This work proposes a maximum likelihood-based parameter estimation framework for a multistatic millimeter wave integrated sensing and communication system using energy-efficient hybrid digital-analog arrays. Due to the typically large arrays used in the higher frequency bands to mitigate isotropic path loss, such arrays may operate in the near-field (NF) regime. To address this, we propose a two-step estimation process. Initially, we consider far-field (FF) propagation assumptions, followed by refined estimation based on NF assumptions, enhancing accuracy when the target is within the NF of the arrays. In particular, when operating in the NF of the transmitter (Tx), we select beamfocusing array weights designed to achieve constant gain over an extended spatial region. Subsequently, we re-estimate target parameters at the receivers (Rxs). The effectiveness of the proposed framework is evaluated over various scenarios through numerical simulations. In particular, the impact of customdesigned flat-gain beamfocusing codewords in improving both communication and sensing performance when the target is in the NF of the Tx is demonstrated. Additionally, the benefit of considering a correct NF channel model when the target is located near an Rx is shown.

This work proposes a maximum likelihood (ML)- based parameter estimation framework for a millimeter wave (mmWave) integrated sensing and communication (ISAC) system in a multistatic configuration using energy-efficient hybrid digital-analog (HDA) arrays. Due to the typically large arrays deployed in the higher frequency bands to mitigate isotropic path loss, such arrays may operate in the near-field (NF) regime. The proposed parameter estimation in this work consists of a two-stage estimation process, where the first stage is based on far-field (FF) assumptions, and is used to obtain a first estimate of the target parameters. In cases where the target is determined to be in the NF of the arrays, a second estimation based on NF assumptions is carried out to obtain more accurate estimates. In particular, when operating in the near-filed of the transmitter (Tx), we select beamfocusing array weights designed to achieve a constant gain over an extended spatial region and re-estimate the target parameters at the receivers (Rxs). We evaluate the effectiveness of the proposed framework in numerous scenarios through numerical simulations and demonstrate the impact of the custom-designed flat-gain beamfocusing codewords in increasing the communication performance of the system.

In this work, we propose a framework based on Deep Neural Networks (DNNs) for radar parameter estimation in an Integrated Sensing and Communication (ISAC) system em-ploying a realistic and hardware-efficient Hybrid Digital-Analog (HDA) architecture that uses Orthogonal Frequency Division Multiplexing (OFDM) digital modulation. This framework takes raw signals as input and utilizes a Variational Autoencoder (VAE) followed by a regression network to output the spatial extent and location of extended targets. Owing to the HDA setup, the co-located radar receiver uses multi-block measurements to perform parameter estimation. The proposed solution is motivated as a remedy for the increasing computational complexity associated with high-resolution extended target estimation in multi-carrier digital modulations such as OFDM. In addition, it is well known that off-grid delay-Doppler shifts which are present in the doubly-dispersive channels in the high mobility scenarios expected in ISAC applications, exhibit leakage effects that adversely affect the parameter estimation performance. Due to the data-centric nature of the proposed method, these effects can be learned by the network. We provide numerical results to showcase the effectiveness of the proposed framework for parameter estimation. 1

In this work, we propose a compressed sensing framework for radar parameter estimation in an Integrated Sensing and Communication (ISAC) system employing a realistic and hardware-efficient Hybrid Digital-Analog (HDA) architecture which uses Orthogonal Time Frequency Space (OTFS) digital modulation. In such a setup, the co-located radar receiver uses multi-block measurements to perform parameter estimation. OTFS is widely considered as a robust modulation to deal with the doubly-dispersive channel in the high mobility scenarios expected in ISAC applications, however it suffers from leakage effects in the presence of fractional Doppler/delay (i.e., off-grid) shifts. By taking the inherent structure of the leakage effect into consideration and casting the multi-block measurements in a Multiple Measurement Vector (MMV) setting, we develop the Joint Hierarchical Sparsity concept based on which, we formulate a soft-thresholding iterative parameter estimation framework. This framework exploits the jointly hierarchical structure of the MMV setting for improved (radar-) parameter estimation. We provide numerical results to showcase the effectiveness of the proposed framework for parameter estimation.

In this paper, we develop two active sensing strategies for a millimeter wave (mmWave) band Integrated Sensing and Communication (ISAC) system adopting a realistic hybrid digital-analog (HDA) architecture. To maintain a desired SNR level, initial beam acquisition (BA) must be established prior to data transmission. In the considered setup, a Base Station (BS) transmitter (Tx) transmits data via a digitally modulated waveform and a co-located radar receiver simultaneously performs radar estimation from the backscattered signal. In this BA scheme a single common data stream is broadcast over a wide angular sector such that the radar receiver can detect the presence of not yet acquired users and perform coarse parameter estimation (angle of arrival, time of flight, and Doppler). As a result of the HDA architecture, we consider the design of multi-block adaptive RF-domain "reduction matrices" (from antennas to RF chains) at the radar receiver, to achieve a compromise between the exploration capability in the angular domain and the directivity of the beamforming patterns. Our numerical results demonstrate that the proposed approaches are able to reliably detect multiple targets while significantly reducing the initial acquisition time.

Motivated by automotive applications, we consider joint radar sensing and data communication for a system operating at millimeter wave (mmWave) frequency bands, where a Base Station (BS) is equipped with a co-located radar receiver and sends data using the Orthogonal Time Frequency Space (OTFS) modulation format. We consider two distinct modes of operation. In Discovery mode, a single common data stream is broadcast over a wide angular sector. The radar receiver must detect the presence of not yet acquired targets and performs coarse estimation of their parameters (angle of arrival, range, and velocity). In Tracking mode, the BS transmits multiple individual data streams to already acquired users via beamforming, while the radar receiver performs accurate estimation of the aforementioned parameters. Due to hardware complexity and power consumption constraints, we consider a hybrid digital-analog architecture where the number of RF chains and A/D converters is significantly smaller than the number of antenna array elements. In this case, a direct application of the conventional MIMO radar approach is not possible. Consequently, we advocate a beam-space approach where the vector observation at the radar receiver is obtained through a RF-domain beamforming matrix operating the dimensionality reduction from antennas to RF chains. Under this setup, we propose a likelihood function-based scheme to perform joint target detection and parameter estimation in Discovery, and high-resolution parameter estimation in Tracking mode, respectively. Our numerical results demonstrate that the proposed approach is able to reliably detect multiple targets while closely approaching the Cramér-Rao Lower Bound (CRLB) of the corresponding parameter estimation problem.

Intelligent reflecting surfaces (IRS) are a novel technology envisaged to significantly improve the performance of next generation wireless communication networks, utilizing passive reflecting elements arranged in planar arrays to reconfigure the wireless propagation environment. This study investigates the use of Intelligent Reflecting Surfaces for Vulnerable Road Users (VRU) such as pedestrians, bicycles, and wheelchair users. This can be made possible by recent advances in IRS technology and can significantly improve the radar visibility of VRUs. In this work we propose a potential use case for IRS which aims to improve the detection of traffic users by automotive radar irrespective of the object's orientation which may severely impact its observable radar cross section. Furthermore, this approach can be extended to form a network where multiple radar sensors can become aware of a VRU's presence even in cases where the users have not been directly observed by the respective sensor. Numerical results are provided to show that the proposed approach can enhance the radar detection capability of VRUs and can help to overcome the challenges due to the orientation-dependent radar cross section of targets.