Next-generation vehicular networks are expected to provide the capability of robust environmental sensing in addition to reliable communications to meet intelligence requirements. A promising solution is the integrated sensing and communication (ISAC) technology, which performs both functionalities using the same spectrum and hardware resources. Most existing works on ISAC consider the Orthogonal Frequency Division Multiplexing (OFDM) waveform. Nevertheless, vehicle motion introduces Doppler shift, which breaks the subcarrier orthogonality and leads to performance degradation. The recently proposed Orthogonal Time Frequency Space (OTFS) modulation, which exploits various advantages of Delay Doppler (DD) channels, has been shown to support reliable communication in high-mobility scenarios. Moreover, the DD waveform can directly interact with radar sensing parameters, which are actually delay and Doppler shifts. This paper investigates the advantages of applying the DD communication waveform to ISAC. Specifically, we first provide a comprehensive overview of implementing DD communications, based on which several advantages of DD-ISAC over OFDM-based ISAC are revealed, including transceiver designs and the ambiguity function. Furthermore, a detailed performance comparison are presented, where the target detection probability and the mean squared error (MSE) performance are also studied. Finally, some challenges and opportunities of DD-ISAC are also provided.

In this paper, we present our radio frequency signal denoising approach, RFDEMUCS,1 for the 2024 IEEE ICASSP RF Signal Separation Challenge. Our approach is based on the DE-MUCS architecture [1], and has a U-Net structure with a bidirectional LSTM bottleneck. For the task of estimating the underlying bit-sequence message, we also propose an extension of the DEMUCS that directly estimates the bits. Evaluations of the presented methods on the challenge test dataset yield MSE and BER scores of -118.71 and -81, respectively, according to the evaluation metrics defined in the challenge.

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 custom-designed 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 multi-static configuration using energy-efficient hybrid digital-analog 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 regime. The proposed parameter estimation in this work consists of a two-stage estimation process, where the first stage is based on far-field assumptions, and is used to obtain a first estimate of the target parameters. In cases where the target is determined to be in the near-field of the arrays, a second estimation based on near-field assumptions is carried out to obtain more accurate estimates. In particular, 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. 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 employing 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 a typical communication system, in order to maintain a desired signal-to-noise ratio (SNR) level, initial beam alignment (BA) must be established prior to data transmission. In a setup where a base station (BS) transmitter (Tx) sends data via a digitally modulated waveform, we propose an user equipment (UE) enhanced with an hybrid-intelligent reflective surface (HIRS) to aid beam alignment. A novel multi-slot estimation scheme is developed that alleviates the restrictions imposed by the hybrid digital-analog (HDA) architecture of the HIRS and the BS. To demonstrate the effectiveness of the proposed BA scheme, we derive the Cramér-Rao lower bound (CRLB) of the parameter estimation scheme and provide numerical results.

We consider radar parameter estimation for multi-scatterer targets with an Integrated Sensing and Communication (ISAC) system employing a Hybrid Digital-Analog (HDA) architecture and Orthogonal Time Frequency Space (OTFS) digital modulation. To deal with the beamforming restrictions imposed by the HDA architecture, we investigate parameter estimation under a multi-block observation scheme and formulate this as two distinct variations of the Multiple Measurement Vector (MMV) framework. The Fast-Iterative Shrinkage Thresholding Algorithm (FISTA) is then used to solve the problem. Our numerical results demonstrate that using this technique, it is possible to reliably resolve a multi-scatter-point target and obtain an estimation of the target’s geometric extent, which can be used for post-processing requirements such as tracking or orientation estimation.

In this work, we propose a waveform based on Modulation on Conjugate-reciprocal Zeros (MOCZ) originally proposed for short-packet communications in [1], as a new Integrated Sensing and Communication (ISAC) waveform. Having previously established the key advantages of MOCZ for noncoherent and sporadic communication, here we leverage the optimal auto-correlation property of Binary MOCZ (BMOCZ) for sensing applications. Due to this property, which eliminates the need for separate communication and radar-centric waveforms, we propose a new frame structure for ISAC, where pilot sequences and preambles become obsolete and are completely removed from the frame. As a result, the data rate can be significantly improved. Aimed at (hardware-) cost-effective radar-sensing applications, we consider a Hybrid Digital-Analog (HDA) beamforming architecture for data transmission and radar sensing. We demonstrate via extensive simulations, that a communication data rate, significantly higher than existing standards can be achieved, while simultaneously achieving sensing performance comparable to state-of-the-art sensing systems.

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.

We investigate radar parameter estimation and beam tracking with a hybrid digital-analog (HDA) architecture in a multi-block measurement framework using an extended target model. In the considered setup, the backscattered data signal is utilized to predict the user position in the next time slots. Specifically, a simplified maximum likelihood framework is adopted for parameter estimation, based on which a simple tracking scheme is also developed. Furthermore, the proposed framework supports adaptive transmitter beamwidth selection, whose effects on the communication performance are also studied. Finally, we verify the effectiveness of the proposed framework via numerical simulations over complex motion patterns that emulate a realistic integrated sensing and communication (ISAC) scenario.

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.

Motivated by recent advances of Integrated Sensing and Communication (ISAC), we study an ISAC system operating at millimeter waves (mmWave) frequency bands where a Base Station (BS) equipped with a co-located radar receiver transmits data via a digitally modulated orthogonal time frequency space (OTFS) waveform and simultaneously performs radar estimation from the backscattered signal. We consider two system function modes. In Discovery mode, a single common data stream is broadcast over a wide angular sector where the radar receiver detects the presence of not yet acquired targets and performs coarse parameter estimation (angle of arrival, delay, and Doppler). In Tracking mode, the BS sends multiple individual data streams to already acquired users via beamforming, while the radar receiver performs fine-resolution parameter estimation. In this work a realistic hybrid digital-analog scheme for RF beamforming at mmWave is considered, where the number of RF chains for modulation/demodulation is significantly smaller than the number of array antenna elements. Hence, a direct application of standard MIMO radar approaches is not possible. Instead, we consider the design of the RF-domain “reduction matrix” (from antennas to RF chains) of the radar receiver, whose role is to trade off between the exploration capability of the angle domain and the directivity of the beamforming patterns. Under this setup, we propose an efficient maximum likelihood scheme to jointly perform target detection and parameter estimation. Our numerical results demonstrate that the proposed approach is able to reliably detect multiple targets while essentially achieving the Cramér-Rao lower bound for parameter estimation.

In this paper, we develop an active sensing strategy 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) 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 approach is able to reliably detect multiple targets while significantly reducing the initial acquisition time.

In this paper, we develop a beam tracking scheme for an orthogonal frequency division multiplexing (OFDM) Integrated Sensing and Communication (ISAC) system with a hybrid digital analog (HDA) architecture operating in the millimeter wave (mmWave) band. Our tracking method consists of an estimation step inspired by radar signal processing techniques, and a prediction step based on simple kinematic equations. The hybrid architecture exploits the predicted state information to focus only on the directions of interest, trading off beamforming gain, hardware complexity and multistream processing capabilities. Our extensive simulations in arbitrary trajectories show that the proposed method can outperform state of the art beam tracking methods in terms of prediction accuracy and consequently achievable communication rate, and is fully capable of dealing with highly non-linear dynamic motion patterns.

Millimeter wave (mmWave) and sub-TeraHertz (sub-THz) multi-user multiple-input multiple-output (MUMIMO) communications operating in the frequency spectrum (30-300 GHz) have already been identified as the most promising candidate for the second phase of 5G and Beyond (5GB) wireless systems aiming to achieve broadband data communications at rates higher than 1 Gb/s while operating in very dense urban small-cell environments. Due to the presence of strong isotropic pathloss in mmWave/sub-THz frequencies, high antenna gains realized through large antenna arrays will be required to mitigate these effects. The small wavelengths under which mmWave and s-THz systems operate, allow for integration of more compact antenna elements. However, the fabrication of such antennas poses challenges such as positioning tolerance. In this work, we introduce a novel modular array synthesis approach and further investigate the effect of non-ideal element/module positions within an array and provide an optimization framework to minimize the expected synthesized array pattern error taking positioning tolerances into account. Furthermore, we use the realized element pattern of the proposed D-Band patch element with an RF bandwidth of over 10 GHz to demonstrate the results of the proposed optimization algorithm.

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 aimed at improving the detection of traffic users by automotive radar irrespective of the user's orientation which may severely impact its observable radar cross section. Furthermore, numerical results are provided to show the proposed approach can enhance the radar detection capability of VRUs and make the orientation-dependent radar cross section of targets a less significant challenge.

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.

The performance of millimeter wave (mmWave) communications critically depends on the accuracy of beam- forming both at base station (BS) and user terminals (UEs) due to high isotropic path-loss and channel attenuation. In high mobility environments, accurate beam alignment becomes even more challenging as the angles of the BS and each UE must be tracked reliably and continuously. In this work, focusing on the beamforming at the BS, we propose an adaptive method based on Recurrent Neural Networks (RNN) that tracks and predicts the Angle of Departure (AoD) of a given UE. Moreover, we propose a modified frame structure to reduce beam alignment overhead and hence increase the communication rate. Our numerical experiments in a highly non-linear mobility scenario show that our proposed method is able to track the AoD accurately and achieve higher communication rate compared to more traditional methods such as the particle filter.

The challenges for sensors and their correlated perception algorithms for driverless vehicles are tremendous. They have to provide more comprehensively than ever before a model of the complete static and dynamic surroundings of the ego-vehicle to understand the correlation of both with reference to the ego-vehicle’s movement. For dynamic objects, this means that radar has to provide the dimension and complete motion state as well as the class information, in highway, rural, and inner city scenarios. For the static world, new algorithm schemes have to be developed to enhance the shape representation of an object by image like semantics. In order to generate the necessary information, radar networking for 360° coverage have to be reinvented. Radar data processing toolchains have to be revolutionized by applying artificial intelligence and advanced signal processing in a synergetic manner.