Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (10): 3014-3025.doi: 10.13229/j.cnki.jdxbgxb.20211326

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Security status updates based on intelligent reflecting surface assistance and age of information metrics

Bao-gang LI1(),Yu WANG1,Fan-wei KONG2,Cheng-wei TIAN1   

  1. 1.Department of Electronic and Communication Engineering，North China Electric Power University（Baoding），Baoding 071000，China
    2.State Grid Hebei Electric Power Co. Ltd. ，Shijiazhuang 050022，China
  • Received:2021-12-03 Online:2023-10-01 Published:2023-12-13

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Abstract:

In order to meet the requirements of the Internet of everything and physical layer security， a design method of secure beamforming assisted by intelligent reflecting surface is proposed to realize active defense against the communication security of status update system with multiple eavesdroppers and multiple legitimate users.Meanwhile， the requirement of information freshness in the status update scenario is considered， so the information age performance metrics of average confidentiality age and interruption probability of confidentiality age are introduced. Under the constraints of transmit power， state update probability and IRS reflection phase shift， the beamforming matrix， IRS phase shift and state update probability are jointly optimized to ensure state update while maximizing system security. For the dynamics and complexity of the system and the challenge of non-convex optimization， a secure beamforming method based on double deep Q network algorithm is proposed to realize the optimal beamforming strategy for eavesdroppers in dynamic environment. The simulation results show that the proposed secure beamforming method based on double deep Q network can significantly improve the security of IRS assisted state updating system.

Key words: intelligent reflecting surface, physical layer security, age of information, secrecy age, secrecy age outage probability, reinforcement learning

CLC Number: 

  • TN92

Fig.1

IRS auxiliary status update system with multipleeavesdroppers"

Fig.2

State transition diagram of dk，m （i，j）"

Fig.3

DDQN algorithm flow chart"

Fig.4

System deployment"

Table 1

Simulation parameter setting"

参数名称取值参数名称取值
PL030 dBnm-90 dB·m
?3nk-90 dB·m
Rs5 bits/s/Hzγ0.02
N4δ0.95
K2ε0.1
M2σth0.6

Fig.5

Convergence comparison of various methods"

Fig.6

Influence of maximum BS transmitting power on performance"

Fig.7

Effect of IRS element number on performance"

Fig.8

Influence of status update probability onperformance"

Fig.9

Influence of number of users and channel lossindex on performance"

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