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Volume 46 Issue 3
Mar.  2024
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ZHANG Xiaoxi, XU Yongjun, WU Cuixian, HUANG Chongwen. Beamforming Design for Reconfigurable Intelligent Surface Enhanced Full-duplex Ambient Backscatter Communication Networks[J]. Journal of Electronics & Information Technology, 2024, 46(3): 914-924. doi: 10.11999/JEIT230356
Citation: ZHANG Xiaoxi, XU Yongjun, WU Cuixian, HUANG Chongwen. Beamforming Design for Reconfigurable Intelligent Surface Enhanced Full-duplex Ambient Backscatter Communication Networks[J]. Journal of Electronics & Information Technology, 2024, 46(3): 914-924. doi: 10.11999/JEIT230356

Beamforming Design for Reconfigurable Intelligent Surface Enhanced Full-duplex Ambient Backscatter Communication Networks

doi: 10.11999/JEIT230356
Funds:  The National Natural Science Foundation of China (62271094), Natural Science Foundation of Chongqing (CSTB2022NSCQ-LZX0009), The Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJZD-K202200601), The Open Project of Zhejiang Provincial Key Laboratory of Information Processing and Communication Networking (IPCAN-2302)
  • Received Date: 2023-05-04
  • Rev Recd Date: 2023-07-12
  • Available Online: 2023-07-17
  • Publish Date: 2024-03-27
  • Current conventional ambient backscatter communication suffers from double fading, obstacle blockage, and limited network capacity. Reconfigurable Intelligent Surface (RIS) as a key candidate technology of 6G has been concerned due to the improvement of signal transmission quality and the enhancement of the transmission performance of communication systems. Based on the above advantages, RIS and full-duplex techniques are introduced into the ambient backscatter communication system, and the beamforming algorithm is designed in an RIS-enhanced full-duplex ambient backscatter communication network with hardware impairments and discrete phase-shift constraints. Firstly, a beamforming optimization problem is formulated to minimize the total transmit power by considering the minimum harvested energy and the quality-of-service constraint of the backscatter nodes, the maximum transmit power constraint of the power station, and the phase shift constraint of the RIS. Then, the original non-convex problem is transformed into a tractable convex optimization problem by using alternating optimization methods, semi-definite relaxation methods, variable substitution, and semi-definite programming. Finally, simulation results show that the proposed algorithm decreases the average power consumption by 7.8% compared to the conventional beamforming method.
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