Joint transmit precoding and reconfigurable intelligent surface phase adjustment: a decomposition-aided channel estimation approach
Joint transmit precoding and reconfigurable intelligent surface phase adjustment: a decomposition-aided channel estimation approach
Reconfigurable intelligent surfaces (RISs), consisting of many low-cost elements that reflect the incident waves by an adjustable phase shift, have attracted sudden attention for their potential of reconfiguring the signal propagation environment and enhancing the performance of wireless networks. The passive nature of RISs is indeed beneficial, but the lack of radio frequency (RF) chains at the RIS has made channel estimation extremely challenging. We face this challenge by proposing a joint channel estimation and transmit precoding framework for RIS-aided multiple-input multiple-output (MIMO) systems. Specifically, the effective cascaded channel of the reflected transmitter-RIS-receiver link is decomposed into multiple subchannels, each of which corresponds to a single RIS element. Then our joint RIS transmitter precoding model is formulated for the individual subchannels of each reflecting element. Finally, we develop a two stage precoding design for successively determining the required phase shifts of each reflecting element of the RIS and the digital baseband precoder of the transmitter, only relying on the channel state information (CSI) of the subchannels. The performance of the proposed subchannel estimation and joint precoding method is evaluated by extensive simulations. Our numerical results show that the proposed designs provide an attractive solution to RIS-aided MIMO systems.
Zhou, Zhengyi
f5dab14d-9ed8-4842-bb66-af63472391e5
Ge, Ning
2007bd68-f742-49e4-bbb4-8f77158f7e89
Wang, Zhaocheng
70339538-3970-4094-bcfc-1b5111dfd8b4
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Zhou, Zhengyi
f5dab14d-9ed8-4842-bb66-af63472391e5
Ge, Ning
2007bd68-f742-49e4-bbb4-8f77158f7e89
Wang, Zhaocheng
70339538-3970-4094-bcfc-1b5111dfd8b4
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Zhou, Zhengyi, Ge, Ning, Wang, Zhaocheng and Hanzo, Lajos
(2020)
Joint transmit precoding and reconfigurable intelligent surface phase adjustment: a decomposition-aided channel estimation approach.
IEEE Transactions on Communications.
(doi:10.1109/TCOMM.2020.3034259).
Abstract
Reconfigurable intelligent surfaces (RISs), consisting of many low-cost elements that reflect the incident waves by an adjustable phase shift, have attracted sudden attention for their potential of reconfiguring the signal propagation environment and enhancing the performance of wireless networks. The passive nature of RISs is indeed beneficial, but the lack of radio frequency (RF) chains at the RIS has made channel estimation extremely challenging. We face this challenge by proposing a joint channel estimation and transmit precoding framework for RIS-aided multiple-input multiple-output (MIMO) systems. Specifically, the effective cascaded channel of the reflected transmitter-RIS-receiver link is decomposed into multiple subchannels, each of which corresponds to a single RIS element. Then our joint RIS transmitter precoding model is formulated for the individual subchannels of each reflecting element. Finally, we develop a two stage precoding design for successively determining the required phase shifts of each reflecting element of the RIS and the digital baseband precoder of the transmitter, only relying on the channel state information (CSI) of the subchannels. The performance of the proposed subchannel estimation and joint precoding method is evaluated by extensive simulations. Our numerical results show that the proposed designs provide an attractive solution to RIS-aided MIMO systems.
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Accepted/In Press date: 18 October 2020
e-pub ahead of print date: 27 October 2020
Identifiers
Local EPrints ID: 444881
URI: http://eprints.soton.ac.uk/id/eprint/444881
ISSN: 0090-6778
PURE UUID: 60e09fa4-9b04-4a8d-9638-7f5e029ab3fb
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Date deposited: 09 Nov 2020 17:32
Last modified: 18 Mar 2024 02:36
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Author:
Zhengyi Zhou
Author:
Ning Ge
Author:
Zhaocheng Wang
Author:
Lajos Hanzo
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