Integrated Sensing and Communication (ISAC) for Vehicles: Bistatic Radar With 5G-NR Signals

As 5G [3 GPP <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">New Radio</i> (NR)] deployments continue to expand, their use as an “opportunistic” bistatic radar sensing system that uses the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">base station</i> (BS) as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transmitter</i> (TX) and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user equipment</i> (UE) as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">receiver</i> (RX) opens the possibility of a new sensing modality that does not need extra hardware, and thus efficiently realize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">integrated sensing and communication for vehicles</i> (ISAC). In particular, such 5G-based radar sensing can complement and enhance the existing monostatic radar in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">advanced driver assistance systems</i> (ADAS) and future self-driving cars, e.g., for detecting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-line-of-sight</i> (NLoS) objects. However, 5G-NR signals have been designed for communication purposes and, as per the 3 GPP standard definition, they are neither continuous nor periodic in time and frequency. This then makes it challenging to create an efficient standards-compliant bistatic radar system. This paper describes a suite of methods for overcoming these obstacles. We first explore the different <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reference signal</i>s (RSs) defined in the NR standard, and then analyze how they can be best combined for radar purposes. We also present high-resolution parameter-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">serial interference cancellation</i> (SIC) to extract the scatterers in the delay-Doppler domain with better-than-Fourier resolution. The impacts of combined precoding and beamforming stipulated in the standard are also discussed in this paper. To demonstrate the validity of our approach, we simulate our results using synthetic channels and validate them with channel measurements.

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Publication Details

Journal
IEEE Transactions on Vehicular Technology
Published
2024-12-11
DOI
https://doi.org/10.1109/tvt.2024.3514573
Citations
9
Primary Topic
Radar Systems and Signal Processing
Type
article
Field-Weighted Citation Impact
11.69
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article

Integrated Sensing and Communication (ISAC) for Vehicles: Bistatic Radar With 5G-NR Signals

Sudhanshu Sharma, Fan Bai, Andreas F. Molisch, Kamran Ali et al.
9 citations
IEEE Transactions on Vehicular Technology
Radar Systems and Signal Processing
11.69
article

Integrated Sensing and Communication (ISAC) for Vehicles: Bistatic Radar With 5G-NR Signals

Sudhanshu Sharma, Fan Bai, Andreas F. Molisch, Kamran Ali, Nikhil K. Nataraja, Rui Wang
article en
9 citations

Abstract

As 5G [3 GPP <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">New Radio</i> (NR)] deployments continue to expand, their use as an “opportunistic” bistatic radar sensing system that uses the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">base station</i> (BS) as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transmitter</i> (TX) and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user equipment</i> (UE) as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">receiver</i> (RX) opens the possibility of a new sensing modality that does not need extra hardware, and thus efficiently realize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">integrated sensing and communication for vehicles</i> (ISAC). In particular, such 5G-based radar sensing can complement and enhance the existing monostatic radar in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">advanced driver assistance systems</i> (ADAS) and future self-driving cars, e.g., for detecting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-line-of-sight</i> (NLoS) objects. However, 5G-NR signals have been designed for communication purposes and, as per the 3 GPP standard definition, they are neither continuous nor periodic in time and frequency. This then makes it challenging to create an efficient standards-compliant bistatic radar system. This paper describes a suite of methods for overcoming these obstacles. We first explore the different <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reference signal</i>s (RSs) defined in the NR standard, and then analyze how they can be best combined for radar purposes. We also present high-resolution parameter-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">serial interference cancellation</i> (SIC) to extract the scatterers in the delay-Doppler domain with better-than-Fourier resolution. The impacts of combined precoding and beamforming stipulated in the standard are also discussed in this paper. To demonstrate the validity of our approach, we simulate our results using synthetic channels and validate them with channel measurements.

IEEE Transactions on Vehicular TechnologyVol. 74(4)
University of Southern California (US), American Metal Processing (United States) (US), Education Coordinating Council (US), University of The Sacred Heart (PR)
Affordable and clean energy
Openalex Percentile: Top 2%
Radar Systems and Signal Processing
11.69
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