Secrecy Sum-Rates for Multi-User MIMO Regularized Channel Inversion Precoding
In this paper, we propose a linear precoder for the downlink of a multi-user MIMO system with multiple users that potentially act as eavesdroppers. The proposed precoder is based on regularized channel inversion (RCI) with a regularization parameter α and power allocation vector chosen in such a way...
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| Published in: | IEEE transactions on communications Vol. 60; no. 11; pp. 3472 - 3482 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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New York, NY
IEEE
01.11.2012
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0090-6778, 1558-0857 |
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| Abstract | In this paper, we propose a linear precoder for the downlink of a multi-user MIMO system with multiple users that potentially act as eavesdroppers. The proposed precoder is based on regularized channel inversion (RCI) with a regularization parameter α and power allocation vector chosen in such a way that the achievable secrecy sum-rate is maximized. We consider the worst-case scenario for the multi-user MIMO system, where the transmitter assumes users cooperate to eavesdrop on other users. We derive the achievable secrecy sum-rate and obtain the closed-form expression for the optimal regularization parameter α LS of the precoder using large-system analysis. We show that the RCI precoder with α LS outperforms several other linear precoding schemes, and it achieves a secrecy sum-rate that has same scaling factor as the sum-rate achieved by the optimum RCI precoder without secrecy requirements. We propose a power allocation algorithm to maximize the secrecy sum-rate for fixed α. We then extend our algorithm to maximize the secrecy sum-rate by jointly optimizing α and the power allocation vector. The jointly optimized precoder outperforms RCI with α LS and equal power allocation by up to 20 percent at practical values of the signal-to-noise ratio and for 4 users and 4 transmit antennas. |
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| AbstractList | In this paper, we propose a linear precoder for the downlink of a multi-user MIMO system with multiple users that potentially act as eavesdroppers. The proposed precoder is based on regularized channel inversion (RCI) with a regularization parameter α and power allocation vector chosen in such a way that the achievable secrecy sum-rate is maximized. We consider the worst-case scenario for the multi-user MIMO system, where the transmitter assumes users cooperate to eavesdrop on other users. We derive the achievable secrecy sum-rate and obtain the closed-form expression for the optimal regularization parameter α LS of the precoder using large-system analysis. We show that the RCI precoder with α LS outperforms several other linear precoding schemes, and it achieves a secrecy sum-rate that has same scaling factor as the sum-rate achieved by the optimum RCI precoder without secrecy requirements. We propose a power allocation algorithm to maximize the secrecy sum-rate for fixed α. We then extend our algorithm to maximize the secrecy sum-rate by jointly optimizing α and the power allocation vector. The jointly optimized precoder outperforms RCI with α LS and equal power allocation by up to 20 percent at practical values of the signal-to-noise ratio and for 4 users and 4 transmit antennas. In this paper, we propose a linear precoder for the downlink of a multi-user MIMO system with multiple users that potentially act as eavesdroppers. The proposed precoder is based on regularized channel inversion (RCI) with a regularization parameter alpha and power allocation vector chosen in such a way that the achievable secrecy sum-rate is maximized. We consider the worst-case scenario for the multi-user MIMO system, where the transmitter assumes users cooperate to eavesdrop on other users. We derive the achievable secrecy sum-rate and obtain the closed-form expression for the optimal regularization parameter alpha _{LS} of the precoder using large-system analysis. We show that the RCI precoder with alpha _{LS} outperforms several other linear precoding schemes, and it achieves a secrecy sum-rate that has same scaling factor as the sum-rate achieved by the optimum RCI precoder without secrecy requirements. We propose a power allocation algorithm to maximize the secrecy sum-rate for fixed alpha . We then extend our algorithm to maximize the secrecy sum-rate by jointly optimizing alpha and the power allocation vector. The jointly optimized precoder outperforms RCI with alpha _{LS} and equal power allocation by up to 20 percent at practical values of the signal-to-noise ratio and for 4 users and 4 transmit antennas. |
| Author | Jinhong Yuan Geraci, G. Collings, I. B. Razi, A. Egan, M. |
| Author_xml | – sequence: 1 givenname: G. surname: Geraci fullname: Geraci, G. email: g.geraci@student.unsw.edu.au organization: Sch. of Electr. Eng. & Telecommun., Univ. of New South Wales, Sydney, NSW, Australia – sequence: 2 givenname: M. surname: Egan fullname: Egan, M. email: m.egan@sydney.edu.au organization: Sch. of Electr. & Inf. Eng., Univ. of Sydney, Sydney, NSW, Australia – sequence: 3 surname: Jinhong Yuan fullname: Jinhong Yuan email: jinhong@ee.unsw.edu.au organization: Sch. of Electr. Eng. & Telecommun., Univ. of New South Wales, Sydney, NSW, Australia – sequence: 4 givenname: A. surname: Razi fullname: Razi, A. email: adeel.razi@ieee.org organization: Dept. of Electron. Eng., NED Univ. of Eng. & Technol., Karachi, Pakistan – sequence: 5 givenname: I. B. surname: Collings fullname: Collings, I. B. email: iain.collings@sciro.au organization: Wireless & Networking Technol. Lab., CSIRO ICT Centre, Sydney, NSW, Australia |
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| Keywords | Performance evaluation MIMO system Multiple access Secrecy rate Coding circuit Worst case method multi-user communications Transmitter Downlink precoding Algorithm Power allocation Channel coding Optimization MIMO systems Transmitting antenna Pretreatment Safety physical layer security System analysis Secrecy Signal to noise ratio Large scale system |
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| SubjectTerms | Algorithms Allocations Applied sciences Channels Coding, codes Exact sciences and technology Information, signal and communications theory Interference Inversions Mathematical analysis MIMO MIMO systems multi-user communications Optimization Optimized production technology physical layer security precoding Radiocommunications Regularization Resource management Secrecy rate Secret Signal and communications theory Signal to noise ratio Studies Systems, networks and services of telecommunications Telecommunications Telecommunications and information theory Transmission and modulation (techniques and equipments) Transmitters. Receivers Vectors Vectors (mathematics) |
| Title | Secrecy Sum-Rates for Multi-User MIMO Regularized Channel Inversion Precoding |
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