555 research outputs found
Robust Secure Transmission in MISO Channels Based on Worst-Case Optimization
This paper studies robust transmission schemes for multiple-input
single-output (MISO) wiretap channels. Both the cases of direct transmission
and cooperative jamming with a helper are investigated with imperfect channel
state information (CSI) for the eavesdropper links. Robust transmit covariance
matrices are obtained based on worst-case secrecy rate maximization, under both
individual and global power constraints. For the case of an individual power
constraint, we show that the non-convex maximin optimization problem can be
transformed into a quasiconvex problem that can be efficiently solved with
existing methods. For a global power constraint, the joint optimization of the
transmit covariance matrices and power allocation between the source and the
helper is studied via geometric programming. We also study the robust wiretap
transmission problem for the case with a quality-of-service constraint at the
legitimate receiver. Numerical results show the advantage of the proposed
robust design. In particular, for the global power constraint scenario,
although cooperative jamming is not necessary for optimal transmission with
perfect eavesdropper's CSI, we show that robust jamming support can increase
the worst-case secrecy rate and lower the signal to interference-plus-noise
ratio at Eve in the presence of channel mismatches between the transmitters and
the eavesdropper.Comment: 28 pages, 5 figure
Jamming Games in the MIMO Wiretap Channel With an Active Eavesdropper
This paper investigates reliable and covert transmission strategies in a
multiple-input multiple-output (MIMO) wiretap channel with a transmitter,
receiver and an adversarial wiretapper, each equipped with multiple antennas.
In a departure from existing work, the wiretapper possesses a novel capability
to act either as a passive eavesdropper or as an active jammer, under a
half-duplex constraint. The transmitter therefore faces a choice between
allocating all of its power for data, or broadcasting artificial interference
along with the information signal in an attempt to jam the eavesdropper
(assuming its instantaneous channel state is unknown). To examine the resulting
trade-offs for the legitimate transmitter and the adversary, we model their
interactions as a two-person zero-sum game with the ergodic MIMO secrecy rate
as the payoff function. We first examine conditions for the existence of
pure-strategy Nash equilibria (NE) and the structure of mixed-strategy NE for
the strategic form of the game.We then derive equilibrium strategies for the
extensive form of the game where players move sequentially under scenarios of
perfect and imperfect information. Finally, numerical simulations are presented
to examine the equilibrium outcomes of the various scenarios considered.Comment: 27 pages, 8 figures. To appear, IEEE Transactions on Signal
Processin
SVM-Based Channel Estimation and Data Detection for One-Bit Massive MIMO systems
The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this paper, we show how Support Vector Machine (SVM), a well-known supervised-learning technique in machine learning, can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, the problem of channel estimation for uncorrelated channels is formulated as a conventional SVM problem. The objective function of this SVM problem is then modified for estimating spatially correlated channels. Next, a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. The performance of the proposed data detection method is very close to that of Maximum-Likelihood (ML) data detection when the channel is perfectly known. We also propose an SVM-based joint Channel Estimation and Data Detection (CE-DD) method, which makes use of both the to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance. Finally, an extension of the proposed methods to OFDM systems with frequency-selective fading channels is presented. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones
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