Intelligent Reflecting Surface-Assisted Secret Key Generation with Discrete Phase Shifts in Static Environment

Citation Author(s):
Xiaoyan
Hu
Submitted by:
Xiaoyan Hu
Last updated:
Mon, 05/17/2021 - 11:19
DOI:
10.21227/qxtf-ph17
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Abstract 

  Physical layer secret key generation is a promising candidate to achieve one-time-pad encryption approach for the wireless communication system. However, in a static environment, the secret key rate is low due to the lack of channel time-variation. To solve this problem, this paper proposes a novel secret key generation scheme assisted by an intelligent reflecting surface with discrete phase shifts. In the scheme, legitimate nodes construct the dynamic time-varying channel by rapidly and randomly switching the phase of IRS elements. Then, channel coefficients are used to generate the secret key. Based on the IRS channel model, we derive the probability distribution of the channel coefficient and the expressions of the secret key rate. Furthermore, we optimize the secret key rate by adjusting the switching time of the IRS phase. Monte Carlo simulation and numerical results show that our proposed scheme can update the secret key in a static environment.

Instructions: 

  In this section, we present results to evaluate the proposed scheme. To ensure the accuracy, we perform Monte Carlo simulation to generate 100000 random channel coefficients in every coherence time slots. Unless otherwise noted, we set σ2= σ2 n= 1, α = 1, Pt= PA= PB, and SNR=γ=γa=γbin the following simulation. 、

First, we plot 1000 channel estimation samples in Fig.2. It can be observed from Fig.2(a) that the channel coefficient follows the complex Gaussian mixture distribution when N= 5 and has KNdifferent values. Fig.2(b) and Fig.2(d) (i.e., the cumulative probability distribution) indicate that the real parts of the channel coefficients approximately follow Gaussian distribution when N= 20. In addition, it can be observed from Fig.2(b) that the channel coefficients of Alice and Bob are very similar, but they are quite different from Eve’s in Fig.2(c), which proves that the eavesdropping channel and the legitimate channel are uncorrelated as [1]–[3] and [9].

Comments

I want to use the dataset for my research purpose. Please allow me to access the dataset.

Submitted by RAJASEKHARREDDY... on Sun, 05/30/2021 - 01:24