TY - GEN
T1 - Optimizing Over-The-Air Computation in IRS-Aided C-RAN Systems
AU - Yu, Daesung
AU - Park, Seok Hwan
AU - Simeone, Osvaldo
AU - Shitz, Shlomo Shamai
N1 - Funding Information:
This work was supported by Basic Science Research Program through the National Research Foundation of Korea grants funded by the Ministry of Education [NRF-2018R1D1A1B07040322, NRF-2019R1A6A1A09031717]. The work of O. Simeone was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725731). The work of S. Shamai was supported by the ERC under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 694630).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - Over-The-Air computation (AirComp) is an efficient solution to enable federated learning on wireless channels. Air-Comp assumes that the wireless channels from different devices can be controlled, e.g., via transmitter-side phase compensation, in order to ensure coherent on-Air combining. Intelligent reflecting surfaces (IRSs) can provide an alternative, or additional, means of controlling channel propagation conditions. This work studies the advantages of deploying IRSs for AirComp systems in a large-scale cloud radio access network (C-RAN). In this system, worker devices upload locally updated models to a parameter server (PS) through distributed access points (APs) that communicate with the PS on finite-capacity fronthaul links. The problem of jointly optimizing the IRSs' reflecting phases and a linear detector at the PS is tackled with the goal of minimizing the mean squared error (MSE) of a parameter estimated at the PS. Numerical results validate the advantages of deploying IRSs with optimized phases for AirComp in C-RAN systems.
AB - Over-The-Air computation (AirComp) is an efficient solution to enable federated learning on wireless channels. Air-Comp assumes that the wireless channels from different devices can be controlled, e.g., via transmitter-side phase compensation, in order to ensure coherent on-Air combining. Intelligent reflecting surfaces (IRSs) can provide an alternative, or additional, means of controlling channel propagation conditions. This work studies the advantages of deploying IRSs for AirComp systems in a large-scale cloud radio access network (C-RAN). In this system, worker devices upload locally updated models to a parameter server (PS) through distributed access points (APs) that communicate with the PS on finite-capacity fronthaul links. The problem of jointly optimizing the IRSs' reflecting phases and a linear detector at the PS is tackled with the goal of minimizing the mean squared error (MSE) of a parameter estimated at the PS. Numerical results validate the advantages of deploying IRSs with optimized phases for AirComp in C-RAN systems.
KW - C-RAN
KW - Over-The-Air computation
KW - intelligent reflecting surface
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U2 - 10.1109/SPAWC48557.2020.9154243
DO - 10.1109/SPAWC48557.2020.9154243
M3 - Conference contribution
AN - SCOPUS:85090384956
T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
BT - 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020
Y2 - 26 May 2020 through 29 May 2020
ER -