IRS-THz · Intelligent Reflecting Surface (IRS) Assisted Ultra-massive MIMO THz Communications for 6G
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2021-12-01 → 2023-11-30
- EU contribution
- €212,934
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Intelligent Reflecting Surface (IRS) Assisted Ultra-massive MIMO THz Communications for 6G
The project “IRS-THz” worked by Dr. Hao Xu from 01/12/2021 to 30/11/2023 has been successfully completed under the supervision of Prof. Kai-Kit Wong. In addition, Dr. Xu also worked closely with the group of Prof. Giuseppe Caire (Technical University Berlin, Germany) to complete the coding scheme in wiretap channels. Through the four work packages, we systematically study the theoretical modeling, performance analysis, performance optimization, and experimental characterization of intelligent reflecting surface (IRS) or fluid antenna system (FAS) assisted communication. Based on that, this project has resulted in several different designs of FAS that show intriguing performance gains, including much lower outage probability in multi-user networks, greatly enhanced secrecy in wiretap channels, where the eavesdroppers exist, and increased system capacity over the systems using the conventional fixed-position antennas. This project shows successful attempts on the application of FAS in multi-user communication systems, and provides many insightful theoretical and experimental foundations for the future study of FAS. The project combined the expertise of Dr. Xu and the host supervisor in the fundamentals of communication, information theory, and techniques of FAS, and equipped Dr. Xu to conduct cutting-edge research on FAS in various application scenarios. The specific objectives for the project “IRS-THz” described in the proposal are listed in the following: RO1: To design hierarchical multi-resolution codebooks and determine the phase-shift matrix of the IRS. RO2: To design adaptive beam alignment methods and develop an IRS-assisted THz testbed. RO3: To design extended Kalman filter (EKF) based beam tracking and machine learning (ML) empowered dynamic beam switching methods. RO4: To design joint resource allocation algorithms for an IRS-assisted multi-UE multi-carrier THz network. All these research objectives have been achieved.
Data: CORDIS, © European Union
Project objective
In this project, we aim to develop and experimentally demonstrate terahertz (THz) communication technologies for the sixth generation (6G) of wireless networks. In particular, we will integrate ultra-massive multiple-input-multiple-output (UM-MIMO) and intelligent reflecting surface (IRS) technologies into THz systems and apply directional beamforming to establish reliable, long-distance, and ultra-fast THz communication links. To realize channel training for directional beamforming, we will design hierarchical multi-resolution codebooks and find the optimal phase-shift matrix for the IRS. Since the presence of the IRS complicates the original one-hop communication topology into a two-hop topology and user equipments (UEs) are usually moving, to reduce the channel training overhead and maintain high-quality links, we will study beam management (BM) problems, including adaptive beam alignment and beam tracking. Besides channel training, we will also design joint resource allocation algorithms to fully reap the potentials of the THz networks and support the diverse needs of UEs. To assess the performance of the proposed schemes in a real-world environment, we will also develop an easy-to-use software-defined IRS-assisted THz testbed for experimental demonstration. With seven years of research experience, Dr. Xu has developed a wide range of research expertise in massive MIMO, millimeter-wave networks, IRS-assisted communications in sub-6GHz systems, machine learning, etc. The implementation of this project will not only further extend his research field into IRS-assisted UM-MIMO THz communications, but will also significantly improve his project management and leadership skills.
Original text from CORDIS.
Participants
- UNIVERSITY COLLEGE LONDON · LondonCoordinatorUnited Kingdom
Links
Data: CORDIS, © European Union