One-Bit Statistical ECSI Learning for Single-Group Multicast Secure Beamforming

Simeng Fan; Jing Xu; Jiang Xue; Yizhai Zhang

doi:10.1109/LCOMM.2022.3196401

One-Bit Statistical ECSI Learning for Single-Group Multicast Secure Beamforming

Simeng Fan
, Jing Xu
, Jiang Xue
, Yizhai Zhang

School of Energy and Power Engineering

Xi'an Jiaotong University
Northwestern Polytechnical University Xian

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

This letter investigates a binary learning mechanism for statistical eavesdroppers' channel state information (SECSI), in which the transmitter utilizes one-bit signal-to-noise-ratio feedback to constantly learn the channel correlation matrices of the eavesdropping links without any prior SECSI. Correspondingly, with the updated SECSI estimate, the optimal single-group multicast secure beamforming (SGMC-SBF) is determined and probed continually for multicast secrecy rate maximization. Simulation results show that this convergent cognitive strategy could not only achieve higher ergodic multicast secrecy rate than the worst-case robust SGMC-SBF with imperfect SECSI, but also approach the ideal performance achieved by perfect SECSI.

Original language	English
Pages (from-to)	2552-2556
Number of pages	5
Journal	IEEE Communications Letters
Volume	26
Issue number	11
DOIs	https://doi.org/10.1109/LCOMM.2022.3196401
State	Published - 1 Nov 2022

Keywords

Binary received signal-to-noise-ratio feedback
channel correlation
convex optimization
physical-layer security
single-group multicast secure beamforming (SGMC-SBF)

Access to Document

10.1109/LCOMM.2022.3196401

Cite this

@article{5a1d053cb391448387b1baaa08f7bb5a,

title = "One-Bit Statistical ECSI Learning for Single-Group Multicast Secure Beamforming",

abstract = "This letter investigates a binary learning mechanism for statistical eavesdroppers' channel state information (SECSI), in which the transmitter utilizes one-bit signal-to-noise-ratio feedback to constantly learn the channel correlation matrices of the eavesdropping links without any prior SECSI. Correspondingly, with the updated SECSI estimate, the optimal single-group multicast secure beamforming (SGMC-SBF) is determined and probed continually for multicast secrecy rate maximization. Simulation results show that this convergent cognitive strategy could not only achieve higher ergodic multicast secrecy rate than the worst-case robust SGMC-SBF with imperfect SECSI, but also approach the ideal performance achieved by perfect SECSI.",

keywords = "Binary received signal-to-noise-ratio feedback, channel correlation, convex optimization, physical-layer security, single-group multicast secure beamforming (SGMC-SBF)",

author = "Simeng Fan and Jing Xu and Jiang Xue and Yizhai Zhang",

note = "Publisher Copyright: {\textcopyright} 1997-2012 IEEE.",

year = "2022",

month = nov,

day = "1",

doi = "10.1109/LCOMM.2022.3196401",

language = "英语",

volume = "26",

pages = "2552--2556",

journal = "IEEE Communications Letters",

issn = "1089-7798",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "11",

}

TY - JOUR

T1 - One-Bit Statistical ECSI Learning for Single-Group Multicast Secure Beamforming

AU - Fan, Simeng

AU - Xu, Jing

AU - Xue, Jiang

AU - Zhang, Yizhai

PY - 2022/11/1

Y1 - 2022/11/1

N2 - This letter investigates a binary learning mechanism for statistical eavesdroppers' channel state information (SECSI), in which the transmitter utilizes one-bit signal-to-noise-ratio feedback to constantly learn the channel correlation matrices of the eavesdropping links without any prior SECSI. Correspondingly, with the updated SECSI estimate, the optimal single-group multicast secure beamforming (SGMC-SBF) is determined and probed continually for multicast secrecy rate maximization. Simulation results show that this convergent cognitive strategy could not only achieve higher ergodic multicast secrecy rate than the worst-case robust SGMC-SBF with imperfect SECSI, but also approach the ideal performance achieved by perfect SECSI.

AB - This letter investigates a binary learning mechanism for statistical eavesdroppers' channel state information (SECSI), in which the transmitter utilizes one-bit signal-to-noise-ratio feedback to constantly learn the channel correlation matrices of the eavesdropping links without any prior SECSI. Correspondingly, with the updated SECSI estimate, the optimal single-group multicast secure beamforming (SGMC-SBF) is determined and probed continually for multicast secrecy rate maximization. Simulation results show that this convergent cognitive strategy could not only achieve higher ergodic multicast secrecy rate than the worst-case robust SGMC-SBF with imperfect SECSI, but also approach the ideal performance achieved by perfect SECSI.

KW - Binary received signal-to-noise-ratio feedback

KW - channel correlation

KW - convex optimization

KW - physical-layer security

KW - single-group multicast secure beamforming (SGMC-SBF)

UR - https://www.scopus.com/pages/publications/85135760848

U2 - 10.1109/LCOMM.2022.3196401

DO - 10.1109/LCOMM.2022.3196401

M3 - 文章

AN - SCOPUS:85135760848

SN - 1089-7798

VL - 26

SP - 2552

EP - 2556

JO - IEEE Communications Letters

JF - IEEE Communications Letters

IS - 11

ER -

One-Bit Statistical ECSI Learning for Single-Group Multicast Secure Beamforming

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this