TY - JOUR
T1 - Artificial Noise-aided Secure Transmission for User-Centric Cell-Free IoT Network
AU - Gao, Xiang
AU - Li, Yong
AU - Dong, Limeng
AU - Cheng, Wei
AU - Shi, Ge
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2024
Y1 - 2024
N2 - This paper investigates the physical layer security for user-centric cell-free massive multiple-input multiple-output (UC-CF-mMIMO)-enabled Internet of Things (IoT) network with multiple active eavesdroppers (Eves). We assume that each Eve intends to decode the information of the user (UE) closest to it and is intelligent enough to eliminate interference of other UEs perfectly. Two eavesdropping modes of non-colluding and colluding Eves are considered. Additionally, access points (APs) inject artificial noise (AN) into confidential data signal to hamper the eavesdropping of Eves. To assess the secrecy performance of UC-CF-mMIMO-enabled IoT network, we derive the worst-case secrecy rate expression. Then, a max-min secrecy rate (MMSR) problem is formulated via joint optimization of AP clustering, AN selection, and power allocation, which aims to maximize the minimal secrecy rate among attacked UEs while adhering to the quality-of-service requirement of UEs, the limitation on the number of UEs and AN sent by each AP, and maximum transmit power limitation of APs. Due to the mixed-integer and non-convex nature, the formulated problem is tackled via employing the ℓ1-norm relaxation and successive convex approximation approach. Finally, we obtain a near-optimal solution to the MMSR problem by iteratively solving a sequence of second-order cone programmings. Simulation results demonstrate the superiority of proposed approach by the comparison with some existing schemes.
AB - This paper investigates the physical layer security for user-centric cell-free massive multiple-input multiple-output (UC-CF-mMIMO)-enabled Internet of Things (IoT) network with multiple active eavesdroppers (Eves). We assume that each Eve intends to decode the information of the user (UE) closest to it and is intelligent enough to eliminate interference of other UEs perfectly. Two eavesdropping modes of non-colluding and colluding Eves are considered. Additionally, access points (APs) inject artificial noise (AN) into confidential data signal to hamper the eavesdropping of Eves. To assess the secrecy performance of UC-CF-mMIMO-enabled IoT network, we derive the worst-case secrecy rate expression. Then, a max-min secrecy rate (MMSR) problem is formulated via joint optimization of AP clustering, AN selection, and power allocation, which aims to maximize the minimal secrecy rate among attacked UEs while adhering to the quality-of-service requirement of UEs, the limitation on the number of UEs and AN sent by each AP, and maximum transmit power limitation of APs. Due to the mixed-integer and non-convex nature, the formulated problem is tackled via employing the ℓ1-norm relaxation and successive convex approximation approach. Finally, we obtain a near-optimal solution to the MMSR problem by iteratively solving a sequence of second-order cone programmings. Simulation results demonstrate the superiority of proposed approach by the comparison with some existing schemes.
KW - artificial noise
KW - Internet of Things
KW - joint optimization
KW - physical layer security
KW - User-centric cell-free massive MIMO
UR - http://www.scopus.com/inward/record.url?scp=85210170548&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2024.3498061
DO - 10.1109/JIOT.2024.3498061
M3 - 文章
AN - SCOPUS:85210170548
SN - 2327-4662
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
ER -