Welcome to Communication Systems Research (CSR) Group at QMUL. The group consists of 10 academic staff and nearly 30 PhD students and postdocs. The group, established in 2017. Our research spans the broad areas of wireless communications and statistical signal processing, with special emphasis on communication theory, information theory, optimization, machine learning, control theory, and random graph theory.
The Communication Systems Research (CSR) group is internationally renowned for its contributions towards Fifth Generation (5G) Networks, Internet of Things (IoT), and Bio-inspired Molecular Communications. We have made fundamental contributions to 5G air interface, more specifically in millimeter wave communications, full-duplex communications, massive multiple‐input multiple‐output (MIMO) and large scale systems. These technologies developed by the group address practical issues concerning the design and implementation of high speed multi‐gigabit wireless for mobile backhaul, energy efficient ultra‐dense small cells, low latency wireless access to the cloud/fog, self-organizing multi-tier UAV and massive IoT networks. Currently, CSR group secured numerous grants on Sixth Generation (6G) Networks, more specifically on cellular connected UAV (C-UAV), Federated Learning (FL) and Reconfigurable Intelligent Reflecting Surfaces (IRS)
Our group also specializes in nano-communications for the Internet of Nano Things (IoNT) to enable connectivity between nano-devices, and to bridge the gap between bio-signal processing and nano-precision healthcare. Our core expertise in this area includes bacteria communication, bio-systems analysis, biological circuits, and DNA computing.
Members of the group have published several hundreds of technical journals and magazines, most of them are in the top IEEE journals of the field, with tens of thousands citations.
According to Shanghai Ranking, Telecommunications Engineering at QMUL has been ranked within top three in the U.K over the past four years (2021, 2022, 2023 and 2024)consistently.