385 research outputs found
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 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 of the precoder using
large-system analysis. We show that the RCI precoder with
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 .
We then extend our algorithm to maximize the secrecy sum-rate by jointly
optimizing and the power allocation vector. The jointly optimized
precoder outperforms RCI with 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.Comment: IEEE Transactions on Communications, accepted for publicatio
Secrecy Sum-Rates with Regularized Channel Inversion Precoding under Imperfect CSI at the Transmitter
In this paper, we study the performance of regularized channel inversion
precoding in MISO broadcast channels with confidential messages under imperfect
channel state information at the transmitter (CSIT). We obtain an approximation
for the achievable secrecy sum-rate which is almost surely exact as the number
of transmit antennas and the number of users grow to infinity in a fixed ratio.
Simulations prove this anaylsis accurate even for finite-size systems. For FDD
systems, we determine how the CSIT error must scale with the SNR, and we derive
the number of feedback bits required to ensure a constant high-SNR rate gap to
the case with perfect CSIT. For TDD systems, we study the optimum amount of
channel training that maximizes the high-SNR secrecy sum-rate.Comment: IEEE International Conference on Acoustics, Speech, and Signal
Processing (ICASSP), May 2013. arXiv admin note: text overlap with
arXiv:1304.585
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