Beam-Space MIMO Radar with OTFS Modulation for Integrated Sensing and Communications

• Main Authors: Dehkordi, Saeid K., Gaudio, Lorenzo, Kobayashi, Mari, Colavolpe, Giulio, Caire, Giuseppe
• Format: Conference Proceeding
• Language: English
• Subjects: hybrid digital-analog beamforming; integrated sensing and communication; maximum like-lihood estimation; Millimeter wave radar; MIMO radar; OTFS; Parameter estimation; Radar antennas; Radar tracking; Radio frequency; Spaceborne radar
• ISSN: 2694-2941
• DOI: 10.1109/ICCWorkshops53468.2022.9814573

Motivated by recent advances of Integrated Sensing and Communication (ISAC), we study an ISAC system operating at millimeter waves (mmWave) frequency bands where a Base Station (BS) equipped with a co-located radar receiver transmits data via a digitally modulated orthogonal time frequency space (OTFS) waveform and simultaneously performs radar estimation from the backscattered signal. We consider two system function modes. In Discovery mode, a single common data stream is broadcast over a wide angular sector where the radar receiver detects the presence of not yet acquired targets and performs coarse parameter estimation (angle of arrival, delay, and Doppler). In Tracking mode, the BS sends multiple individual data streams to already acquired users via beamforming, while the radar receiver performs fine-resolution parameter estimation. In this work a realistic hybrid digital-analog scheme for RF beamforming at mmWave is considered, where the number of RF chains for modulation/demodulation is significantly smaller than the number of array antenna elements. Hence, a direct application of standard MIMO radar approaches is not possible. Instead, we consider the design of the RF-domain "reduction matrix" (from antennas to RF chains) of the radar receiver, whose role is to trade off between the exploration capability of the angle domain and the directivity of the beamforming patterns. Under this setup, we propose an efficient maximum likelihood scheme to jointly perform target detection and parameter estimation. Our numerical results demonstrate that the proposed approach is able to reliably detect multiple targets while essentially achieving the Cramér-Rao lower bound for parameter estimation.