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

• Published in: IEEE transactions on vehicular technology Vol. 69; no. 12; pp. 14903 - 14917
• Main Authors: Ali, Anum, Gonzalez-Prelcic, Nuria, Ghosh, Amitava
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Antenna arrays; Automobiles; Automotive radar; Beamforming; beyond 5G; Communication; Covariance; FMCW radar; Measurement; millimeter wave communications; Millimeter waves; Out-of-band information; Passive radar; Radar antennas; Radar receivers; radar-aided communication; Ray tracing; Receivers; Roadsides; Similarity; Training; Transmissions (automotive); Vehicle-to-infrastructure; Waveforms; Wireless communication
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2020.3027636

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.