Measuring radar and communication congruence at millimeter wave frequencies

Graff, Andrew; González–Prelcic, Nuria

doi:10.1109/ieeeconf44664.2019.9048969

Cited by 13 publications

(4 citation statements)

References 15 publications

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“…Co-locating sensing with the infrastructure has many advantages for automation applications like vehicular communication systems [46]. The core idea is that there is congruence between the multipath channels and the radar channels as validated through analysis, simulations and measurements [46], [53], so that information from the radar can be exploited to reduce CSI or beam training overheads. Of course, the radar also provides a more accurate source of position, which can add in position-aided beam training methods.…”

Section: Maximal Likelihoodmentioning

confidence: 99%

An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing

Zhang

Liu

Masouros

et al. 2021

IEEE J. Sel. Top. Signal Process.

531

152

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Joint communication and radar sensing (JCR) represents an emerging research field aiming to integrate the above two functionalities into a single system, by sharing the majority of hardware, signal processing modules and, in a typical case, the transmitted signal. The close cooperation of the communication and sensing functions can enable significant improvement of spectrum efficiency, reduction of device size, cost and power consumption, and improvement of performance of both functions. Advanced signal processing techniques are critical for making the integration efficient, from transmission signal design to receiver processing. This paper provides a comprehensive overview of the state-of-the-art on JCR systems from the signal processing perspective. A balanced coverage on both transmitter and receiver is provided for three types of JCR systems, namely, communicationcentric, radar-centric, and joint design and optimization.

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Section: Maximal Likelihoodmentioning

confidence: 99%

An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing

Zhang

Liu

Masouros

et al. 2021

IEEE J. Sel. Top. Signal Process.

531

152

View full text Add to dashboard Cite

show abstract

“…Ref. [28] analyzes spatial consistency characteristics between communication and sensing channels based on measurements with a vehicle-mounted radar, which only focuses on power spectrum analysis. In [29], communication and sensing indoor channel measurement is conducted at 28 GHz, capturing shared features between communication and sensing channels.…”

Section: A Related Workmentioning

confidence: 99%

A General Channel Model for Integrated Sensing and Communication Scenarios

Zhang

et al. 2023

IEEE Commun. Mag.

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The emerging 6G network envisions integrated sensing and communication (ISAC) as a promising solution to meet growing demand for native perception ability. To optimize and evaluate ISAC systems and techniques, it is crucial to have an accurate and realistic wireless channel model. However, some important features of ISAC channels have not been well characterized, for example, most existing ISAC channel models consider communication channels and sensing channels independently, whereas ignoring correlation under the consistent environment. Moreover, sensing channels have not been well modeled in the existing standard-level channel models. Therefore, in order to better model ISAC channel, a cluster-based statistical channel model is proposed in this paper, which is based on measurements conducted at 28 GHz. In the proposed model, a new framework based on 3GPP standard is proposed, which includes communication clusters and sensing clusters. Clustering and tracking algorithms are used to extract and analyze ISAC channel characteristics. Furthermore, some special sensing cluster structures such as shared sensing cluster, newborn sensing cluster, etc., are defined to model correlation and difference between communication and sensing channels. Finally, accuracy of the proposed model is validated based on measurements and simulations.

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“…Position information obtained with a radar unit at the road infrastructure was also used in [23] to reduce the overhead of the beam training protocol. The accuracy of position information provided by radar is higher than that provided by GPS, what leads to a larger reduction in communication overhead when exploiting position information provided by a radar sensor than when leveraging GPS-based position, as shown in the field measurements provided in [24]. Although all these approaches based on an active radar provide an interesting reduction of the link configuration overhead, they only perform well in LOS scenarios.…”

Section: B Prior Workmentioning

confidence: 99%

Deep Learning-based Link Configuration for Radar-aided Multiuser mmWave Vehicle-to-Infrastructure Communication

Graff¹,

Chen²,

Nuria³

et al. 2022

Preprint

Self Cite

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Configuring millimeter wave links following a conventional beam training protocol, as the one proposed in the current cellular standard, introduces a large communication overhead, specially relevant in vehicular systems, where the channels are highly dynamic. In this paper, we propose the use of a passive radar array to sense automotive radar transmissions coming from multiple vehicles on the road, and a radar processing chain that provides information about a reduced set of candidate beams for the links between the road-infrastructure and each one of the vehicles. This prior information can be later leveraged by the beam training protocol to significantly reduce overhead. The radar processing chain estimates both the timing and chirp rates of the radar signals, isolates the individual signals by filtering out interfering radar chirps, and estimates the spatial covariance of each individual radar transmission. Then, a deep network is used to translate features of these radar spatial covariances into features of the communication spatial covariances, by learning the intricate mapping between radar and communication channels, in both line-of-sight and non-line-ofsight settings. The communication rates and outage probabilities of this approach are compared against exhaustive search and pure radar-aided beam training methods (without deep learningbased mapping), and evaluated on multi-user channels simulated by ray tracing. Results show that: (i) the proposed processing chain can reliably isolate the spatial covariances for individual radars, and (ii) the radar-to-communications translation strategy based on deep learning provides a significant improvement over pure radar-aided methods in both LOS and NLOS channels.

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Measuring radar and communication congruence at millimeter wave frequencies

Cited by 13 publications

References 15 publications

An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing

An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing

A General Channel Model for Integrated Sensing and Communication Scenarios

Deep Learning-based Link Configuration for Radar-aided Multiuser mmWave Vehicle-to-Infrastructure Communication

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