TY - GEN
T1 - Autoencoder-based Communications with Reconfigurable Intelligent Surfaces
AU - Erpek, Tugba
AU - Sagduyu, Yalin E.
AU - Alkhateeb, Ahmed
AU - Yener, Aylin
N1 - Funding Information:
This effort is supported by the U.S. Army Research Office under contract W911NF-21-C-0015. The content of the information does not necessarily reflect the position or the policy of the U.S. Government, and no official endorsement should be inferred.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - This paper presents a novel approach for the joint design of a reconfigurable intelligent surface (RIS) and a transmitter-receiver pair that are trained together as a set of deep neural networks (DNNs) to optimize the end-To-end communication performance at the receiver. The RIS is a software-defined array of unit cells that can be controlled in terms of the scattering and reflection profiles to focus the incoming signals from the transmitter to the receiver. The benefit of the RIS is to improve the coverage and spectral efficiency for wireless communications by overcoming physical obstructions of the line-of-sight (LoS) links. The selection process of the RIS beam codeword (out of a pre-defined codebook) is formulated as a DNN, while the operations of the transmitter-receiver pair are modeled as two DNNs, one for the encoder (at the transmitter) and the other one for the decoder (at the receiver) of an autoencoder, by accounting for channel effects including those induced by the RIS in between. The underlying DNNs are jointly trained to minimize the symbol error rate at the receiver. Numerical results show that the proposed design achieves major gains in error performance with respect to various baseline schemes, where no RIS is used or the selection of the RIS beam is separated from the design of the transmitter-receiver pair.
AB - This paper presents a novel approach for the joint design of a reconfigurable intelligent surface (RIS) and a transmitter-receiver pair that are trained together as a set of deep neural networks (DNNs) to optimize the end-To-end communication performance at the receiver. The RIS is a software-defined array of unit cells that can be controlled in terms of the scattering and reflection profiles to focus the incoming signals from the transmitter to the receiver. The benefit of the RIS is to improve the coverage and spectral efficiency for wireless communications by overcoming physical obstructions of the line-of-sight (LoS) links. The selection process of the RIS beam codeword (out of a pre-defined codebook) is formulated as a DNN, while the operations of the transmitter-receiver pair are modeled as two DNNs, one for the encoder (at the transmitter) and the other one for the decoder (at the receiver) of an autoencoder, by accounting for channel effects including those induced by the RIS in between. The underlying DNNs are jointly trained to minimize the symbol error rate at the receiver. Numerical results show that the proposed design achieves major gains in error performance with respect to various baseline schemes, where no RIS is used or the selection of the RIS beam is separated from the design of the transmitter-receiver pair.
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U2 - 10.1109/DySPAN53946.2021.9677378
DO - 10.1109/DySPAN53946.2021.9677378
M3 - Conference contribution
AN - SCOPUS:85122606699
T3 - 2021 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2021
SP - 242
EP - 247
BT - 2021 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2021
Y2 - 13 December 2021 through 15 December 2021
ER -