High-resolution signal processing for a switch antenna array FMCW radar with a single channel receiver

Katkovnik, Vladimir; Lee, M.S.; Kim, Y.H.

doi:10.1109/sam.2002.1191099

Cited by 16 publications

(7 citation statements)

References 3 publications

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“…We can model the propagation delay as the sum of two components: one accounting for common distance τ and another accounting for the difference among antenna elements at the ULA τ n . This delay at antenna n is described as τ n,rm = τ rm + τ n,rm [29]. We assume our ULA has halfwavelength spacing and that the signal from radar m and path r m arrives at an angle of θ rm , so…”

Section: Radar System Modelmentioning

confidence: 99%

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

Graff¹,

Chen²,

Nuria³

et al. 2022

Preprint

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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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Section: Radar System Modelmentioning

confidence: 99%

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

Graff¹,

Chen²,

Nuria³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…We can model the propagation delay as the sum of two components: one accounting for common distance τ and another accounting for the difference among antenna elements at the ULA τ n . This delay at antenna n is described as τ n = τ + τ n [10]. We assume our ULA has half-wavelength spacing, so τ n = sin θ(n−1) 2fr .…”

Section: Radar System Modelmentioning

confidence: 99%

Radar Aided mmWave Vehicle-to-InfrastructureLink Configuration Using Deep Learning

Chen¹,

Graff²,

González–Prelcic³

et al. 2021

Preprint

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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.

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“…With τ being the delay due to distance, and τ n being the additional delay, we can write the delay of the transmit waveform on antenna n as τ n = τ + τ n [36].…”

Section: A Perfect Waveform Knowledge At the Receivermentioning

confidence: 99%

Passive Radar at the Roadside Unit to Configure Millimeter Wave Vehicle-to-Infrastructure Links

Nuria¹,

Ghosh²

2019

Preprint

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Millimeter wave (mmWave) vehicular channels are highly dynamic, and the communication link needs to be reconfigured frequently. In this work, we propose to use a passive radar receiver at the roadside unit to reduce the training overhead of establishing an mmWave communication link. Specifically, the passive radar will tap the transmissions from the automotive radars of the vehicles on the road. The spatial covariance of the received radar signals will be estimated and used to establish the communication link. We propose a simplified radar receiver that does not require the transmitted waveform as a reference. To leverage the radar information for beamforming, the radar azimuth power spectrum (APS) and the communication APS should be similar. We outline a radar covariance correction strategy to increase the similarity between the radar and communication APS. We also propose a metric to compare the similarity of the radar and communication APS that has a connection with the achievable rate. We present simulation results based on ray-tracing data. The results show that: (i) covariance correction improves the similarity of radar and communication APS, and (ii) the radar-assisted strategy significantly reduces the training overhead, being particularly useful in non-line-of-sight scenarios.

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High-resolution signal processing for a switch antenna array FMCW radar with a single channel receiver

Cited by 16 publications

References 3 publications

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

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

Radar Aided mmWave Vehicle-to-InfrastructureLink Configuration Using Deep Learning

Passive Radar at the Roadside Unit to Configure Millimeter Wave Vehicle-to-Infrastructure Links

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