MSc. Umut Utku Erdem 

Background & Motivation

Integrated Sensing and Communication (ISAC) is a technology for future wireless networks, sharing hardware and spectral resources between communication and radar subsystems. In high-dynamic-range scenarios, monostatic OFDM-ISAC systems face severe performance bottlenecks. Transmitter-receiver spillover dictates the dynamic range and elevates the quantization noise floor, while target delays exceeding the Cyclic Prefix (CP) induce severe Inter-Symbol Interference (ISI) and Inter-Carrier Interference (ICI).

While recent interference cancellation frameworks successfully reconstruct and isolate weak point targets, real-world objects (e.g., vehicles, pedestrians) act as extended targets. These targets distribute signal energy across multiple range-Doppler bins, causing severe interference and complex phase fluctuations. Furthermore, extending these systems to Multiple-Input Multiple-Output (MIMO) architectures introduces additional challenges.

Task Description

This thesis aims to push the boundaries of current OFDM-ISAC interference mitigation frameworks by expanding them into the spatial domain (MIMO) and accounting for the complex electromagnetic scattering profiles of extended targets.

The core tasks include:

Mathematical Modeling: Formulate a robust 3D (Range-Doppler-Angle) signal model for a MIMO-OFDM ISAC system that accurately captures self-interference of extended scatterers exceeding the CP limit.

Algorithm Extension: Evolve algorithms into the MIMO domain. Develop methodologies to perform spatial-temporal signal reconstruction without destroying the phase coherency required for accurate AoA estimation.

Interference Cancellation: Design a framework to mitigate interference for extended targets, managing the severe computational load of high-dimensional existing techniques.

Simulation & Evaluation: Implement a comprehensive MATLAB simulation environment to evaluate the proposed algorithms against classical point-target baselines, analyzing the trade-offs between computational complexity, dynamic range, and spatial resolution.

Prerequisites & Profile

This topic requires a student who is comfortable with mathematical derivations and advanced algorithm design.

Good theoretical understanding of Digital Signal Processing (DSP).

Solid foundation in radar systems engineering and/or MIMO communications.

Proficiency in MATLAB.

High degree of initiative, problem-solving ability.