Simon Tarboush

The core functionalities of future wireless networks extend beyond communication, with localization and sensing playing a crucial role in enabling new services.

The objective of this research project is to explore unsourced random access (uRA) within a user-centric cell-free massive MIMO (CF-mMIMO) network, developing novel centralized and/or distributed multi-source sparse estimation techniques that leverage location-based codebooks. The approach will enable fast random access, simultaneous identification of active users, estimation of their channels, and inference of their positions, making it well-suited to distributed cell-free architectures and aligned with the emerging 2-step RACH in the 3GPP standards. This involves exploring additional sources of information inherent to such networks, with the aim of designing high-accuracy localization and sensing schemes. Moreover, considering a finite block length framework, this research will also characterize the achievable rates for short-block communication by analyzing the trade-off between rate and probability of error. The study further investigates resource allocation in dense scenarios where a very large number of users, compared to the spatial multiplexing gain of the system, aim to utilize the network. Additionally, it focuses on optimizing edge computing resource allocation by strategically placing user-centric cluster processors as software-defined network functions hosted in the Distributed Units (DUs) and optimizing data routing through the fronthaul to minimize network load.