In this paper, we propose a hybrid precoding/combining framework for communication-centric integrated sensing and full-duplex (FD) communication operating at mmWave bands. The designed precoders and combiners enable multiuser (MU) FD communication while simultaneously supporting monostatic sensing in a frequency-selective setting. The joint design of precoders and combiners involves the mitigation of self-interference (SI) caused by simultaneous transmission and reception at the FD base station (BS). Additionally, MU interference needs to be handled by the precoder/combiner design. The resulting optimization problem involves non-convex constraints since hybrid analog/digital architectures utilize networks of phase shifters. To solve the proposed problem, we separate the optimization of each precoder/combiner, and design each one of them while fixing the others. The precoders at the FD BS are designed by reformulating the communication and sensing constraints as signal-to-leakage-plus-noise ratio (SLNR) maximization problems that consider SI and MU interference as leakage. Furthermore, we design the frequency-flat analog combiner such that the residual SI at the FD BS is minimized under communication and sensing gain constraints. Finally, we design an interference-aware digital combining stage that separates MU signals and target reflections. The communication performance and sensing results show that the proposed framework efficiently supports both functionalities simultaneously.

This paper introduces a novel hybrid analog/digital transceiver design for full-duplex (FD) integrated sensing and communication (ISAC) systems operating at mmWave band. The proposed scheme simultaneously supports downlink (DL) and uplink (UL) multiuser communication along with monostatic sensing, while suppressing the self-interference (SI). Considering that high SI levels may lead to saturation at low-noise amplifiers (LNAs), our design incorporates two SI mitigation constraints: one imposed at the receiver (RX) antennas before LNAs and another after the analog combining stage before analog-to-digital converters (ADCs). By formulating optimization problems that balance the trade-offs between spectral efficiency and beam-pattern error, we leverage a projected gradient ascent (PGA) algorithm with penalty-based methods to iteratively design hybrid precoders and combiners. Simulation results show that the proposed architecture strikes a balance between communication and sensing performance while effectively mitigating SI.

The upper mid-band spectrum, spanning 7–24 GHz, offers a promising balance between high data rates, sensing resolution and wide coverage, making it a key candidate band for future generation networks. However, its dynamic and shared nature—with incumbents such as radar, satellite, and radio astronomy—demands highly adaptive and efficient channel state information (CSI) acquisition techniques to support multiband operation and band switching. In this work, we propose a novel transformer-based deep learning architecture for cross-frequency channel prediction within the upper mid-band, eliminating the immediate need of pilot transmission when switching to a new band. Our approach combines convolutional neural network (CNN) for local spatial feature extraction and vision transformer (ViT) for capturing global dependencies within the channel matrix. We introduce a masked image modeling (MIM) strategy, selectively removing the least significant channel information when creating the channel matrix. This forces the model to infer dominant channel structures. Cross-attention is employed to align encoder and decoder representations, enabling accurate frequency translation guided by informative features from the source band. Evaluations on a ray-tracing generated dataset of upper mid-band channels at 8 GHz and 15 GHz, demonstrate that our hybrid model achieves a high prediction accuracy across frequencies, even under varying array configurations and bandwidths. These results highlight the model’s potential for real-time, low-overhead reconfiguration in upper mid-band systems with multi-band operation and band switching.

In this paper, we characterize the effect of radar-to-radar interference in a system setting in terms of the probability of missed detection for two common driving scenarios: Highway traffic and a 4-way intersection. We focus on the frequency modulated continuous wave (FMCW) and phase modulated continuous wave (PMCW) radar waveforms and try to answer the question: Is one waveform inherently better than the other for automotive applications? In contrast to most results available in published literature, we show that PMCW does not have an advantage over FMCW in a system setting. For the scenarios considered in this paper, our results show that a typical FMCW radar unit actually outperforms a typical PMCW radar unit in terms of the probability of missed detection by using coordination across the radar network.

In this paper, we propose a novel indoor localization algorithm that exploits the angle and delay information of the sparse channel paths at mmWave. We consider that the user and the access point (AP) are not perfectly synchronized, which results in an unknown clock offset for the estimated delays. The proposed algorithm comprises two stages where the initial stage is to estimate the unknown clock offset and the locations of the users by leveraging the properties of the indoor environment. Then, the initial location estimates of users are collected and used to learn the virtual anchor locations. Finally, we propose a one-shot anchor-based localization algorithm that outperforms the initial one.

In the context of integrated sensing and communication (ISAC), a full-duplex (FD) transceiver can operate as a monostatic radar while maintaining communication capabilities. This paper investigates the design of precoders and combiners for a joint radar and communication (JRC) system at mmWave frequencies. The primary goal of the design is to guarantee certain performance in terms of some sensing and communication metrics while minimizing the self-interference (SI) caused by FD operation and taking into account the hardware limitations coming from a hybrid MIMO architecture. Specifically, we introduce a generalized eigenvalue-based precoder design that considers the downlink user rate, the radar gain, and the SI suppression. Since the hybrid analog/digital architecture degrades the SI mitigation capability of the precoder, we further enhance SI suppression with the analog combiner. Our numerical results demonstrate that the proposed architecture achieves the required radar gain and SI mitigation while incurring a small loss in downlink spectral efficiency. Additionally, the numerical experiments also show that the use of orthogonal frequency division multiplexing (OFDM) radar with the proposed beamforming architecture results in highly accurate range and velocity estimates for the detected targets.

In this paper, we address the complex task of estimating the parameters for multiple propagation paths of realistic millimeter wave (mmWave) channels. We propose a solution with a reasonable computational complexity while providing high accuracy, which is required for precise positioning in joint localization and communication systems. We introduce an innovative method termed ESPRIT-D - beamspace Estimation of Signal Parameters via Rotational Invariance Techniques with a Dictionary based solution. It exploits a model for the mmWave multipath channel accounting for filtering effects, represented as a 5D tensor. Our solution develops a modification of beamspace ESPRIT that can operate with analog beamforming to extract the directions of departure and arrival in azimuth and elevation, while retrieving the delay estimates by a greedy sparse recovery method. We evaluated the proposed method using channel realizations generated by ray-tracing simulation of an outdoor environment, demonstrating an average angular error below 0.01° for line-of-sight (LoS) paths and 0.1° for nonline-of-sight (NLoS) components. The accuracy in delay estimation achieves an average of$3\mathrm{e}^{-10}\mathrm{s}$. Compared with state-of-the-art (SOTA), our algorithm exhibits a 10× improvement in estimation accuracy.

Future wireless networks will integrate sensing, learning, and communication to provide new services beyond communication and to become more resilient. Sensors at the network infrastructure, sensors on the user equipment (UE), and the sensing capability of the communication signal itself provide a new source of data that connects the physical and radio frequency (RF) environments. A wireless network that harnesses all these sensing data can not only enable additional sensing services but also become more resilient to channel-dependent effects such as blockage and better support adaptation in dynamic environments as networks reconfigure. In this article, we provide a vision for integrated sensing and communication (ISAC) networks and an overview of how signal processing, optimization, and machine learning (ML) techniques can be leveraged to make them a reality in the context of 6G. We also include some examples of the performance of several of these strategies when evaluated using a simulation framework based on a combination of ray-tracing measurements and mathematical models that mix the digital and physical worlds.

Reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) wireless systems offer robustness to blockage and enhanced coverage. In this paper, we develop an algorithmic solution that shows how RISs can also enhance the positioning performance in a joint localization and communication setting, even when hardware impairments are considered. We propose a realistic system architecture that considers the clock offset between the transmitter and the receiver, impairments at transmit and receive arrays, and mutual coupling between the RIS elements. We formulate the estimation of the composite channel in a RIS-aided mmWave system as a multidimensional orthogonal matching pursuit problem, which can be solved with high accuracy and low complexity, even when operating with large antenna arrays as required at mmWave. In addition, we introduce a dictionary learning stage to calibrate the hardware impairments at the user array. To complete our design, we devise a localization scheme that exploits the estimated composite channel while accounting for the clock offset between the transmitter and the receiver. Numerical results show how RIS-aided mmWave systems can significantly improve the localization accuracy in a realistic 3D indoor scenario simulated by ray tracing.

Reconfigurable intelligent surfaces (RISs )-aided communication at millimeter wave frequencies will likely require a large size RIS to achieve the expected link budget in outdoor environments. This large size of the RIS will induce a near field (NF) operation mode. Previous approaches to operate in the NF consider RIS configurations that provide very focused beams, targeted toward a grid of user locations to build the corresponding codebook. In this paper, we design variable width beam codebooks for the NF using a technique that maps the RIS elements to a tunable spherical surface. By tuning the size and center of the surface, variable width beams and illuminated areas can be achieved. Simulation results show the effectiveness of the proposed method to maintain a high average rate while expanding the illumination region associated to beam focusing. Our configuration also outperforms the state-of-the-art in terms of maintaining the intended shape of the illuminated region even if not in the boresight directions of the RIS.

This paper proposes a radio simultaneous location and mapping (radio-SLAM) scheme based on sparse multipath channel estimation. By leveraging sparse channel estimation schemes at millimeter wave bands, namely high resolution estimates of the multi-path angle of arrival (AoA), time difference of arrival (TDoA), and angle of departure (AoD), we develop a radio-SLAM algorithm that operates without any requirements of clock synchronization, receiver orientation knowledge, multiple anchor points, or two-way protocols. Thanks to the AoD information obtained via compressed sensing (CS) of the channel, the proposed scheme can estimate the receiver clock offset and orientation from a single anchor transmission, achieving sub-meter accuracy in a realistic typical channel simulation.

We describe a novel constrained least-squares (LS) optimization problem and propose an iterative solution that deals with the constraints of placing variables on the complex unit circle. We reformulate the LS problem with unit magnitude constraints so that we obtain efficient closed-form local updates based on Procrustes orthogonal solutions that monotonically improve the objective function. We show the performance of the proposed algorithm in a synthetic experiment and an application to hybrid precoding/combining in mmWave MIMO communications.

Low overhead channel estimation based on compressive sensing (CS) has been widely investigated for hybrid wideband millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. The channel sparsifying dictionaries used in prior work are built from ideal array response vectors evaluated on discrete angles of arrival/departure. In addition, these dictionaries are assumed to be the same for all subcarriers, without considering the impacts of hardware impairments and beam squint. In this manuscript, we derive a general channel and signal model that explicitly incorporates the impacts of hardware impairments, practical pulse shaping functions, and beam squint, overcoming the limitations of mmWave MIMO channel and signal models commonly used in previous work. Then, we propose a dictionary learning (DL) algorithm to obtain the sparsifying dictionaries embedding hardware impairments, by considering the effect of beam squint without introducing it into the learning process. We also design a novel CS channel estimation algorithm under beam squint and hardware impairments, where the channel structures at different subcarriers are exploited to enable channel parameter estimation with low complexity and high accuracy. Numerical results demonstrate the effectiveness of the proposed DL and channel estimation strategy when applied to realistic mmWave channels.

Millimeter wave (MmWave) systems are vulnerable to blockages, which cause signal drop and link outage. One solution is to deploy reconfigurable intelligent surfaces (RISs) to add a strong non-line-of-sight path from the transmitter to receiver. To achieve the best performance, the location of the deployed RIS should be optimized for a given site, considering the distribution of potential users and possible blockers. In this paper, we find the optimal location, height and downtilt of RIS working in a realistic vehicular scenario. Because of the proximity between the RIS and the vehicles, and the large electrical size of the RIS, we consider a 3D geometry including the elevation angle and near-field beamforming. We provide results on RIS configuration in terms of both coverage ratio and area-averaged rate. We find that the optimized RIS improves the area-averaged rate fifty percent over the case without a RIS, as well as further improvements in the coverage ratio.

In this article, we study the joint communication and sensing (JCAS) paradigm in the context of millimeter-wave (mm-wave) mobile communication networks. We specifically address the JCAS challenges stemming from the full-duplex operation in monostatic orthogonal frequency-division multiplexing (OFDM) radars and from the co-existence of multiple simultaneous beams for communications and sensing purposes. To this end, we first formulate and solve beamforming optimization problems for hybrid beamforming based multiuser multiple-input and multiple-output JCAS systems. The cost function to be maximized is the beamformed power at the sensing direction while constraining the beamformed power at the communications directions, suppressing interuser interference and cancelling full-duplexing related self-interference (SI). We then also propose new transmitter and receiver beamforming solutions for purely analog beamforming based JCAS systems that maximize the beamforming gain at the sensing direction while controlling the beamformed power at the communications direction(s), cancelling the SI as well as eliminating the potential reflection from the communication direction and optimizing the combined radar pattern (CRP). Both closed-form and numerical optimization based formulations are provided. We analyze and evaluate the performance through extensive numerical experiments, and show that substantial gains and benefits in terms of radar transmit gain, CRP, and SI suppression can be achieved with the proposed beamforming methods.

Configuring precoders and combiners is a major challenge to deploy practical multiple-input multiple-output (MIMO) millimeter wave (mmWave) communication systems with large antenna arrays. Most prior work addresses the problem focusing on a total transmit power constraint. In practical transmitters, however, power amplifiers must operate within their linear range, so that a power constraint applies to each one of the input signals to these devices. Therefore, precoder and combiner designs should incorporate per-antenna or per-radio frequency (RF) chain transmit power constraints. We focus on such problem for frequency-selective channels with multicarrier modulation, and assuming hybrid analog/digital architectures based on fully connected analog blocks implemented with finite-resolution phase shifters. We first derive an all-digital solution which aims to maximize spectral efficiency. Then, we develop hybrid precoders and combiners by approximately matching the corresponding all-digital matrices while still enforcing the power constraints. Numerical results show that the proposed all-digital design performs close to the upper bound given by the standard waterfilling-based solution with a total power constraint. Additionally, the hybrid designs exhibit a moderate loss even when low-resolution phase shifters are considered.

The high overhead of the beam training process is the main challenge when establishing mmWave communication links, especially for vehicle-to-everything (V2X) scenarios where the channels are highly dynamic. In this paper, we obtain prior information to speed up the beam training process by implementing two deep neural networks (DNNs) that realize radar-to-communication (R2C) channel information translation in a vehicle-to-infrastructure (V2I) system. Specifically, the first DNN is built to extract the information from the radar azimuth power spectrum (APS) to reconstruct the communication APS, while the second DNN exploits the information extracted from the spatial covariance of the radar channel to realize R2C covariance prediction. The achieved data rate and the similarity between the estimated and the true communication APS are used to evaluate the prediction performance. The covariance estimation method generally provides higher similarity, as the APS predictions cannot always capture the mismatch between the radar and communication APS. Compared to the beam training method which exploits directly the radar APS without an attempt to translate it to the communication channel, our proposed deep learning (DL) aided methods remarkably reduce the beam training overhead, resulting in a 13.3% and 21.9% rate increase when using the communication APS prediction and covariance prediction, respectively.

Tracking wideband millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems based on a hybrid architecture is a challenging problem, especially in high mobility scenarios where links are likely to be blocked by obstacles, such as trees, pedestrians or vehicles. In this paper, we propose a new strategy to track the frequency selective mmWave channel under blockage. We first introduce a statistical channel model that includes the evolution models for channel gains and angles of arrival and departure, as well as the statistics of blockage events. Then, we define a change point detection (CPD) test to identify the time instants where blockage appears/disappears, so the appropriate channel evolution models can be used for wideband channel tracking during the blockage events. To simultaneously achieve a high CPD success rate and a high accuracy in the channel estimate, we further propose a double digital combiner architecture that employs different digital combiners for CPD and channel tracking. Finally, we integrate into our framework a previously proposed Bayesian channel tracking algorithm. Simulation results show that the proposed approach achieves a good channel tracking performance even in mobile scenarios that suffer from highly dynamic blockage events.

A major source of difficulty when operating with large arrays at millimeter wave (mmWave) frequencies is to estimate the wideband channel, since the use of hybrid architectures acts as a compression stage for the received signal. Moreover, the channel has to be tracked and the antenna arrays regularly reconfigured to obtain appropriate beamforming gains when a mobile setting is considered. In this paper, we focus on the problem of channel tracking for frequency-selective mmWave channels, and propose two novel channel tracking algorithms. One of them exploits the sparsity of the mmWave channel, while the other one leverages prior statistical information about the channel parameters. We also propose a hybrid precoder and combiner design method to increase the received signal-to-noise ratio (SNR) during channel tracking, such that near-optimum data rates can be obtained with low-overhead. In our numerical results, we analyze the performance of our proposed algorithms for different system parameters. Simulation results show that our proposed channel tracking algorithms are able to achieve near-optimum data rates outperforming state-of-the-art methods.

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.

The development of dual-functional radar-communication (DFRC) systems, where vehicle localization and tracking can be combined with vehicular communication, will lead to more efficient future vehicular networks. In this paper, we develop a predictive beamforming scheme in the context of DFRC systems. We consider a system model where the road-side unit estimates and predicts the motion parameters of vehicles based on the echoes of the DFRC signal. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the accuracy of the angle estimation. To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields a near optimal performance achieved by the maximum a posteriori estimation. The beamformers are then designed based on the predicted angles for establishing the communication links. With the employment of appropriate approximations, all messages on the factor graph can be derived in a closed-form, thus reduce the complexity. Simulation results show that the proposed DFRC based beamforming scheme is superior to the feedback-based approach in terms of both estimation and communication performance. Moreover, the proposed message passing algorithm achieves a similar performance of the high-complexity particle filtering-based methods.

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) has the potential to offer high data rates for vehicle-to-everything (V2X) communication. In this paper, we provide an introduction to important use cases of V2X as they pertain to fifth generation (5G) communication networks. As 5G technology is still evolving to support vehicles, and mmWave is reflected and blocked by vehicles, it remains unclear if the target rates for the use cases can be achieved. Motivated by the different data rate requirements, we introduce a methodology for evaluating rates in 5G mmWave V2X scenarios. Our approach leverages available city CAD models, realistic traffic simulators, and industry standard ray tracing tools to allow site-specific propagation evaluation. This approach may be used to devise insight into the role of traffic density, antenna array placement, and base station density in important urban propagation settings. Results are provided that highlight the application to develop a rate map for an urban intersection. They show that rate increases in dense deployments by more than ten percent per base station, but only decreases about one percent when going from light to heavy traffic.

Spatial channel covariance information can replace full instantaneous channel state information for the analog precoder design in hybrid analog/digital architectures. Obtaining spatial channel covariance estimation, however, is challenging in the hybrid structure due to the use of fewer radio frequency (RF) chains than the number of antennas. In this paper, we propose a spatial channel covariance estimation method based on higher-order tensor decomposition for spatially sparse time-varying frequency-selective channels. The proposed method leverages the fact that the channel can be represented as a low-rank higher-order tensor. We also derive the Cramér-Rao lower bound on the estimation accuracy of the proposed method. Numerical results and theoretical analysis show that the proposed tensor-based approach achieves higher estimation accuracy in comparison with prior compressive-sensing-based approaches or conventional angle-of-arrival estimation approaches.

Exploiting channel sparsity at millimeter wave (mmWave) frequencies reduces the high training overhead associated with the channel estimation stage. Compressive sensing (CS) channel estimation techniques usually adopt the (overcomplete) Fourier transform matrix as sparsifying dictionary. This may not be the best choice when considering hardware impairments in practical arrays. We propose two dictionary learning (DL) algorithms to learn the best sparsifying dictionaries for channel matrices from observations obtained with practical hybrid frequency-selective mmWave multiple-input-multiple-output (MIMO) systems. First, we optimize the combined dictionary, i.e., the Kronecker product of transmit and receive dictionaries, as it is used in practice to sparsify the channel matrix. This stage operates as a calibration phase, since all the hardware imperfections are embedded into the learnt dictionaries. Second, considering the different array structures at the transmitter and receiver, we exploit separable DL to find the best transmit and receive dictionaries. Once the channel is expressed in terms of the optimized dictionaries, various CS-based sparse recovery techniques can be applied for low overhead channel estimation. The effectiveness of the proposed DL algorithms under low SNR conditions has been corroborated via numerical simulations with different system configurations, array geometries and hardware impairments.

Full-duplex (FD) communication is an enabling technology to increase spectral efficiency. The self- interference (SI) resulting from sharing time and frequency resources between different transceivers in FD mode must, however, be managed. At millimeter wave (mmWave) frequencies, FD communication is different than at sub-6 GHz frequencies, because non conventional MIMO architectures and large antenna arrays are often used. In this paper, we address a major challenge for FD mmWave systems: the design of hybrid precoders and combiners that simultaneously maximize the sum spectral efficiency and cancel the SI in the analog domain, to keep control of the signal level at the input of the analog- to-digital converters (ADCs). The optimal joint design is a very difficult problem, since it involves the optimization of eight precoding/combining matrices with several constraints, some of them non convex. We derive two suboptimal solutions which exhibit near- optimum spectral efficiency and significantly outperform prior work in terms of SI cancelation.

Configuring hybrid precoders and combiners is the main challenge to be solved to operate at millimeter wave (mmWave) frequencies. The use of hybrid architectures imposse hardware constraints on the analog precoder that need to be carefully dealt with. In this paper, we develop hybrid precoders and combiners aiming at minimizing the Euclidean distance with respect to the approximate all-digital precoders and combiners maximizing the spectral efficiency under per-antenna power constraints. Numerical results demonstrate the effectiveness of the proposed design method, whose performance is close to that of the all-digital solution.

A major challenge in millimeter wave (mmWave) communications is to configure the antenna arrays in the transceivers. The mobility of the users in mmWave cellular networks makes it necessary to periodically reconfigure the precoders and combiners, according to the variations of the channel. In this paper, we propose a new strategy to track the channel variations in a multi-user (MU) hybrid mmWave MIMO communication cellular network. Besides exploiting the sparse nature of the mmWave channel, this approach also leverages statistical knowledge of the channel parameters, which enables further overhead reductions. Simulation results show that using the proposed algorithm, it is possible to maintain an effective high data rate even in high mobility scenarios.

In high mobility applications of millimeter wave (mmWave) communications, e.g., vehicle-to-everything communication and next-generation cellular communication, frequent link configuration can be a source of significant overhead. We use the sub-6 GHz channel covariance as an out-of-band side information for mmWave link configuration. Assuming: 1) a fully digital architecture at sub-6 GHz and 2) a hybrid analog-digital architecture at mmWave, we propose an out-of-band covariance translation approach and an out-of-band aided compressed covariance estimation approach. For covariance translation, we estimate the parameters of sub-6 GHz covariance and use them in theoretical expressions of covariance matrices to predict the mmWave covariance. For out-of-band aided covariance estimation, we use weighted sparse signal recovery to incorporate out-of-band information in compressed covariance estimation. The out-of-band covariance translation eliminates the in-band training completely, whereas out-of-band aided covariance estimation relies on in-band as well as out-of-band training. We also analyze the loss in the signal-to-noise ratio due to an imperfect estimate of the covariance. The simulation results show that the proposed covariance estimation strategies can reduce the training overhead compared to the in-band only covariance estimation.

Channel estimation at millimeter wave (mmWave) allows designing hybrid precoders and combiners to optimize performance metrics, such as the spectral efficiency. One major source of difficulty when estimating the high-dimensional and large-bandwidth channel, however, is the carrier frequency offset (CFO) that impairs the received signal. Most of the prior work assumes perfect time-frequency synchronization when estimating the channel. However, in practical systems, this assumption does not hold, and joint time-frequency synchronization is an important issue to deal with. In this paper, we propose a multi-stage algorithm for joint CFO and channel estimation. Using a training protocol between a transmitter and a receiver, we estimate first the equivalent low-SNR beamformed channel and the CFO, using the maximum-likelihood (ML) criterion. Thereafter, using these estimates, the high-dimensional multiple-input multiple-output (MIMO) channel is estimated leveraging its angular sparsity. The simulation results show that, using our proposed strategy, small estimation errors and near-optimal values of spectral efficiency can be achieved, without incurring in significant overhead and/or computational complexity.

The two key features of millimeter wave-(mmWave) based MIMO communication are the use of large antenna arrays at the transceivers and large bandwidth. The former complicates the design of optimal beamformers, while the latter makes the system frequency-selective and, thus, requires equalization. Conventionally, for wideband mmWave channels, choosing the precoders/combiners have involved frequency-selective designs that are based on channel state information. In this paper, we show that under some assumptions, semi-unitary frequency-flat precoding and combining are sufficient for low-scattering millimeter wave channels. To show this, we evaluate the conditions and practical settings under which the dominant subspaces of the frequency-domain channel matrices are similar. We model the frequency-dependence of uniform linear antenna arrays, which leads to what is known as beam-squint, for two different practical setting to analyze the optimality of frequency-flat beamforming designs under practical hardware impairments. For the cases when the optimality conditions hold, we propose novel techniques based on compressive subspace estimation to design the optimal frequency-flat, semi-unitary precoders and combiners. Simulation results show that the system achieves near-digital spectral efficiencies at very small implementation cost of beamformers and channel training overhead.

Acquiring channel informaiton is necessary to design the precoders and combiners in hybrid MIMO (Multiple-Input Multiple-Output) systems operating at millimeter wave (mmWave). Most of prior work on channel estimation with hybrid MIMO architectures neglects the beam-squint effect that appears when the system bandwidth is very large. To consider beam-squint, it is necessary to assume frequency- dependent array steering vectors, which further complicates the estimation of the mmWave wideband channel. In this paper, we propose al algorithm that approximates the optimal solution to the dictionary-constrained Maximum-Likelihood (ML) estimator for frequency-selective mmWave channels considering frequency-dependent array responses. Further, we develop a suboptimal channel estimation strategy to consider the beam squint effect as well. Simulation results based on channel realizations extracted from Quadriga show the effectiveness of the proposed methods at the low Signal-to-Noise Ratio (SNR) regime without assuming the channel sparsity is known.

Channel estimation at millimeter wave (mmWave) allows designing hybrid precoders and combiners to maximize performance metrics such as the spectral efficiency. A major source of difficulty when estimating the channel is the carrier frequency offset. In this paper, we propose the first joint compressive - Maximum Likelihood (ML) algorithm for joint carrier frequency offset and channel estimation at mmWave. Simulation results show that small estimation errors and near-optimal values of spectral efficiency can be achieved without incurring in significant overhead and/or computational complexity.

Traffic monitoring is important in urban areas. Traffic sensing solutions based on static cameras, however, do not offer a flexible, inexpensive solution for short-term traffic studies. To overcome the limitations of static solutions, we propose an aerial traffic monitoring system. It uses unmanned aerial vehicles (UAV) and onboard cameras to capture traffic video, which is sent and processed in the cloud. To validate the proposed system, we implemented a prototype with a quadcopter, an onboard camera with video and data processing algorithms, and a web application. The video processing module includes a vehicle detection stage based on the Haar cascade model and a frame-by-frame tracking stage. After experimental testing based on videos collected by the UAV, we conclude that the designed system can monitor traffic with high accuracy and flexibility.

Millimeter wave (mmWave) communication is one feasible solution for high data-rate applications like vehicular-to-everything communication and next generation cellular communication. Configuring mmWave links, which can be done through channel estimation or beam-selection, however, is a source of significant overhead. In this paper, we propose using spatial information extracted at sub-6 GHz to help establish the mmWave link. Assuming a fully digital architecture at sub-6 GHz; and an analog architecture at mmWave, we outline a strategy to extract spatial information from sub-6 GHz and its use in mmWave compressed beam-selection. Specifically, we formulate compressed beam-selection as a weighted sparse signal recovery problem, and obtain the weighting information from sub-6 GHz channels. In addition, we outline a structured precoder/combiner design to tailor the training to out-of-band information. We also extend the proposed out-of-band aided compressed beam-selection approach to leverage information from all active subcarriers at mmWave. To simulate multi-band frequency dependent channels, we review the prior work on frequency dependent channel behavior and outline a multi-frequency channel model. The simulation results for achievable rate show that out-of-band aided beam-selection can considerably reduce the training overhead of in-band only beam-selection.

Channel estimation is useful in millimeter wave (mm-wave) MIMO communication systems. Channel state information allows optimized designs of precoders and combiners under different metrics, such as mutual information or signal-to-interference noise ratio. At mm-wave, MIMO precoders and combiners are usually hybrid, since this architecture provides a means to trade-off power consumption and achievable rate. Channel estimation is challenging when using these architectures, however, since there is no direct access to the outputs of the different antenna elements in the array. The MIMO channel can only be observed through the analog combining network, which acts as a compression stage of the received signal. Most of the prior work on channel estimation for hybrid architectures assumes a frequency-flat mm-wave channel model. In this paper, we consider a frequency-selective mm-wave channel and propose compressed sensing-based strategies to estimate the channel in the frequency domain. We evaluate different algorithms and compute their complexity to expose tradeoffs in complexity overhead performance as compared with those of previous approaches.

In this work we consider the problem of designing the precoders and combiners in a multiuser hybrid frequency selective millimeter wave (mmWave) system. We propose two methods to design the hybrid analog-digital filters when only an estimate of the channel is available at both the base station (BS) and the users. The first one is a projected gradient method that factorizes the all-digital solution, while the second approach is based on an alternating minimization (AM) iterative strategy. The channel is estimated using an algorithm that takes into account the hardware limitations of the hybrid architecture and leverages the sparse structure of mmWave channels. The proposed methods are evaluated in terms of achievable sum rate, outperforming the state of the art solutions. The negligible impact of the hybrid structure and the imperfect channel knowledge on the system performance are shown via numerical experiments.

Hybrid architectures reduce power consumption for MIMO systems at millimeter wave (mmWave) with respect to all-digital solutions. Fast configuration of hybrid arrays is one of the main challenges to be solved to unlock the potential of mmWave in high mobility scenarios. Channel estimation is one of the means of obtaining the information needed to configure these arrays. Prior work on channel estimation at mmWave considers a frequency-flat narrowband channel model, although the the mmWave channel is intrinsically wideband and frequency-selective. In this paper, we design a frequency-domain algorithm that leverages the sparsity in the channel to estimate the frequency-selective mmWave channel. We evaluate this technique by simulation and study its complexity, comparing the trade-offs complexity-overhead-performance to those of previous approaches. We conclude that the proposed approach outperforms previous algorithms, providing a better performance at a lower computational cost.

Millimeter wave (mmWave) systems will likely employ large antennas at both the transmitter and receiver for directional beamforming. Hybrid analog/digital MIMO architectures have been proposed previously for leveraging both array gain and multiplexing gain, while reducing the power consumption in analog-to-digital converters. Channel knowledge is needed to design the hybrid precoders/combiners, which is difficult to obtain due to the large antenna arrays and the frequency selective nature of the channel. In this paper, we propose a sparse recovery based time-domain channel estimation technique for hybrid architecture based frequency selective mmWave systems. The proposed compressed sensing channel estimation algorithm is shown to provide good estimation error performance, while requiring small training overhead. The simulation results show that using multiple RF chains at the receiver and the transmitter further reduces the training overhead.

Channel estimation allows millimeter wave (mmWave) MIMO communication systems to design pre-coders and combiners under different objective functions. Hybrid MIMO architectures provide a good trade-off power consumption-performance at mmWave frequencies, but most of the prior work on channel estimation for these structures assumes a narrowband channel model. In this paper, we propose a sparse approach for frequency selective channel estimation for mmWave channels, assuming a hybrid architecture. Simulation results show that the estimation error is small and the computational complexity is kept low. Moreover, the algorithm requires less training overhead than competing approaches based on beam training.

Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the hybrid precoders and combiners, though, is usually based on the knowledge of the channel. Prior work on mmWave channel estimation with hybrid architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for hybrid architectures-based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing-based solutions for the wideband mmWave channel estimation problem for hybrid architectures. First, we leverage the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both single carrier-frequency domain equalization and orthogonal frequency-division multiplexing systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the hybrid architecture at the transceivers gives further improvement in estimation error performance and achievable rates.

This paper considers the design of hybrid precoders and combiners for mmWave MIMO systems with per-antenna power constraints and the additional limitations introduced by the phase-shifting network in the analog processing stage. Previous hybrid designs were obtained using a total power constraint, but in practical implementations per-antenna constraints are more realistic, specially at mmWave, given the large number of power amplifiers used in the transmit array. Assuming perfect channel knowledge, we obtain first an approximation to the all-digital solution for the precoder and the combiner given the per-antenna constraints. Then, we develop a new method for the design of the hybrid precoder and combiner which attempts to match such all- digital approximation. Simulation results show the effectiveness of the proposed approach, which performs close to the all-digital solution.

This paper addresses the problem of designing thinned arrays with minimized side lobe levels for antenna selection in millimeter wave MIMO systems. We propose a new optimization solution based on compressed sensing techniques and convex optimization relaxation which we show to be a heuristic that solves the original binary optimization problem of side lobe level minimization. We compare the proposed method with other approaches from the literature like simulated annealing and genetic algorithms showing the superiority of the method in terms of performance, running time and ease of parameter tuning. The simulation results cover a wide range of dimensions and situations.

In this paper we analyze the mean squared error (MSE) for one-bit compressed sensing schemes based on measurement matrices that correspond to unit norm tight frames. We show that, as in the unquantized case, sensing with unit norm tight frames improves the MSE in the reconstruction of sparse vectors from one-bit measurements using l\ and thresholding algorithms. From our analytical and experimental results we conclude that when implementing one-bit compressed sensing schemes with fixed measurement matrices unit norm tight frames are the measurements of choice.

Millimeter wave (mmWave) communication provides high data rates thanks to large arrays at the transmitter and receiver, coupled with large bandwidth channels. Exploiting the arrays is challenging due to the need to configure precoding at the transmitter based on the large frequency selective channel. In this paper, we exploit the power iteration principle and propose a robust analog precoding training algorithm that can be applied in both frequency division duplex transmission systems and time division duplex transmission systems with or without RF calibration. We further analyze the convergence of the proposed algorithm and show how it converges to the singular value decomposition optimality exponentially. We propose null space projection on top of the power iteration to form multiple orthogonal beams at the transmitter and receiver. Strongest tap selection with proper energy pruning is used to collect as much precoding gain as possible from a frequency selective fading channel. The exponential effective SINR mapping performance is evaluated and demonstrates that the overall approach works smoothly. Numerical simulation results demonstrate algorithm robustness and the algorithm works not only for the simplified mmWave directional channels, but also for more general rich scattering channels.

Millimeter communication systems use large antenna arrays to provide good average received power and to take advantage of multi-stream MIMO communication. Unfortunately, due to power consumption in the analog front-end, it is impractical to perform beamforming and fully digital precoding at baseband. Hybrid precoding/combining architectures have been proposed to overcome this limitation. The hybrid structure splits the MIMO processing between the digital and analog domains, while keeping the performance close to that of the fully digital solution. In this paper, we introduce and analyze several algorithms that efficiently design hybrid precoders and combiners starting from the known optimum digital precoder/combiner, which can be computed when perfect channel state information is available. We propose several low complexity solutions which provide different trade-offs between performance and complexity. We show that the proposed iterative solutions perform better in terms of spectral efficiency and/or are faster than previous methods in the literature. All of them provide designs which perform close to the known optimal digital solution. Finally, we study the effects of quantizing the analog component of the hybrid design and show that even with coarse quantization, the average rate performance is good.

Multiple input multiple output (MIMO) systems employing large antenna arrays are the basic architecture for millimeter wave (mmWave) systems. Due to the higher bandwidths to be used at mmWave, the corresponding sampling rates of high-resolution analog-to-digital converters (ADCs) are also very high, so that ADCs become the most power hungry devices in the reception chain. One solution is to employ low resolution, i.e. one-bit, ADCs. We develop an adaptive one-bit compressed sensing scheme that can be used at low-resolution mmWave receivers for channel estimation. The simulation results show that the adaptive one-bit compressed sensing scheme outperforms the fixed one in the context of mmWave channel estimation.

High resolution direction-of-arrival (DOA) estimation is an important problem in many array signal processing applications. This paper proposes an augmented covariance estimator for DOA estimation. The new method exploits the periodicity of the covariance lags when the DOAs are assumed on a discrete grid with a certain resolution. Then, it achieves twice the resolution of typical methods such as the direct augmentable approach or forward backward spatial smoothing. When the sources are not on the discrete grid, an interpolated array manifold technique is proposed to mitigate the grid mismatch error.

Antenna subset modulation (ASM) is a physical layer security technique that is well suited for millimeter wave communication systems. The key idea is to vary the radiation pattern at the symbol rate by selecting one from a subset of patterns with a similar main lobe and different side lobes. This paper shows that ASM is not robust to an eavesdropper that makes multiple simultaneous measurements at multiple angles. The measurements are combined and used to formulate an estimation problem to undo the effects of the side lobe randomization. Simulations show the performance of the estimation algorithms and how the eavesdropper can effectively recover the information if the signal-to-noise ratio exceeds a certain threshold. Using fewer active radio frequency chains makes it harder for the attacker to recover the transmit symbol, at the expense of more grating lobes.

Millimeter wave (mmWave) multiple-input multipleoutput (MIMO) communication with large antenna arrays has been proposed to enable gigabit per second communication for next generation cellular systems and local area networks. A key difference relative to lower frequency solutions is that in mmWave systems, precoding/combining can not be performed entirely at digital baseband, due to the high cost and power consumption of some components of the radio frequency (RF) chain. In this paper we develop a low complexity algorithm for finding hybrid precoders that split the precoding/combining process between the analog and digital domains. Our approach exploits sparsity in the received signal to formulate the design of the precoder/combiners as a compressed sensing optimization problem. We use the properties of the matrix containing the array response vectors to find first an orthonormal analog precoder, since sparse approximation algorithms applied to orthonormal sensing matrices are based on simple computations of correlations. Then, we propose to perform a local search to refine the analog precoder and compute the baseband precoder. We present numerical results demonstrate substantial improvements in complexity while maintaining good spectral efficiency.

Millimeter wave (mmWave) technology is widely used for automotive radar applications, like adaptive cruise control and obstacle detection. Unlike conventional radar waveforms which are usually propriety, this paper explores the use of a consumer wireless local area network (WLAN) waveform in the 60GHz unlicensed mmWave band for automotive radar applications. In particular, this paper develops a joint framework of long range automotive radar (LRR) and vehicle-to-vehicle communication (V2V) at 60 GHz by exploiting the special data-aided structure (repeated Golay complimentary sequences) of an IEEE 802.11ad single carrier physical layer (SCPHY) frame. This framework leverages the signal processing algorithms used in the typical WLAN receiver for time and frequency synchronization to perform radar parameter estimation. The initial simulation results show that it is possible to achieve the desired range accuracy of 0.1 m with a very high probability of detection (above 99%) using the preamble of a SCPHY frame. Furthermore, the velocity estimation algorithm achieves the desired accuracy of 0.1 m/s at high SNR using the preamble and pilot words of only a single frame.

One of the main challenges in cognitive radio systems is the development of fast algorithms for spectrum sensing of wideband signals. To alleviate sampling requirements, several strategies have been proposed, relying on the fact that only covariance information is of interest. Under this premise, this paper solves the design of low rate covariance samplers by developing a multicoset sampling strategy based on co-prime samplers, achieving higher compression ratios than previous solutions. The designed sampling patterns constitute a family of circular sparse rulers, which can be implemented with only two analog-to-digital converters working in parallel, and operating at sampling frequencies significantly lower than the Nyquist rate.

We consider the design of a multicoset sampling pattern to be used in power spectrum blind sampling (PSBS). The criterion for the PSBS pattern design that we propose is based on the minimization of the mean square error of the power spectrum estimate. The design framework appears as a constrained optimization problem, whose complexity increases with the pattern length. We solve such a constrained optimization problem in terms of nonlinear integer programming by using exhaustive search.

Many problems in digital communications involve wideband radio signals. As the most recent example, the impressive advances in Cognitive Radio systems make even more necessary the development of sampling schemes for wideband radio signals with spectral holes. This is equivalent to considering a sparse multiband signal in the framework of Compressive Sampling theory. Starting from previous results on multicoset sampling and recent advances in compressive sampling, we analyze the matrix involved in the corresponding reconstruction equation and define a new method for the design of universal multicoset codes, that is, codes guaranteeing perfect reconstruction of the sparse multiband signal.

In Cognitive Radio scenarios channelization information from primary network may be available to the spectral monitor. Under this assumption we propose a spectral estimation algorithm from compressed measurements of a multichannel wideband signal. The analysis of the Cramer-Rao Lower Bound (CRLB) for this estimation problem shows the importance of detecting the underlaying sparsity pattern of the signal. To this end we describe a Bayesian based iterative algorithm that discovers the set of active signals conforming the band and simultaneously reconstructs the spectrum. This iterative spectral estimator is shown to perform close to a Genie-Aided CRLB that includes full knowledge about the sparsity pattern of the channels.

This work is concerned with subband processing of finite length signals using tree-structured filter banks. In many applications the subband transform is required to be orthogonal. For this purpose, the filter bank must be paraunitary, and the signal borders must be processed via either orthogonal boundary filters or via orthogonal signal extension methods. Periodization is an orthogonal extension technique, but introduces undesired artificial discontinuities or spurious high frequencies in the transform vector. Boundary filter design techniques do not solve this problem either. The solution we provide is a new algorithm for the generation of alternative orthogonal signal extensions which do not introduce spurious high frequencies in the subband signals. Some experimental results illustrate the effectiveness of the proposed design method in comparison to other existing techniques.

Signal extension methods have been extensively used when applying filter banks to finite length sequences. However, not every extension guarantees perfect reconstruction without considering extra subband samples, that is, in some cases the associated transform is expansionist. In this paper we introduce first the most general method for designing signal extensions which yield non-expansionist subband transforms. Secondly, all orthogonal extension methods are constructed. We conclude by analyzing some examples of the new boundary filters associated to the proposed extensions.

In this paper we introduce a novel and general matrix formulation of classical signal extension methods for subband processing of finite length signals. Considering a paraunitary 2-channel filter bank as a transformation cell, this new characterization makes it possible to show that perfect reconstruction of finite signals can be ensured without resorting to extra subband samples; thus, by using some traditional signal extension methods, non-expansionist transforms can be defined. Some of these transformations are analyzed to illustrate our theoretical results.

A segmentation procedure of time sequences based on a time-frequency analysis is presented. The use of both a wavelet packet transform and the original time signal provides a set of spectral and time parameters that allows the algorithm to locate some proper break points to split the input frame into a discrete number of smaller segments. Some examples showing the performance of the method are also presented. An application to wavelet-based audio coding is also discussed.

A new bit assignment algorithm is presented. Its goals are the simultaneous assignment on all subbands in a few steps of an iterative calculus, the use of memory to achieve a better speed of convergence and the consideration of a deformable error curve. The basis of the algorithm is discussed and also other considerations that are likely to arise in practice. Finally, an example of its performance is given.