141 research outputs found
Artificial Noise-Aided Biobjective Transmitter Optimization for Service Integration in Multi-User MIMO Gaussian Broadcast Channel
This paper considers an artificial noise (AN)-aided transmit design for
multi-user MIMO systems with integrated services. Specifically, two sorts of
service messages are combined and served simultaneously: one multicast message
intended for all receivers and one confidential message intended for only one
receiver and required to be perfectly secure from other unauthorized receivers.
Our interest lies in the joint design of input covariances of the multicast
message, confidential message and artificial noise (AN), such that the
achievable secrecy rate and multicast rate are simultaneously maximized. This
problem is identified as a secrecy rate region maximization (SRRM) problem in
the context of physical-layer service integration. Since this bi-objective
optimization problem is inherently complex to solve, we put forward two
different scalarization methods to convert it into a scalar optimization
problem. First, we propose to prefix the multicast rate as a constant, and
accordingly, the primal biobjective problem is converted into a secrecy rate
maximization (SRM) problem with quality of multicast service (QoMS) constraint.
By varying the constant, we can obtain different Pareto optimal points. The
resulting SRM problem can be iteratively solved via a provably convergent
difference-of-concave (DC) algorithm. In the second method, we aim to maximize
the weighted sum of the secrecy rate and the multicast rate. Through varying
the weighted vector, one can also obtain different Pareto optimal points. We
show that this weighted sum rate maximization (WSRM) problem can be recast into
a primal decomposable form, which is amenable to alternating optimization (AO).
Then we compare these two scalarization methods in terms of their overall
performance and computational complexity via theoretical analysis as well as
numerical simulation, based on which new insights can be drawn.Comment: 14 pages, 5 figure
On the Secrecy Capacity of MIMO Wiretap Channels: Convex Reformulation and Efficient Numerical Methods
This paper presents novel numerical approaches to finding the secrecy
capacity of the multiple-input multiple-output (MIMO) wiretap channel subject
to multiple linear transmit covariance constraints, including sum power
constraint, per antenna power constraints and interference power constraint. An
analytical solution to this problem is not known and existing numerical
solutions suffer from slow convergence rate and/or high per-iteration
complexity. Deriving computationally efficient solutions to the secrecy
capacity problem is challenging since the secrecy rate is expressed as a
difference of convex functions (DC) of the transmit covariance matrix, for
which its convexity is only known for some special cases. In this paper we
propose two low-complexity methods to compute the secrecy capacity along with a
convex reformulation for degraded channels. In the first method we capitalize
on the accelerated DC algorithm which requires solving a sequence of convex
subproblems, for which we propose an efficient iterative algorithm where each
iteration admits a closed-form solution. In the second method, we rely on the
concave-convex equivalent reformulation of the secrecy capacity problem which
allows us to derive the so-called partial best response algorithm to obtain an
optimal solution. Notably, each iteration of the second method can also be done
in closed form. The simulation results demonstrate a faster convergence rate of
our methods compared to other known solutions. We carry out extensive numerical
experiments to evaluate the impact of various parameters on the achieved
secrecy capacity
Algorithms for Globally-Optimal Secure Signaling over Gaussian MIMO Wiretap Channels Under Interference Constraints
Multi-user Gaussian MIMO wiretap channel is considered under interference
power constraints (IPC), in addition to the total transmit power constraint
(TPC). Algorithms for \textit{global} maximization of its secrecy rate are
proposed. Their convergence to the secrecy capacity is rigorously proved and a
number of properties are established analytically. Unlike known algorithms, the
proposed ones are not limited to the MISO case and are proved to converge to a
\textit{global} rather than local optimum in the general MIMO case, even when
the channel is not degraded. In practice, the convergence is fast as only a
small to moderate number of Newton steps is required to achieve a high
precision level. The interplay of TPC and IPC is shown to result in an unusual
property when an optimal point of the max-min problem does not provide an
optimal transmit covariance matrix in some (singular) cases. To address this
issue, an algorithm is developed to compute an optimal transmit covariance
matrix in those singular cases. It is shown that this algorithm also solves the
dual (nonconvex) problems of \textit{globally} minimizing the total transmit
power subject to the secrecy and interference constraints; it provides the
minimum transmit power and respective signaling strategy needed to achieve the
secrecy capacity, hence allowing power savings.Comment: accepted for publicatio
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