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

As 5G [3 GPPNew Radio(NR)] deployments continue to expand, their use as an “opportunistic” bistatic radar sensing system that uses thebase station(BS) as thetransmitter(TX) and theuser equipment(UE) as thereceiver(RX) opens the possibility of a new sensing modality that does not need extra hardware, and thus efficiently realizeintegrated sensing and communication for vehicles(ISAC). In particular, such 5G-based radar sensing can complement and enhance the existing monostatic radar inadvanced driver assistance systems(ADAS) and future self-driving cars, e.g., for detectingnon-line-of-sight(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 differentreference signals (RSs) defined in the NR standard, and then analyze how they can be best combined for radar purposes. We also present high-resolution parameter-basedserial interference cancellation(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.