TY - GEN
T1 - A Digital Twin Assisted Framework for Interference Nulling in Millimeter Wave MIMO Systems
AU - Zhang, Yu
AU - Osman, Tawfik
AU - Alkhateeb, Ahmed
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Millimeter wave (mmWave) and terahertz MIMO systems rely on pre-defined beamforming codebooks for both initial access and data transmission. However, most of the existing codebooks adopt pre-defined beams that focus mainly on improving the gain of their target users, without taking interference into account, which could incur critical performance degradation in the dense networks. To address this problem, in this paper, we propose a sample-efficient digital twin-assisted beam pattern design framework that learns how to form the beam pattern to reject the signals from the interfering directions. The proposed approach does not require any explicit channel knowledge or any coordination with the interferers. The adoption of the digital twin improves the sample efficiency by better leveraging the underlying signal relationship and by incorporating a demand-based data acquisition strategy. Simulation results show that the developed signal model-based learning framework can significantly reduce the actual interaction with the radio environment (i.e., number of measurements) compared to the model-unaware design, leading to a more practical and efficient interference-aware beam design approach.
AB - Millimeter wave (mmWave) and terahertz MIMO systems rely on pre-defined beamforming codebooks for both initial access and data transmission. However, most of the existing codebooks adopt pre-defined beams that focus mainly on improving the gain of their target users, without taking interference into account, which could incur critical performance degradation in the dense networks. To address this problem, in this paper, we propose a sample-efficient digital twin-assisted beam pattern design framework that learns how to form the beam pattern to reject the signals from the interfering directions. The proposed approach does not require any explicit channel knowledge or any coordination with the interferers. The adoption of the digital twin improves the sample efficiency by better leveraging the underlying signal relationship and by incorporating a demand-based data acquisition strategy. Simulation results show that the developed signal model-based learning framework can significantly reduce the actual interaction with the radio environment (i.e., number of measurements) compared to the model-unaware design, leading to a more practical and efficient interference-aware beam design approach.
UR - http://www.scopus.com/inward/record.url?scp=85177825375&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85177825375&partnerID=8YFLogxK
U2 - 10.1109/ICCWorkshops57953.2023.10283591
DO - 10.1109/ICCWorkshops57953.2023.10283591
M3 - Conference contribution
AN - SCOPUS:85177825375
T3 - 2023 IEEE International Conference on Communications Workshops: Sustainable Communications for Renaissance, ICC Workshops 2023
SP - 248
EP - 253
BT - 2023 IEEE International Conference on Communications Workshops
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Communications Workshops, ICC Workshops 2023
Y2 - 28 May 2023 through 1 June 2023
ER -