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

Previous Articles     Next Articles

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

RICH HTML

 8   

PDF (PC)

310

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"

1 Yang N, Wang L, Geraci G, et al. Safeguarding 5G wireless communication networks using physical layer security[J]. IEEE Commun, 2015, 53(4): 20-27.
2 Wyner A D. The wiretap channel[J]. Bell Syst Tech J, 1975, 54(8): 1355-1387.
3 Li Q, Yang L. Beamforming for cooperative secure transmission in cognitive two-way relay networks[J]. IEEE Trans Inf Forensics Security, 2020, 15(4): 130-143.
4 Xiao L, Lu X, Xu D, et al. UAV relay in VANETs against smart jamming with reinforcement learning[J]. IEEE Trans Veh Technol, 2018, 67(5): 4087-4097.
5 Wang W, Teh K C, Li K H. Artificial noise aided physical layer security in multi-antenna small-cell networks[J]. IEEE Trans Inf Forensics Security, 2017, 12(6): 1470-1482.
6 Wang H, Zheng T, Xia X. Secure MISO wiretap channels with multiantenna passive eavesdropper: artificial noise vs. artificial fast fading[J]. IEEE Trans Wireless Commun, 2015, 14(1): 94-106.
7 Nakai R, Sugiura S. Physical layer security in buffer-state-based max-ratio relay selection exploiting broadcasting with cooperative beamforming and jamming[J]. IEEE Trans Inf Forensics Security, 2019, 14(2): 431-444.
8 Mobini Z, Mohammadi M, Tellambura C. Wireless-powered full-duplex relay and friendly jamming for secure cooperative communications[J]. IEEE Trans Inf Forensics Security, 2019, 14(3): 621-634.
9 Wu Q, Zhang R. Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network[J]. IEEE Commun, 2020, 58(1): 106-112.
10 Zhao J. A survey of intelligent reflecting surfaces (IRSs): towards 6G wireless communication networks with massive MIMO 2.0[J]. Electrical Engineering and Systems Science, 2019(2): 180-188.
11 Han H, Zhao J, Niyato D, et al. Intelligent reflecting surface aided network: power control for physical-layer broadcasting[C]∥IEEE International Conference on Communications(ICC), Dublin, Ireland, 2020: 1-7.
12 Huang C, Zappone A, Alexandropoulos G C, et al. Reconfigurable intelligent surfaces for energy efficiency in wireless communication[J]. IEEE Trans Wireless Commun, 2019, 18(8): 4517-4170.
13 Wu Q, Zhang R. Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming[J]. IEEE Trans Wireless Commun, 2019, 18(11): 5394-5409.
14 Liu Q, Yang J, Xu Y, et al.Energy-efficient Resource allocation for secure IRS networks with an active eavesdropper[C]∥IEEE/CIC International Conference on Communications in China(ICCC), Shanghai,China, 2020: 1-5.
15 Tiong T. Robust secure SWIPT MISO[C]∥The 7th International Conference on Smart Computing & Communications(ICSCC), Sarawak, Malaysia, 2019: 1-5.
16 Xu D, Yu X, Sun Y, et al. Resource allocation for secure IRS-assisted multiuser MISO systems[C]∥IEEE Globecom Workshops(GC Wkshps), Waikoloa, USA, 2019: 1-6.
17 Zhou G, Pan C, Ren H, et al. Secure wireless communication in RIS-aided MISO system with hardware impairments[J]. IEEE Wireless Communications Letters, 10(6): 1309-1313.
18 Hong S, Pan C, Ren H, et al. Artificial-noise-aided secure MIMO wireless communications via intelligent reflecting surface[J]. IEEE Transactions on Communications, 2020, 68(12): 7851-7866.
19 Dong L, Wang H. Secure MIMO transmission via intelligent reflecting surface[J]. IEEE Wireless Communications Letters, 2020, 9(6): 787-790.
20 Dong L, Wang H M, Xiao H, et al. Secure intelligent reflecting surface assisted MIMO cognitive radio transmission[C]∥IEEE Wireless Communications and Networking Conference(WCNC), Nanjing,China, 2021: 1-6.
21 Dong L, Wang H. Enhancing secure MIMO transmission via intelligent reflecting surface[J]. IEEE Transactions on Wireless Communications, 2020, 19(11): 7543-7556.
22 Zhang Z, Lv L, Wu Q, et al. Robust and secure communications in intelligent reflecting surface assisted NOMA networks[J]. IEEE Communications Letters, 2021, 25(3): 739-743.
23 Xiu Y, Wu J, Gui G, et al. Artificial noise-aided secure SWIPT communication systems using intelligent reflecting surface[C]∥IEEE/CIC International Conference on Communications in China(ICCC), Shanghai,China, 2020: 6-11.
24 Xue J, Zhou X, Wang C, et al. Hybrid precoding for IRS-assisted secure mmwave communication system with SWIPT[C]∥International Conference on Space-Air-GroundComputing(SAGC), Beijing,China, 2020: 82-86.
25 Psomas C, Krikidis I. On the effect of correlation in IRS-aided SWIPT networks[C]∥IEEE Wireless Power Transfer Conference (WPTC), Seoul, Korea, 2020: 460-462.
26 Yang H, Xiong Z, Zhao J, et al. Deep reinforcement learning-based intelligent reflecting surface for secure wireless communications[J]. IEEE Transactions on Wireless Communications, 2021, 20(1): 375-388.
27 Kaul S, Yates R, Gruteser M. Real-time status: How often should one update?[J]. Proc IEEE INFOCOM, 2012, 18(6): 2731-2735.
28 Soysal A, Ulukus S. Age of Information in G/G/1/1 systems: age expressions, bounds, special cases, and optimization, 2018[J]. IEEE Transactions on Information Theory, 2021, 67(11): 7477-7489.
29 Inoue Y, Masuyama H, Takine T,et al. A general formula for the stationary distribution of the age of information and its application to single-server queues[J]. IEEE Transactions on Information Theory, 2019, 65(12): 8305-8324.
30 Zheng X, Zhou S, Jiang Z, et al. Closed-form analysis of non-linear age of information in status updates with an energy harvesting transmitter[J]. IEEE Trans Wireless Commun, 2019, 18(8): 4129-4142.
31 Arafa A, Yang J, Ulukus S, et al. Age-minimal transmission for energy harvesting sensors with finite batteries:online policies[J]. IEEE Transactions on Information Theory, 2020, 66(1): 534-556.
32 Kam C, Kompella S, Nguyen G D, et al. Effect of message transmission path diversity on status age[J]. IEEE Trans Inf Theory, 2016, 62(3): 1360-1374.
33 Costa M, Codreanu M, Ephremides A. On the age of informationin status update systems with packet management[J]. IEEE Trans Inf Theory, 2016, 62(4): 1897-1910.
34 Guo X, Singh R, Kumar P R, et al. A risk-sensitive approach for packet inter-delivery time optimization in networked cyber-physical systems[J]. IEEE/ACM Trans Netw, 2018, 26(4): 1976-1989.
35 Bacinoglu B T, Ceran E T, Uysal-Biyikoglu E. Age of information under energy replenishment constraints[C]// Proc Inf Theory Appl Workshop(ITA),San Diego, USA, 2015;25-31.
36 Sun Y, Uysal-Biyikoglu E, Yates R D, et al. Update or wait: how to keep your data fresh[J]. IEEE Trans Inf Theory, 2017, 63(11): 7492-7508.
37 Xiao Y, Sun Y. A dynamic jamming game for real-time status updates[C]∥IEEE Conference on Computer Communications Workshops(INFOCOM WKSHPS), Honolulu, USA, 2018: 354-360.
38 Garnaev A, Zhang W, Zhong J, et al. Maintaining information freshness under jamming[C]∥IEEE Conference on Computer Communications Workshops, Paris, France, 2019: 90-95.
39 Chen H, Wang Q, Mohapatra P, et al. Secure status updates under eavesdropping: age of information-based physical layer security metrics[J/OL]. [2020-12-20]..
40 Chu Z, Xiao P, Shojafar M D,et al. Intelligent reflecting surface assisted mobile edge computing for Internet of Things[J]. IEEE Wireless Communications Letters, 2021, 10(3): 619-623.
41 Huang C, Chen G, Gong Y, et al. Deep reinforcement learning-based relay selection in intelligent reflecting surface assisted cooperative networks[J]. IEEE Wireless Communications Letters, 2021, 10(5): 1036-1040.
42 Cui M, Zhang G, Zhang R. Secure wireless communication via intelligent reflecting surface[J]. IEEE Wireless Commun Letter, 2019, 8(5): 1410-1414.
[1] Jian ZHANG,Qing-yang LI,Dan LI,Xia JIANG,Yan-hong LEI,Ya-ping JI. Merging guidance of exclusive lanes for connected and autonomous vehicles based on deep reinforcement learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(9): 2508-2518.
[2] Yan-tao TIAN,Yan-shi JI,Huan CHANG,Bo XIE. Deep reinforcement learning augmented decision⁃making model for intelligent driving vehicles [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 682-692.
[3] Wei-chao ZHUANG,Hao-nan Ding,Hao-xuan DONG,Guo-dong YIN,Xi WANG,Chao-bin ZHOU,Li-wei XU. Learning based eco⁃driving strategy of connected electric vehicle at signalized intersection [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(1): 82-93.
[4] Yong LIU,Lei XU,Chu-han ZHANG. Deep reinforcement learning model for text games [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 666-674.
[5] Zhong-li WANG,Hao WANG,Yan SHEN,Bai-gen CAI. A driving decision⁃making approach based on multi⁃sensing and multi⁃constraints reward function [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2718-2727.
[6] Bin XIAN,Shi-jing ZHANG,Xiao-wei HAN,Jia-ming CAI,Ling WANG. Trajectory planning for unmanned aerial vehicle slung⁃payload aerial transportation system based on reinforcement learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2259-2267.
[7] Ya-hui ZHAO,Fei-yang YANG,Zhen-guo ZHANG,Rong-yi CUI. Korean text structure discovery based on reinforcement learning and attention mechanism [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1387-1395.
[8] Shuai LYU,Jing LIU. Stochastic local search heuristic method based on deep reinforcement learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1420-1426.
[9] Li-na LI,Xiao-hui WEI,Lin-lin HAO,Xing-wang WANG,Chu WANG. Cost⁃effective elastic resource allocation strategy in large⁃scale streaming data processing [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1832-1843.
[10] Shun YANG,Yuan⁃de JIANG,Jian WU,Hai⁃zhen LIU. Autonomous driving policy learning based on deep reinforcement learning and multi⁃type sensor data [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1026-1033.
[11] LEI Wei-jia, ZUO Li-jie, XIE Xian-zhong. Eavesdropping-resistant cooperative relay transmission scheme with partial channel state information [J]. 吉林大学学报(工学版), 2015, 45(5): 1658-1664.
[12] DONG Bo,LIU Ke-ping,LI Yuan-chun. Decentralized reinforcement learning optimal control for time varying constrained reconfigurable modular robot [J]. 吉林大学学报(工学版), 2014, 44(5): 1375-1384.
[13] YE Jin-hua,LI Di,YE Feng. Dual reinforcement learning adaptive fuzzy control of wheeled mobile robot [J]. 吉林大学学报(工学版), 2014, 44(3): 742-749.
[14] LIU Quan, YANG Xu-dong, JING Ling, XIAO Fei. Improved E3 algorithm based on multi-agent parallel sampling and learning experience reuse [J]. 吉林大学学报(工学版), 2013, 43(01): 135-140.
[15] FU Yan-Ning, ZHANG Jia-Chen, LIU Lei. Process oriented WS composition via reinforcement learning [J]. 吉林大学学报(工学版), 2010, 40(05): 1313-1317.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd