TY - GEN
T1 - Distributed Joint Multi-cell Optimization of IRS Parameters with Linear Precoders
AU - Wiesmayr, R.
AU - Honig, M.
AU - Joham, M.
AU - Utschick, W.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We present distributed methods for jointly optimizing Intelligent Reflecting Surface (IRS) phase-shifts and beamformers in a cellular network. The proposed schemes require knowledge of only the intra-cell training sequences and corresponding received signals without explicit channel estimation. Instead, an achievable sum-rate objective is estimated via sample means and maximized directly. This automatically includes and mitigates both intra- and inter-cell interference provided that the uplink training is synchronized across cells. Different schemes are considered that limit the set of known training sequences from interferers. With MIMO links an iterative synchronous bi-directional training scheme jointly optimizes the IRS parameters with the beamformers and combiners. Simulation results show that the proposed distributed methods show a modest performance degradation compared to centralized channel estimation schemes, which estimate all channels including all cross-channels, and perform significantly better than decentralized channel estimation schemes which ignore the inter-cell interference.
AB - We present distributed methods for jointly optimizing Intelligent Reflecting Surface (IRS) phase-shifts and beamformers in a cellular network. The proposed schemes require knowledge of only the intra-cell training sequences and corresponding received signals without explicit channel estimation. Instead, an achievable sum-rate objective is estimated via sample means and maximized directly. This automatically includes and mitigates both intra- and inter-cell interference provided that the uplink training is synchronized across cells. Different schemes are considered that limit the set of known training sequences from interferers. With MIMO links an iterative synchronous bi-directional training scheme jointly optimizes the IRS parameters with the beamformers and combiners. Simulation results show that the proposed distributed methods show a modest performance degradation compared to centralized channel estimation schemes, which estimate all channels including all cross-channels, and perform significantly better than decentralized channel estimation schemes which ignore the inter-cell interference.
KW - Intelligent reflecting surfaces
KW - MIMO
KW - channel estimation
KW - precoder optimization
UR - http://www.scopus.com/inward/record.url?scp=85137261729&partnerID=8YFLogxK
U2 - 10.1109/ICC45855.2022.9839115
DO - 10.1109/ICC45855.2022.9839115
M3 - Conference contribution
AN - SCOPUS:85137261729
T3 - IEEE International Conference on Communications
SP - 1468
EP - 1474
BT - ICC 2022 - IEEE International Conference on Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Communications, ICC 2022
Y2 - 16 May 2022 through 20 May 2022
ER -