Beam Alignment and Tracking for Autonomous Vehicular Communication using IEEE 802.11ad-based Radar

Muns, Guillem Reus; Mishra, Kumar Vijay; Guerra, Carlos Bocanegra; C., Eldar, Yonnina; Chowdhury, Kaushik R.

doi:10.48550/arxiv.1712.02453

Cited by 3 publications

(7 citation statements)

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“…An alternative strategy, which can be considered as an extreme case of shared communication waveforms, is to exploit the frame structure in existing communication protocols, utilizing them as an automotive radar waveform. Here, there is no compromise in the communications part, and the radar functionality is a byproduct of the protocol, which is typically IEEE 802.11p or IEEE 802.11ad [21]- [23], [46]- [49].…”

Section: Protocol-oriented Strategiesmentioning

confidence: 99%

“…Its large bandwidth enables higher data rates for communications, and better accuracy/resolution for radar operation. In order to avoid the usage of data-dependent waveforms, it has been proposed to utilize the a-priori known IEEE 802.11ad preamble for radar probing [22], [23], [46]. As the preamble now affects radar performance, the work [47] studied the design of radar suitable preamble sequences.…”

Section: Protocol-oriented Strategiesmentioning

confidence: 99%

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Joint Radar-Communications Strategies for Autonomous Vehicles

Ma,

Shlezinger,

Huang

et al. 2019

Preprint

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Self-driving cars constantly asses their environment in order to choose routes, comply with traffic regulations, and avoid hazards. To that aim, such vehicles are equipped with wireless communications transceivers as well as multiple sensors, including automotive radars. The fact that autonomous vehicles implement both radar and communications motivates designing these functionalities in a joint manner.Such dual function radar-communications (DFRC) designs are the focus of a large body of recent works. These approaches can lead to substantial gains in size, cost, power consumption, robustness, and performance, especially when both radar and communications operate in the same range, which is the case in vehicular applications. This article surveys the broad range of DFRC strategies and their relevance to autonomous vehicles. We identify the unique characteristics of automotive radar technologies and their combination with wireless communications requirements of self-driving cars. Then, we map the existing DFRC methods along with their pros and cons in the context of autonomous vehicles, and discuss the main challenges and possible research directions for realizing their full potential.

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Section: Protocol-oriented Strategiesmentioning

confidence: 99%

Section: Protocol-oriented Strategiesmentioning

confidence: 99%

Joint Radar-Communications Strategies for Autonomous Vehicles

Ma,

Shlezinger,

Huang

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…In [24], [25], the location of the vehicle was estimated using radar, and later this information was used to reduce the mmWave training overhead. In [26], a joint communication-radar system based on IEEE 802.11ad physical layer frames was proposed. Once the location of the vehicle was determined through the radar operation, it was used to reduce the training overhead.…”

Section: B Prior Workmentioning

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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“…For uniform waveform design, the CRB expression in (12) exists when the target count K is smaller than the number of preambles M U . Additionally, CRB in (12) reduces to zero as SNR goes to infinity [39].…”

Section: A Uniform Waveform Designmentioning

confidence: 99%

“…To enhance velocity estimation resolution, [11] investigated the possibility of increasing radar integration time by developing velocity estimation algorithms that exploit multiple fixed-length IEEE 802.11ad SC PHY frames. Similar velocity enhancement techniques were used in [12] that proposed an IEEE 802.11ad media access control configuration to accommodate radar operations for vehicle-to-infrastructure applications.…”

mentioning

confidence: 99%

Adaptive Virtual Waveform Design for Millimeter-Wave Joint Communication-Radar

Kumari,

Vorobyov,

Heath

2019

Preprint

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Joint communication and radar (JCR) waveforms with fully digital baseband generation and processing can now be realized at the millimeter-wave (mmWave) band. Prior work has proposed a mmWave wireless local area network (WLAN)-based JCR that exploits the WLAN preamble for radars. The performance of target velocity estimation, however, was limited. In this paper, we propose a virtual waveform design for an adaptive mmWave JCR. The proposed system transmits a few non-uniformly placed preambles to construct several receive virtual preambles for enhancing velocity estimation accuracy, at the cost of only a small reduction in the communication data rate. We evaluate JCR performance tradeoffs using the Cramer-Rao Bound (CRB) metric for radar estimation and a novel distortion minimum mean square error (MMSE) metric for data communication. Additionally, we develop three different MMSE-based optimization problems for the adaptive JCR waveform design. Simulations show that an optimal virtual (non-uniform) waveform achieves a significant performance improvement as compared to a uniform waveform. For a radar CRB constrained optimization, the optimal radar range of operation and the optimal communication distortion MMSE (DMMSE) are improved. For a communication DMMSE constrained optimization with a high DMMSE constraint, the optimal radar CRB is enhanced. For a weighted MMSE average optimization, the advantage of the virtual waveform over the uniform waveform is increased with decreased communication weighting. Comparison of MMSE-based optimization with traditional virtual preamble count-based optimization indicated that the conventional solution converges to the MMSE-based one only for a small number of targets and a high signal-to-noise ratio.Preeti Kumari and Robert W. Heath Jr. are with the Wireless Networking and Communications Group, the

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Beam Alignment and Tracking for Autonomous Vehicular Communication using IEEE 802.11ad-based Radar

Cited by 3 publications

References 0 publications

Joint Radar-Communications Strategies for Autonomous Vehicles

Joint Radar-Communications Strategies for Autonomous Vehicles

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

Adaptive Virtual Waveform Design for Millimeter-Wave Joint Communication-Radar

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