Compressive sensing (CS) is key to reduce the overhead in estimating sparse high dimensional channels at millimeter wave or terahertz frequencies. The channel measurements in CS are usually perturbed by random phase errors, commonly modeled as a Wiener process, at the oscillators. CS algorithms that ignore such phase errors fail to accurately estimate the channel. In practice, the phase errors are similar within a batch of measurements acquired in a short burst and the errors vary significantly across different batches, resulting in partially coherent measurements. We develop a message passing-based channel estimation algorithm that exploits the sparse structure of the channel together with the Wiener statistics of the phase errors. To this end, we absorb the phase errors into the sparse channel, and introduce three hidden variables to model its support, magnitude, and phase. We derive the message flows between these variables while incorporating Wiener phase noise statistics. Finally, we use alternating optimization to decouple the sparse channel and the phase errors from the vector estimated with our message-passing technique. Using simulations, we show that the proposed algorithm achieves better channel reconstruction than comparable benchmarks.

Millimeter wave (mmWave) systems, currently employed in 5G and IEEE 802.11ad/ay devices, enable high data rates through wide bandwidths and directional communication. However, high carrier frequencies used in these systems result in a higher phase noise than lower frequency systems. This paper investigates the problem of spatial channel estimation in the presence of severe phase noise, which manifests as partially coherent phase perturbations in the observed channel measurements. In this model, phase noise remains relatively constant within a packet but varies substantially across packets. Under such partially coherent phase noise, we first develop two computationally efficient on-grid algorithms to estimate narrowband mmWave channels: Partially Coherent Matching Pursuit (PCMP) and Enhanced Partially Coherent Matching Pursuit (EPCMP), assuming a known channel sparsity. Both algorithms exploit the sparse structure in mmWave channels, enabling a significant reduction in training overhead while achieving good estimation performance. The main difference between PCMP and EPCMP is how the sparse channel support is identified. The EPCMP algorithm can achieve better estimation performance at the cost of increased computational complexity compared to the PCMP algorithm. We then relax the known-sparsity assumption, adapt the proposed algorithms accordingly, and further extend them to the wideband case for an unknown sparsity. Additionally, we derive sufficient conditions to recover a support element with proposed algorithms. Simulation results demonstrate the advantages of our methods over comparable channel estimation benchmarks.

Block compressed sensing (BCS) alleviates the high storage and memory complexity with standard CS by dividing the sparse recovery problem into sub-problems. This paper presents a Welch bound-based guarantee on the reconstruction error with BCS, revealing that sparse recovery deteriorates with more partitions. To address this performance loss, we propose a data-driven BCS technique that leverages correlation across signal partitions. Our method surpasses classical BCS in moderate SNR regimes, with a modest increase in storage and computational complexities.

Radar is a key technology in automotive driving for target detection and perception. In this work, we leverage prior environmental information in the form of occupancy maps to design space-time codes for a fully digital MIMO radar. We transform this design problem into the optimization of spatial beamforming gains and time-domain codes. The beamforming gains are optimized to enhance the strength of returns from cells associated with a higher uncertainty of occupancy. The time-domain codes are optimized to minimize the correlation between returns of targets within the drivable space. We validate our method on the nuScenes dataset to show that the designed space-time codes achieve higher detection rates than designs that do not rely on prior information from occupancy maps.

Automotive LiDARs typically have a uniform scanning range over their field of view (FoV). Such a range profile does not account for the varying risk of misdetecting targets in different regions. For instance, prioritizing crosswalks in a LiDAR scan is crucial, as the financial consequences of missing a pedestrian far exceed that of overlooking a distant vehicle. In this paper, we construct a spatial risk map that quantifies the risk of misdetecting targets across different regions around the vehicle. Our risk map incorporates lane semantics, knowledge about previously identified objects, and their potential trajectories. We use this risk map to adapt the LiDAR's scanning range over different sectors in its FoV. Simulations on nuScenes episodes demonstrate that our misdetection risk-aware design reduces the effective risk by about 40% compared to a standard LiDAR.

Channel estimation can lead to a substantial training overhead in millimeter wave (mmWave) and terahertz (THz) systems employing large arrays. Prior work has leveraged channel sparsity at these frequencies to reduce this overhead. Most of the sparsity-aware algorithms, however, assume perfect phase coherence in the channel measurements, which is disrupted due to phase noise. Due to the errors induced by phase noise, standard sparse channel estimation algorithms assuming perfect phase coherence can fail. In this paper, we consider a frame structure in which the channel measurements are acquired over multiple packets. Our model assumes that the phase errors remain constant within a packet and vary considerably across different packets, leading to partially coherent channel measurements. We develop a message passing-based technique for sparse channel estimation under such partially coherent phase errors and show that our approach achieves a lower channel reconstruction error than comparable benchmarks.

Estimation problems in wireless sensor networks typically involve gathering and processing data from distributed sensors to infer the state of an environment at the fusion center. However, not all measurements contribute significantly to improving estimation accuracy. The ordered transmission protocol, a promising approach for enhancing energy efficiency in wireless networks, allows for the selection of measurements from different sensors to ensure the desired estimation quality. In this work, we use the idea of ordered transmission to reduce the number of transmissions required for sequential estimation within a network, thereby achieving energy-efficient estimation. We derive a new stopping rule that minimizes the number of transmissions while maintaining estimation accuracy similar to general sequential estimation with unordered transmissions. Moreover, we derive the expected number of transmissions required for both general sequential estimation with unordered transmissions and proposed sequential estimation with ordered transmissions and make a comparison between the two systems. Simulation results indicate that our proposed scheme can efficiently reduce transmissions while still ensuring the quality of estimation.

Light detection and ranging (LiDAR) plays a crucial role in machine perception for advanced driver assistance systems. Existing LiDARs, however, do not adapt their sensing strategy to complement driver's perception. We demonstrate a novel LiDAR prototype that dynamically adapts its range and resolution over the field of view, according to real-time driver gaze. Our gaze-aware LiDAR emphasizes scanning peripheral zones the driver may overlook, i.e., critical areas during driving. Our demonstration showcases enhanced perception, highlighting the potential of hybrid human-machine sensing for safer driving.

Beam acquisition is key in enabling millimeter wave and terahertz radios to achieve their capacity. Due to the use of large antenna arrays in these systems, the common exhaustive beam scanning results in a substantial training overhead. Prior work has addressed this issue by developing compressive sensing (CS)-based methods which exploit channel sparsity for faster beam acquisition. Unfortunately, most CS techniques employ wide beams and suffer from a low signal-to-noise ratio (SNR) in the channel measurements. To solve this challenge, we develop an IEEE 802.11ad/ay compatible technique that takes an in-sector approach for CS. In our method, the angle domain channel is partitioned into several sectors, and the channel within the best sector is estimated and then used for beamforming. The essence of our framework lies in the construction of a low-resolution beam codebook to identify the best sector and in the design of a CS matrix optimized for in-sector channel estimation. Our beam codebook illuminates distinct non-overlapping sectors and can be realized with low-resolution phased arrays. We show that the proposed codebook results in a higher received SNR than the state-of-the-art sector sweep codebooks. Furthermore, our optimized CS matrix achieves a better in-sector channel reconstruction and a higher achievable rate than comparable benchmarks.

Millimeter-wave radar is a common sensor modality used in automotive driving for target detection and perception. These radars can benefit from side information on the environment being sensed, such as lane topologies or data from other sensors. Existing radars do not leverage this information to adapt waveforms or perform prior-aware inference. In this paper, we model the side information as an occupancy map and design transmit beamformers that are customized to the map. Our method maximizes the probability of detection in regions with a higher uncertainty on the presence of a target. Simulation results on the nuScenes dataset show that the designed beamformer achieves substantially higher detection rates than a conventional omnidirectional beamformer for the same transmitted power.

This paper develops a channel estimation technique for millimeter wave (mmWave) communication systems. Our method exploits the sparse structure in mmWave channels for low training overhead and accounts for the phase errors in the channel measurements due to phase noise at the oscillator. Specifically, in IEEE 802.11ad/ay-based mmWave systems, the phase errors within a beam refinement protocol packet are almost the same, while the errors across different packets are substantially different. Consequently, standard sparsity-aware algorithms, which ignore phase errors, fail when channel measurements are acquired over multiple beam refinement protocol packets. We present a novel algorithm called partially coherent matching pursuit for sparse channel estimation under practical phase noise perturbations. Our method iteratively detects the support of sparse signal and employs alternating minimization to jointly estimate the signal and the phase errors. We numerically show that our algorithm can reconstruct the channel accurately at a lower complexity than the benchmarks.

Millimeter wave (mmWave) technology can achieve high-speed communication due to the large available spectrum. Furthermore, the use of directional beams in mmWave system provides a natural defense against physical layer security attacks. In practice, however, the beams are imperfect due to mmWave hardware limitations such as the low-resolution of the phase shifters. These imperfections in the beam pattern introduce an energy leakage that can be exploited by an eavesdropper. To defend against such eavesdropping attacks, we propose a directional modulation-based defense technique where the transmitter applies random circulant shifts of a beamformer. We show that the use of random circulant shifts together with appropriate phase adjustment induces artificial phase noise (APN) in the directions different from that of the target receiver. Our method corrupts the phase at the eavesdropper without affecting the communication link of the target receiver. We also experimentally verify the APN induced due to circulant shifts, using channel measurements from a 2-bit mmWave phased array testbed. Using simulations, we study the performance of the proposed defense technique against a greedy eavesdropping strategy in a vehicle-to-infrastructure scenario. The proposed technique achieves better defense than the antenna subset modulation, without compromising on the communication link with the target receiver.

Eavesdropping attacks are a severe threat to millimeter-wave (mmWave) networks that use low-resolution phased arrays. Although directional beamforming in mmWave phased arrays provides natural defense against eavesdropping, the use of low-resolution phase shifters induces energy leakage into unintended directions. This energy leakage can be exploited by the adversaries. In this paper, we propose a directional modulation (DM)-based defense against eavesdropping attacks on low-resolution phased arrays. Our defense technique applies random circulant shifts to the beamformer for every symbol transmission. By appropriately adjusting the phase of the transmitted symbol, the transmitter (TX) can maintain a high-quality link with the receiver while corrupting the symbols transmitted along unintended directions. We theoretically analyze the secrecy mutual information (SMI) achieved by the proposed defense mechanism and show that our defense induces artificial phase noise (APN) along unintended directions, which increases the SMI of the system. Finally, we numerically show the superiority of the proposed defense technique over the state-of-the-art defense techniques.

Phased arrays, commonly used in IEEE 802.11ad and 5G radios, are capable of focusing radio frequency signals in a specific direction or a spatial region. Beamforming achieves such directional or spatial concentration of signals and enables phased array-based radios to achieve high data rates. Designing beams for millimeter wave and terahertz communication using massive phased arrays, however, is challenging due to hardware constraints and the wide bandwidth in these systems. For example, beams which are optimal at the center frequency may perform poor in wideband communication systems where the radio frequencies differ substantially from the center frequency. The poor performance in such systems is due to differences in the optimal beamformers corresponding to distinct radio frequencies within the wide bandwidth. Such a mismatch leads to a misfocus effect in near-field systems and the beam squint effect in far-field systems. In this paper, we investigate the misfocus effect and propose InFocus, a low complexity technique to construct beams that are well suited for massive wideband phased arrays. The beams are constructed using a carefully designed frequency modulated waveform in the spatial dimension. InFocus mitigates beam misfocus and beam squint when applied to near-field and far-field systems.

In this paper, we propose a learning-aided signal processing solution for channel estimation in 5G new radio (NR). Channel estimation is an important algorithm for baseband modem design. In 5G NR, estimating the channel is challenging due to two reasons. First, the pilot signals are transmitted over a small fraction of the available time-frequency resources. Second, the real time nature of physical layer processing introduces a strict limitation on the computational complexity of channel estimation. To this end, we propose a channel estimation technique that integrates a small one hidden layer neural network between two linear minimum mean squared error (LMMSE) interpolation blocks. While the neural network leverages the advantages of offline data-driven learning, the LMMSE blocks exploit the second order online channel statistics along time and frequency dimensions. The training procedure tunes the weights of the neural network by back-propagating through the time domain LMMSE interpolation block. We derive bounds on the training loss with the proposed method and show that our approach can improve the channel estimate.

Millimeter wave (mmWave) communication is one viable solution to support Gbps sensor data sharing in vehicular networks. The use of large antenna arrays at mmWave and high mobility in vehicular communication make it challenging to design fast beam alignment solutions. In this paper, we propose a novel framework that learns the channel angle-of-departure (AoD) statistics at a base station (BS) and uses this information to efficiently acquire channel measurements. Our framework integrates online learning for compressive sensing (CS) codebook learning and the optimized codebook is used for CS-based beam alignment. We formulate a CS matrix optimization problem based on the AoD statistics available at the BS. Furthermore, based on the CS channel measurements, we develop techniques to update and learn such channel AoD statistics at the BS. We use the upper confidence bound (UCB) algorithm to learn the AoD statistics and the CS matrix. Numerical results show that the CS matrix in the proposed framework provides faster beam alignment than standard CS matrix designs. Simulation results indicate that the proposed beam training technique can reduce overhead by 80% compared to exhaustive beam search, and 70% compared to standard CS solutions that do not exploit any AoD statistics.

Receivers with one-bit analog-to-digital converters (ADCs) are promising for high bandwidth millimeter wave (mmWave) systems as they consume less power than their full resolution counterparts. The extreme quantization in one-bit receivers and the use of large antenna arrays at mmWave make channel estimation challenging. In this paper, we develop channel estimation algorithms that exploit the low-rank property of mmWave channels. We also propose a novel training solution that results in a low complexity implementation of our algorithms. Simulation results indicate that the proposed methods achieve better channel reconstruction than compressed sensing-based techniques that exploit sparsity of mmWave channels.

Millimeter wave channels exhibit structure that allows beam alignment with fewer channel measurements than exhaustive beam search. From a compressed sensing (CS) perspective, the received channel measurements are usually obtained by multiplying a CS matrix with a sparse representation of the channel matrix. Due to the constraints imposed by analog frontends, designing CS matrices that efficiently exploit the channel structure is challenging. In this paper, we propose an end-to-end deep learning technique to design a structured CS matrix that is well suited to the underlying channel distribution, leveraging both sparsity and the particular spatial structure that appears in vehicular channels. The channel measurements acquired with the designed CS matrix are then used to predict the best beam for link configuration. Simulation results for vehicular communication channels indicate that our deep learning-based approach achieves better beam alignment than standard CS techniques that use the random phase shift-based design.

Millimeter wave (mmWave) communication in typical wearable and data center settings is short range. As the distance between the transmitter and the receiver in short range scenarios can be comparable to the length of the antenna arrays, the common far field approximation for the channel may not be applicable. As a result, dictionaries that result in a sparse channel representation in the far field setting may not be appropriate for short distances. In this paper, we develop a novel framework to exploit the structure in short range mmWave channels. The proposed method splits the channel into several subchannels for which the far field approximation can be applied. Then, the structure within and across different subchannels is leveraged using message passing. We show how information about the antenna array geometry can be used to design message passing factors that incorporate structure across successive subchannels. Simulation results indicate that our framework can be used to achieve better beam alignment with fewer channel measurements when compared to standard compressed sensing-based techniques that do not exploit structure across subchannels.

Compressed sensing (CS)-based beam alignment is a promising solution for rapid link configuration in millimeter wave (mmWave) systems that use large arrays. Translating CS to practical mmWave radios, however, can be challenging due to carrier frequency offset (CFO). Standard sparse recovery techniques that use random sampling strategies to acquire channel measurements can fail even if there is a slight mismatch in carrier frequencies. In this paper, we show that restricting the randomness in compressive sampling to local sets can achieve robustness to structured errors due to CFO. The proposed approach requires fewer channel measurements than comparable algorithms and has the same complexity as standard CS.

Millimeter wave multiple-input-multiple-output (MIMO) achieves the best performance when reliable channel state information is used to design the beams. Most channel estimation methods proposed in the literature, however, ignore practical hardware impairments such as carrier frequency offset (CFO) and may fail under such impairment. In this paper, we present a joint CFO and channel estimation method based on tensor modeling and compressed sensing. Simulation results indicate that the proposed method yields better channel recovery performance than the benchmark and that it is more robust to a small number of channel measurements.

Millimeter wave (mmWave) systems can enable high data rates if the link between the transmitting and receiving radios is configured properly. Fast configuration of mmWave links, however, is challenging due to the use of large antenna arrays and hardware constraints. For example, a large amount of training overhead is incurred by exhaustive search-based beam alignment in typical mmWave phased arrays. In this paper, we present a framework called FALP for Fast beam Alignment with Low-resolution Phase shifters. FALP uses an efficient set of antenna weight vectors to acquire channel measurements, and allows faster beam alignment when compared to exhaustive scan. The antenna weight vectors in FALP can be realized in ultra-low power phase shifters whose resolution can be as low as one-bit. From a compressed sensing (CS) perspective, the CS matrix designed in FALP satisfies the restricted isometry property and allows CS algorithms to exploit the fast Fourier transform. The proposed framework also establishes a new connection between channel acquisition in phased arrays and magnetic resonance imaging.

Channel estimation at millimeter wave (mmWave) carrier frequencies is challenging when large antenna arrays are used. Prior work has leveraged the sparse nature of mmWave channels via compressed sensing-based algorithms for channel estimation. Most of these algorithms, though, assume perfect synchronization and are vulnerable to phase errors that arise due to carrier frequency offset and phase noise. Recently, sparsity-aware, non-coherent beamforming algorithms that are robust to phase errors were proposed for narrowband phased array systems with full resolution analog-to-digital converters. Such energy-based algorithms, however, are not robust to heavy quantization at the receiver. In this paper, we develop a joint carrier frequency offset and wideband channel estimation algorithm that is scalable across different hardware architectures. Our method exploits the sparse nature of mmWave channels in the angle-delay domain, in addition to the compressibility of the phase error vector. We formulate the joint estimation as a quantized sparse bilinear optimization problem and then use message passing for recovery. We also give an efficient implementation of a generalized bilinear message passing algorithm for the joint estimation in one-bit receivers. Simulation results show that our method is able to estimate the frequency offset and the channel compressibility, even in the presence of phase noise.