Signal Processing in Wireless Communication

Testing a wireless system in the field is slow and expensive; a channel emulator reproduces the radio channel in the laboratory instead — but only as faithfully as its underlying channel model allows. We develop a grid-based channel modeling technique in which multipath parameters are precomputed deterministically — by ray tracing or extracted from measurements — at the nodes of a spatial grid, and the channel at any receiver position along any trajectory is then synthesized by interpolation. This turns site-specific, ray-traced fidelity, normally far too heavy for real time, into something an emulator can play back live for a moving receiver. The framework has been validated from indoor offices to urban field measurements, and its grid database further points toward a practical way to share site-specific channel models without disclosing the underlying 3D environment.

We build the testbeds that take Integrated Sensing and Communication (ISAC) from concept to real systems. The core is a software-defined-radio (SDR) node that senses entirely passively: it captures signals already in the air — unknown waveforms from commercial transmitters or known broadcast signals from a 5G base station — and uses them as opportunistic sources to detect human motion through Doppler signatures, without transmitting anything itself. Alongside it, we develop an OpenAirInterface(OAI)-based 5G base station that extracts channel state information, letting us prototype network-based ISAC in a controlled laboratory before moving to commercial networks.

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.

Traditional Wi-Fi roaming selects access points by signal strength (RSSI), which masks multipath fading and can trap devices on poor links despite strong signal. Using fine-grained channel state information (CSI), we characterize the measured channel quality using metrics related to statistical multipath fading parameters — driving smarter handover decisions in complex, and rich-multipath indoor environments.

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.