Applications of Radio Waves for Integrated Sensing and Communication (ISAC)

Aircraft on the airport surface are located by multilateration systems that measure the arrival times of their broadcast signals — and strong multipath from terminals and hangars is a principal source of localization error. Knowing the multipath channel reveals how reflections distort the measured arrival times — the knowledge needed to correct the resulting position errors and to place receivers where distortion is least. Because active channel sounding is prohibited inside an operational airport, we estimate the channel passively, using the ADS-B surveillance signals that aircraft already transmit. From the passive measurements conducted at the Airport, we resolve the multipath structure of the airport surface with super-resolution accuracy — and, since each ADS-B message reports the aircraft's own position, it can trace strong reflections back to the structures that cause them by comparing with the ray tracing simulation built upon 3D outdoor airport environment map.

Vehicles are the central sensing targets of ISAC in outdoor environments. From multipath components (MPCs) extracted from channel measurements, we use a single-bounce geometry to efficiently separate the MPCs reflected by moving vehicles from the static background, keeping the computation light. A multi-target association and tracking algorithm then follows these vehicles (ordinary, non-control traffic rather than dedicated test cars) and geo-locates them directly from the measured MPCs, providing ground truth to validate and refine deterministic outdoor channel models for ISAC.

Passive crowd counting infers how many people are present from perturbations in ambient wireless signals, offering a privacy-preserving alternative to cameras. We empirically model how multi-person walking fades the beam-level RSRP of a commercial 5G mmWave base station, linking crowd size to a single interpretable fading-spread parameter for lightweight, explainable analytics.

OTFS modulation suits wireless communication in high-mobility environments — high-speed rail or aircraft links — by representing the channel in the sparse delay-Doppler domain. We propose a semi-blind framework in which delay and Doppler are estimated jointly while the data itself is being decoded where the channel estimate and the data decisions are improved jointly each iteration. Additionally, by resolving fractional delay and Doppler beyond the resolution of the OTFS grid, the framework directly improves the range and velocity accuracy that ISAC sensing depends on.