TY - GEN
T1 - Secrecy Wireless Information and Power Transfer in Ultra-Dense Cloud-RAN with Wireless Fronthaul
AU - Wang, Ji
AU - Ma, Xinxin
AU - Zheng, Le
AU - Yang, Kai
AU - Chen, Zhao
AU - Xia, Qiaoqiao
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper studies the secrecy wireless information and power transfer problem in ultra-dense cloud radio access network (UD-CRAN) with wireless fronthaul, which is a promising framework for future Internet of Things (IoT). The transmission schemes of wireless fronthaul and access links are jointly designed, while addressing the characteristics of ultra-dense network such as base station diversity and high probability of line-of-sight transmission. Specifically, we employ the idea of block diagonalization to deal with the fronthaul interference, which support multi-stream fronthaul transmission for each remote radio head (RRH). We then jointly optimize the power allocation in the fronthaul and the resource allocation in the access link which includes beamforming for information and energy transmission, on/off of RRHs, and user-RRH association. In order to solve the formulated mixed integer non-convex optimization problem, we leverage the sparsity of beamforming vectors brought by the ultra-dense RRHs. We then solve the reformulated problem by employing the successive convex approximation approach. Finally, numerical results are presented to demonstrate the effectiveness of the proposed scheme.
AB - This paper studies the secrecy wireless information and power transfer problem in ultra-dense cloud radio access network (UD-CRAN) with wireless fronthaul, which is a promising framework for future Internet of Things (IoT). The transmission schemes of wireless fronthaul and access links are jointly designed, while addressing the characteristics of ultra-dense network such as base station diversity and high probability of line-of-sight transmission. Specifically, we employ the idea of block diagonalization to deal with the fronthaul interference, which support multi-stream fronthaul transmission for each remote radio head (RRH). We then jointly optimize the power allocation in the fronthaul and the resource allocation in the access link which includes beamforming for information and energy transmission, on/off of RRHs, and user-RRH association. In order to solve the formulated mixed integer non-convex optimization problem, we leverage the sparsity of beamforming vectors brought by the ultra-dense RRHs. We then solve the reformulated problem by employing the successive convex approximation approach. Finally, numerical results are presented to demonstrate the effectiveness of the proposed scheme.
KW - Internet of Things
KW - energy harvesting, successive convex approximation
KW - ultra-dense cloud radio access network
UR - http://www.scopus.com/inward/record.url?scp=85159786628&partnerID=8YFLogxK
U2 - 10.1109/WCNC55385.2023.10118894
DO - 10.1109/WCNC55385.2023.10118894
M3 - Conference contribution
AN - SCOPUS:85159786628
T3 - IEEE Wireless Communications and Networking Conference, WCNC
BT - 2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Wireless Communications and Networking Conference, WCNC 2023
Y2 - 26 March 2023 through 29 March 2023
ER -