218 research outputs found
Spatially Selective Artificial-Noise Aided Transmit Optimization for MISO Multi-Eves Secrecy Rate Maximization
Consider an MISO channel overheard by multiple eavesdroppers. Our goal is to
design an artificial noise (AN)-aided transmit strategy, such that the
achievable secrecy rate is maximized subject to the sum power constraint.
AN-aided secure transmission has recently been found to be a promising approach
for blocking eavesdropping attempts. In many existing studies, the confidential
information transmit covariance and the AN covariance are not simultaneously
optimized. In particular, for design convenience, it is common to prefix the AN
covariance as a specific kind of spatially isotropic covariance. This paper
considers joint optimization of the transmit and AN covariances for secrecy
rate maximization (SRM), with a design flexibility that the AN can take any
spatial pattern. Hence, the proposed design has potential in jamming the
eavesdroppers more effectively, based upon the channel state information (CSI).
We derive an optimization approach to the SRM problem through both analysis and
convex conic optimization machinery. We show that the SRM problem can be recast
as a single-variable optimization problem, and that resultant problem can be
efficiently handled by solving a sequence of semidefinite programs. Our
framework deals with a general setup of multiple multi-antenna eavesdroppers,
and can cater for additional constraints arising from specific application
scenarios, such as interference temperature constraints in interference
networks. We also generalize the framework to an imperfect CSI case where a
worst-case robust SRM formulation is considered. A suboptimal but safe solution
to the outage-constrained robust SRM design is also investigated. Simulation
results show that the proposed AN-aided SRM design yields significant secrecy
rate gains over an optimal no-AN design and the isotropic AN design, especially
when there are more eavesdroppers.Comment: To appear in IEEE Trans. Signal Process., 201
Optimal and Robust Transmit Designs for MISO Channel Secrecy by Semidefinite Programming
In recent years there has been growing interest in study of multi-antenna
transmit designs for providing secure communication over the physical layer.
This paper considers the scenario of an intended multi-input single-output
channel overheard by multiple multi-antenna eavesdroppers. Specifically, we
address the transmit covariance optimization for secrecy-rate maximization
(SRM) of that scenario. The challenge of this problem is that it is a nonconvex
optimization problem. This paper shows that the SRM problem can actually be
solved in a convex and tractable fashion, by recasting the SRM problem as a
semidefinite program (SDP). The SRM problem we solve is under the premise of
perfect channel state information (CSI). This paper also deals with the
imperfect CSI case. We consider a worst-case robust SRM formulation under
spherical CSI uncertainties, and we develop an optimal solution to it, again
via SDP. Moreover, our analysis reveals that transmit beamforming is generally
the optimal transmit strategy for SRM of the considered scenario, for both the
perfect and imperfect CSI cases. Simulation results are provided to illustrate
the secrecy-rate performance gains of the proposed SDP solutions compared to
some suboptimal transmit designs.Comment: 32 pages, 5 figures; to appear, IEEE Transactions on Signal
Processing, 201
A Decentralized Method for Joint Admission Control and Beamforming in Coordinated Multicell Downlink
In cellular networks, admission control and beamforming optimization are
intertwined problems. While beamforming optimization aims at satisfying users'
quality-of-service (QoS) requirements or improving the QoS levels, admission
control looks at how a subset of users should be selected so that the
beamforming optimization problem can yield a reasonable solution in terms of
the QoS levels provided. However, in order to simplify the design, the two
problems are usually seen as separate problems. This paper considers joint
admission control and beamforming (JACoB) under a coordinated multicell MISO
downlink scenario. We formulate JACoB as a user number maximization problem,
where selected users are guaranteed to receive the QoS levels they requested.
The formulated problem is combinatorial and hard, and we derive a convex
approximation to the problem. A merit of our convex approximation formulation
is that it can be easily decomposed for per-base-station decentralized
optimization, namely, via block coordinate decent. The efficacy of the proposed
decentralized method is demonstrated by simulation results.Comment: 2012 IEEE Asilomar Conference on Signals, Systems, and Computer
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