Power-Efficient STAR-RIS Aided MIMO-SWIPT Towards 6G Green Communications Under Channel Estimation Error
Globecom, pp. 4405–4410
Abstract
In this paper, we explore a novel approach of using a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) to aid a multi-user (MU) multi-input multi-output (MIMO) system for simultaneous wireless information and power transfer (SWIPT) in the presence of statistical channel estimation errors (CEE). Our focus is on minimizing the power required for the SWIPT system through joint beamforming design at both the base station (BS) and STAR-RIS, while ensuring that the minimum rate and minimum energy harvesting requirements are met for information and energy receivers, respectively. Due to the non-convex and NP-hard nature of the problem, we utilize a minimum mean square error (MMSE) approach to simplify the problem and then use an alternating optimization framework to solve the beamforming design problems at the BS and STAR-RIS iteratively using general approximations. Simulation results show that the proposed algorithm provides a significant beamforming gain for STAR-RIS-aided SWIPT system over conventional RIS system while satisfying the given quality of service (QoS) constraints for SWIPT systems under the CEE.
Authors 6
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National Sun Yat-sen University
Affiliation as printed
Institute of Communications Engineering, National Sun Yat-sen University,Kaohsiung,Taiwan,804
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Affiliation as printed
Southeast University,National Mobile Communications Research Laboratory,Nanjing,China,210096
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National Sun Yat-sen University
Affiliation as printed
Institute of Communications Engineering, National Sun Yat-sen University,Kaohsiung,Taiwan,804
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Affiliation as printed
Lenovo Deutschland GmbH,5G Wireless Research Group,Germany
5G Wireless Research Group, Lenovo Deutschland GmbH, Germany
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Affiliation as printed
Southeast University,National Mobile Communications Research Laboratory,Nanjing,China,210096
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Affiliation as printed
RWTH Aachen University,Chair of Information Theory and Data Analytics,Aachen,Germany,52062
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