  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.
