TY  - INPR
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
TI  - Reconfigurable Intelligent Surface-Assisted Secret Key Generation Under Spatially Correlated Channels in Quasi-Static Environments
EP  - 1
SP  - 1
AV  - public
Y1  - 2024/01/02/
JF  - IEEE Internet of Things Journal
KW  - Physical layer secret key generation
KW  -  spatial
correlation
KW  -  reconfigurable intelligent surface (RIS)
KW  -  achievable key generation rate
A1  - Shahiri, Vahid
A1  - Behroozi, Hamid
A1  - Kuhestani, Ali
A1  - Wong, Kai-Kit
ID  - discovery10185517
N2  - Physical layer key generation (PLKG) can significantly enhance the security of classic encryption schemes by efficiently providing secret keys in resource-limited network like the Internet of Things (IoT). However, reaching a high key generation rate (KGR) is challenging in applications like smart home or remote area sensing with quasi-static channels. Recently, exploiting reconfigurable intelligent surface (RIS) to induce randomness in quasi-static wireless channels has received significant research interest. However, the inherent spatial correlation among the RIS elements is rarely studied, which can alter the optimum PLKG approach in terms of KGR and randomness in the key sequence. Specifically, for the first time, in this contribution, we take into account a spatially correlated RIS, which intends to enhance the KGR in a quasi-static medium. Novel closed-form analytical expressions for KGR are derived for the two cases of random phase shift (RPS) and our proposed equal phase shift (EPS) in the RIS elements. We also analyze the correlation between the channel samples to ensure the randomness of the generated secret key sequence. It is shown that the EPS scheme can effectively exploit the inherent spatial correlation between the RIS elements and it leads to a higher KGR compared to the widely used RPS strategy. We further formulate an optimization problem in which we determine the optimal portion of time dedicated to direct and indirect channel estimation, which has never been addressed in the previous studies. We show the accuracy and the fast convergence of our sequential convex programming (SCP) based algorithm and discuss the various parameters affecting spatially correlated RIS-assisted PLKG.
SN  - 2327-4662
UR  - http://dx.doi.org/10.1109/jiot.2023.3349354
PB  - Institute of Electrical and Electronics Engineers (IEEE)
ER  -