TY - GEN
T1 - Twin-Timescale Beamforming for IRS-Assisted Millimeter Wave Massive MIMO-OFDM Systems
AU - Wang, Peilan
AU - Wu, Zhuoran
AU - Fang, Jun
AU - Li, Hongbin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We investigate a twin-timescale joint beamforming problem for multiple intelligent reflecting surfaces (IRSs)-assisted multi-user mm Wave orthogonal frequency division multiplexing (OFDM) systems, where the base station (BS) employs a hybrid analog and digital precoder. To alleviate the burden of frequent channel state information (CSI) acquisition and reduce design complexity, we devise the passive beamforming vector and the analog precoder based on statistical CSI, while the digital precoder is designed based on low-dimensional instantaneous CSI. Specifically, the former long-term optimization can be formulated as a stochastic optimization problem. To address this problem, we propose two different solutions. The first method devises the passive beamforming vector and the analog precoder by maximizing the ergodic channel gain. We also propose a deep unrolling-based method to provide a unified framework for the stochastic optimization problem. Our simulation results demonstrate the effectiveness and computational efficiency of the proposed methods.
AB - We investigate a twin-timescale joint beamforming problem for multiple intelligent reflecting surfaces (IRSs)-assisted multi-user mm Wave orthogonal frequency division multiplexing (OFDM) systems, where the base station (BS) employs a hybrid analog and digital precoder. To alleviate the burden of frequent channel state information (CSI) acquisition and reduce design complexity, we devise the passive beamforming vector and the analog precoder based on statistical CSI, while the digital precoder is designed based on low-dimensional instantaneous CSI. Specifically, the former long-term optimization can be formulated as a stochastic optimization problem. To address this problem, we propose two different solutions. The first method devises the passive beamforming vector and the analog precoder by maximizing the ergodic channel gain. We also propose a deep unrolling-based method to provide a unified framework for the stochastic optimization problem. Our simulation results demonstrate the effectiveness and computational efficiency of the proposed methods.
KW - Intelligent reflecting surfaces
KW - millimeter wave communications
KW - twin-timescale beamforming
UR - http://www.scopus.com/inward/record.url?scp=85187362605&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85187362605&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM54140.2023.10437357
DO - 10.1109/GLOBECOM54140.2023.10437357
M3 - Conference contribution
AN - SCOPUS:85187362605
T3 - Proceedings - IEEE Global Communications Conference, GLOBECOM
SP - 1555
EP - 1560
BT - GLOBECOM 2023 - 2023 IEEE Global Communications Conference
T2 - 2023 IEEE Global Communications Conference, GLOBECOM 2023
Y2 - 4 December 2023 through 8 December 2023
ER -