of this architecture for communication and sensing. First, the switching frequency is high, as it must be a multiple of the sampling frequency. Second, the discrete modulating sequence introduces undesired harmonic replicas of the signal, which are out-of-band interference. This paper proposes the OFDM modulation with an appropriate precoder to facilitate the aliasing of the harmonic components to simultaneously reduce sideband radiation and switching frequency. The transmit signal has a repeated block structure in the frequency domain to facilitate coherent combining of the aliased signal blocks. As a result, a factor $A$ reduction in switching frequency is achieved at the cost of a factor $A$ reduction in communication capacity. Doubling $A$ reduces sideband radiation by around 2.9 dB. The feasibility of the proposed method is experimentally validated for wideband signals. Full-wave simulations are performed to validate the beamforming performance based on the experimental results.

Single carrier (SC) modulation is a promising waveform for sub-THz communications due to its low peak-to-average power ratio (PAPR). However, in multi-antenna transmitters, multi-user MIMO precoding significantly increases the PAPR of SC signals. This effect can be mitigated through dedicated PAPR reduction algorithms, yet the associated costs are often overlooked. In this work, we analyze the power consumption of a recently proposed peak reduction signal (PRS)-based PAPR reduction technique, in which the PRS is transmitted alongside the precoded information symbols. Existing studies primarily focus on designing the PRS to maximize PAPR reduction, without assessing whether the benefits justify the associated cost. We address this gap by quantifying both the power savings achieved through the PAPR reduction and the additional power required to design and transmit the PRS. This allows us to evaluate the net power savings. Our analysis reveals that net power savings are obtained only for specific PRS design configurations.

This work investigates the spatially wideband (SWB) antenna array factor (AF) of uniform linear arrays. First, the SWB AF approximation is derived for narrowband (NB) signals and a large aperture with element spacing satisfying the Nyquist criterion. The derivation accounts for different spatial and spectral windows. Next, an approximation of the SWB AF for a wideband (WB) signal is developed under uniform spatial and spectral weighting. The analysis shows that for fully populated arrays, increasing the bandwidth effectively suppresses the AF sidelobes. Finally, a universal SWB AF approximation is introduced, which is based on recognizing the SWB AF as a spatially variant convolution. In this formulation, the SWB AF is expressed as a convolution of the spatially narrowband (SNB) AF and the SWB kernel, providing insight into how the bandwidth and the spectral weighting affect the resulting SWB AF. The proposed approximation is shown to be accurate for a wide range of bandwidths and element spacings, including sparse arrays. In particular, for sparse arrays, the bandwidth enables suppression of grating-lobe amplitudes by spreading their energy over a wider angular range. An approximation of the grating lobe envelope as a function of the bandwidth-aperture product is provided.

Radars improve the sensing robustness of UAVs by operating under poor lighting and weather conditions and seeing through occlusions such as vegetation. However, they suffer from poor angular resolution, which can be addressed using synthetic aperture radar (SAR) algorithms. State-of-the-art UAV SAR methods operate at a depression angle and are not suitable for sensor fusion applications where the data are collected from areas directly below the UAV (i.e., the UAV nadir). In this paper, we present an interferometric SAR (InSAR) framework for reconstructing 3D images from the UAV nadir using a low-cost multi-input-multi-output (MIMO) mm-wave radar. Additionally, an effective method based on the phase gradient autofocus (PGA) is presented for compensating the phase error across the virtual receive antennas. We demonstrate the effectiveness of our 3D imaging algorithm in both simulation and experimental scenarios.

Single carrier (SC) modulation is a promising candidate for sub-THz (90-300 GHz) communication due to its relaxed hardware requirements. However, MIMO precoding increases the peak-to-average power ratio (PAPR) in SC systems. Recent works have addressed this challenge by introducing a PAPR reduction signal (PRS), which is transmitted alongside the precoded information symbols. In existing works, the PRS is designed to minimize the PAPR by solving a constrained optimization problem. While this approach effectively reduces PAPR, current methods to solve the optimization problem suffer from high computational complexity. In this letter, we propose a novel low complexity algorithm for designing the PRS. We adopt a projected gradient descent method that converges toward the optimal solution while maintaining significantly lower computational complexity. Simulation results demonstrate that our algorithm achieves a 33-fold reduction in computation time compared to existing methods.

The single carrier modulation is a potential candidate at the sub-THz band, because of the channel sparsity and the necessity for an analog front-end friendly modulation. However, the peak-to-average-power ratio (PAPR) of the single carrier modulation in a multi-user MIMO downlink scenario is still a concern. In this work, we address this problem, by transmitting a PAPR reduction signal (PRS) together with the precoded data symbols. The goal of the PRS is to reduce the PAPR of the continuous-time signal entering the power amplifier attached to each antenna. We propose two different methods to design the PRS. In the first method, we restrict the PRS to the channel null space, so that it does not cause interference with the users’ information symbols at the receiver. As such, this method has limited degrees of freedom for the PAPR reduction. Considering this, we propose a second method for the PRS design, where the PRS can interfere with the users’ information symbols in a constrained manner. This relaxation enables an increased PAPR reduction without reducing the data detection performance. The design of the PRS is formulated as an optimization problem and the performance of the proposed schemes is studied semi-analytically. Our analysis shows that the proposed scheme is able to reduce the PAPR by about 9 dB at the cost of only 0.5 dB SINR. Consequently, the bit-error-rate performance of the proposed scheme is negligibly impacted by non-linear power amplifier distortion.

This paper compares the sensing performance of a narrowband near-field system across several practical antenna array geometries and SIMO/MISO and MIMO configurations. For identical transmit and receive apertures, MIMO processing is equivalent to squaring the near-field array factor, resulting in improved beamdepth and sidelobe level. Analytical derivations, supported by simulations, show that the MIMO processing improves the maximum near-field sensing range and resolution by approximately a factor of 1.4 compared to a single-aperture system. Using a quadratic approximation of the mainlobe of the array factor, an analytical improvement factor of √(2) is derived, validating the numerical results. Finally, MIMO is shown to improve the poor sidelobe performance observed in the near-field by a factor of two, due to squaring of the array factor.

This paper presents key performance metrics for near-field communication and sensing systems with a focus on their scaling behavior as a function of the antenna array aperture. Analytical expressions are derived for several standard array geometries to enable the design of the large antenna arrays for given system requirements. First, the near-field beam focusing is analyzed and the minimum beamdepth is observed to rapidly saturate to a low asymptotic limit as the array aperture increases. In contrast, the near-field region span is shown to scale quadratically with the array aperture. Based on these two metrics, the maximum number of resolvable beamspots at 3 dB separation is derived analytically, exhibiting a linear dependence on the array aperture. Finally, the number of significant singular values of a channel observed at the array's broadside is estimated, showing a power-law dependence on the aperture. The resulting expressions provide practical design guidelines for evaluating aperture requirements in near-field communication and sensing applications.

This article investigates the range ambiguity function of near-field (NF) systems where bandwidth and NF beamfocusing jointly determine the resolution. First, the general matched filter ambiguity function is derived and the NF array factors of different antenna array geometries are introduced. Next, the NF ambiguity function is approximated as a product of the range-dependent NF array factor and the ambiguity function due to the utilized waveform and bandwidth. An approximation criterion based on the aperture-bandwidth product is formulated, and its accuracy is examined. Finally, the improvements to the ambiguity function offered by the NF beamfocusing, as compared to the far-field case, are presented. The performance gains are evaluated in terms of resolution improvement offered by beamfocusing, peak-to-sidelobe, and integrated-sidelobe-level improvement for a few popular array geometries. The gains offered by the NF regime are shown to be range-dependent and substantial only in close proximity to the array.

The time-domain signal in orthogonal frequency division multiplexing (OFDM) has a large peak-to-average power ratio (PAPR). Most existing solutions for this problem are designed for OFDM systems operating with a fully-digital MIMO architecture. However, these solutions cannot be used for an OFDM system operating with a hybrid beamforming architecture. Therefore, in this work we address PAPR reduction in OFDM with hybrid beamforming. We propose to transmit a PAPR reduction signal along with the precoded information signal. For an efficient design of the PAPR reduction signal, we propose to use extra radio frequency (RF) chains. The design of the PAPR reduction signal and the analog beamformers corresponding to the extra RF chains, is written as a constrained optimization problem. An iterative algorithm based on successive convex approximation is proposed to solve the optimization problem. We show that the proposed technique achieves a considerable end-to-end performance gain in the presence of a non-linear power amplifier.

We present and analyze a method for reducing the computational complexity of the Back-Projection image reconstruction algorithm in the framework of forward-looking synthetic aperture radar. The proposed approach leverages a radar network and a processing architecture combining decimated Back-Projection and the Sequential Spatial Masking algorithm. Simulation results show that the proposed approach enables extreme slow-time decimation, which significantly reduces the complexity without sacrificing the field of view.

Radars operating in the mm-wave frequency range offer high-range resolution, making them suitable for various applications. However, limited angular resolution is available by the commercially-out-of-shelf multiple-input-multiple-output (MIMO) radars. For cases with stationary radar, Inverse synthetic aperture radar (ISAR) can be used to improve the angular resolution. This paper presents explicit formulations for ISAR imaging with MIMO frequency-modulated continuous wave (FMCW) radars. We demonstrate that integrating an appropriate tracking algorithm with an antenna array significantly improves imaging quality. The proposed ISAR imaging pipeline is validated through an experimental setup involving radar imaging of an individual at distances ranging from 6 to 11 meters.

The radiative near-field and integration of sensing capabilities are seen as two key components of the next generation of wireless communication systems. In this paper, the sensing performance of a narrowband near-field system is investigated for several practical antenna array geometries and configurations, namely SIMO/MISO and MIMO. In the SIMO/MISO configuration, the antenna aperture is exploited only a single time for either transmit or receive signal processing, while the MIMO configuration exploits both TX and RX processing. Analytical derivations, supported by simulations, show that the MIMO processing improves the maximum near-field range and sensing resolution by approximately a factor of 1.4 as compared to single-aperture systems. The value of the improvement factor is consistent for all considered array geometries. Finally, using a quadratic approximation of the array factor, an analytical improvement factor of $\sqrt{2}$ is derived, clarifying the observed improvements and validating the numerical results.

Two sparse multiple-input multiple-output (MIMO) radar configurations are compared against a conventional dense MIMO radar to investigate low-complexity, super-resolution direction-of-arrival (DOA) estimation from a single snapshot. Both sparse configurations divide the virtual array (VA) into two uniformly spaced subarrays with a coprime relationship between them. The first is the well-known coprime array in which the inter-element spacings of the two subarrays are coprimes. The second is the VEXPA configuration, where both subarrays share the same inter-element spacing and a coprime parameter defines the relative shift between them. The analysis highlights the spatial resolution gains of sparse configurations over dense arrays while revealing limitations from the de-aliasing mechanism and reduced degrees of freedom (DOFs) inherent to the subarray partitioning.

This paper presents an efficient pipeline for estimating the altitude of an unmanned aerial vehicle (UAV) using a commercial off-the-shelf mm-wave radar. The proposed method leverages a synthetic aperture radar (SAR) algorithm to generate high-resolution depth maps (DMs) of the terrain directly beneath the UAV (nadir). In this work, a depth map (DM) refers to a SAR image reconstructed in the UAV nadir plane. These DMs are fed into a lightweight machine learning (ML) model to accurately estimate the UAV's altitude. To meet the computational and power constraints of UAV platforms, we employ the polar format algorithm (PFA) as a low-complexity SAR processing method. The use of DMs as high-level features enables the deployment of a simple yet effective ML model for altimetry. Additionally, since GPS-based altitudes are referenced relative to a fixed point (e.g., the UAV's take-off location), we derive a nadir depth profile by subtracting GPS altitude from the radar-based estimates. The proposed approach is validated through real-world UAV flight experiments, demonstrating its effectiveness for accurate radarbased altimetry and depth profiling.

Single carrier modulation with hybrid beamforming is a promising choice for sub-THz communication, thanks to its relaxed hardware requirements. However, the peak-to-averagepower ratio (PAPR) of single carrier modulation in a multi-user MIMO scenario is still a concern. In this work, we develop a PAPR reduction technique for single carrier modulation operating with hybrid beamforming. We propose to add a PAPR reduction signal (PRS) to the precoded information symbols in the baseband. The design of PRS is written as an constrained optimization problem. An algorithm based on successive convex approximation is proposed to solve the optimization problem. Our analysis shows that by trading off only a little SINR, a considerable PAPR reduction can be achieved. Further, our simulations reveal that the proposed scheme achieves a significant performance gain compared to the classical single carrier system in the presence of power amplifier distortion.

Unmanned aerial vehicles (UAVs) have become ubiquitous in environmental perception due to their easy deployment. Radars enable UAVs to perceive their surroundings in poor weather and low-visibility conditions, as well as to see through occlusions such as tree foliage and shadows. In this paper, we present a high-resolution depth map (DM) reconstruction method for UAVs using a mm-wave frequency-modulated continuous-wave (FMCW) radar, based on the polar format algorithm (PFA). Additionally, the DM quality is enhanced through a combination of phase gradient autofocus (PGA) and minimum entropy autofocus (MEA) algorithms. The proposed algorithm is significant for its low complexity, meeting the constraints of UAV hardware as an edge device. The effectiveness of the algorithm is demonstrated in a real-world UAV deployment.

Radars provide robust perception of vehicle surroundings by effectively functioning in poor light and adverse weather conditions. Synthetic aperture radar (SAR) algorithms are employed to address the limited angular resolution of radars by enlarging antenna aperture size synthetically as the radar moves. An autofocus algorithm is essential to improve the SAR image quality by compensating for errors mainly caused by inaccurate radar localization. Existing autofocus algorithms are mostly tailored for the frequency domain SAR techniques which are prevalent in aviation and spaceborne applications thanks to their lower complexity in large data processing. However, in the automotive context, the backprojection algorithm (BPA) is often preferred since it provides less distorted images at the cost of more complexity. Addressing the gap in efficient autofocus solutions for time-domain algorithms, this paper introduces a dual-layered autofocus strategy that integrates the Polar Format Algorithm (PFA) with BPA. The first layer employs a novel Localization Error Compensation Autofocus (LECA) processing pipeline to estimate and correct the localization errors within the PFA domain, leveraging its computational efficiency. The second layer seamlessly transfers these corrections to BPA, enabling high-quality SAR imaging while maintaining low complexity. Additionally, the strategy extends Phase Gradient Autofocus (PGA) techniques to enhance the efficiency of localization error compensation for BPA. Validated through real-world automotive experiments, the proposed pipeline delivers state-of-the-art image focus and resolution, setting a new benchmark for computationally efficient SAR imaging.

This paper presents a distributed cell-free communication and radar system that operates in the uplink. The system schedules dedicated transmit (Tx) access points (APs) to transmit dedicated radar signals in the uplink together with the user equipment (UE). The receiving (Rx) APs decode the UE payloads while also detecting targets based on the Tx AP signals. To mitigate the added Tx AP interference, the Rx APs use multiuser processing to recover the UE payloads, while a combination of large processing gains, adaptive beamforming, spatial diversity, interference cancellation and power control is used to mitigate the UE interference impacting the radar. The radar introduces few changes to the physical layer and the additional computations needed are comparable to the communication system. The system is validated numerically by using Monte-Carlo simulations, where we highlight the inherent trade-offs between the various system parameters (such as the power control balancing, and the number of Tx APs scheduled and UEs cancelled) and show that both the communication and radar systems can be effectively integrated into the same network at a near optimal performance.

This work addresses the problem of autofocusing for forward-looking MIMO synthetic aperture radar (FL-MIMO-SAR) images. To this end, we first present and analyze the detailed geometry and signal model of the FL-MIMO-SAR autofocusing problem. Then, we propose and test a comprehensive pipeline for FL-MIMO-SAR autofocusing with automatic radar motion parameters estimation and compensation. The approach leverages a combination of three SAR image quality indicators (IQIs) to assess the performance of the autofocusing process, which is compatible with both time-domain and frequency-domain image reconstruction algorithms. Moreover, the computational complexity of the optimization problem is reduced by employing a guided backprojection (GBP) algorithm. Furthermore, we compare the three IQIs with respect to their sensitivity to different types of positioning errors. The performance of the proposed solution is quantitatively evaluated using different simulated scenarios and controlled experimental data from an anechoic chamber. Finally, we test the applicability of the proposed solution using real data from automotive scenarios. The results show that the proposed pipeline is capable of handling phase-only as well as range-cell-migration defocusing models.