Linear Precoding of Data and Artificial Noise in Secure Massive MIMO Systems

In this paper, we consider secure downlink transmission in a multicell massive multiple-input multiple-output (MIMO) system where the numbers of base station (BS) antennas, mobile terminals, and eavesdropper antennas are asymptotically large. The channel state information of the eavesdropper is assu...

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Vydané v:IEEE transactions on wireless communications Ročník 15; číslo 3; s. 2245 - 2261
Hlavní autori: Jun Zhu, Schober, Robert, Bhargava, Vijay K.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.03.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
artificial noise
Collaboration
Complexity theory
computational complexity and matrix polynomial
Contamination
Downlink
ergodic secrecy rate
Interference
massive MIMO
MIMO
multicell systems
Physical layer security
pilot contamination
Polynomials
artificial noise
pilot contamination
and matrix polynomial
ergodic secrecy rate
Physical layer security
massive MIMO
multi-cell systems
computational complexity
ISSN:1536-1276, 1558-2248
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Shrnutí:In this paper, we consider secure downlink transmission in a multicell massive multiple-input multiple-output (MIMO) system where the numbers of base station (BS) antennas, mobile terminals, and eavesdropper antennas are asymptotically large. The channel state information of the eavesdropper is assumed to be unavailable at the BS and hence, linear precoding of data and artificial noise (AN) are employed for secrecy enhancement. Four different data precoders (i.e., selfish zero-forcing (ZF)/regularized channel inversion (RCI) and collaborative ZF/RCI precoders) and three different AN precoders (i.e., random, selfish/collaborative null-space-based precoders) are investigated and the corresponding achievable ergodic secrecy rates are analyzed. Our analysis includes the effects of uplink channel estimation, pilot contamination, multicell interference, and path-loss. Furthermore, to strike a balance between complexity and performance, linear precoders that are based on matrix polynomials are proposed for both data and AN precoding. The polynomial coefficients of the data and AN precoders are optimized, respectively, for minimization of the sum-mean-squared-error of and the AN leakage to the mobile terminals in the cell of interest using tools from free probability and random matrix theory. Our analytical and simulation results provide interesting insights for the design of secure multicell massive MIMO systems and reveal that the proposed polynomial data and AN precoders closely approach the performance of selfish RCI data and null-space-based AN precoders, respectively.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2015.2500578