12,581 research outputs found
Physical Layer Service Integration in 5G: Potentials and Challenges
High transmission rate and secure communication have been identified as the
key targets that need to be effectively addressed by fifth generation (5G)
wireless systems. In this context, the concept of physical-layer security
becomes attractive, as it can establish perfect security using only the
characteristics of wireless medium. Nonetheless, to further increase the
spectral efficiency, an emerging concept, termed physical-layer service
integration (PHY-SI), has been recognized as an effective means. Its basic idea
is to combine multiple coexisting services, i.e., multicast/broadcast service
and confidential service, into one integral service for one-time transmission
at the transmitter side. This article first provides a tutorial on typical
PHY-SI models. Furthermore, we propose some state-of-the-art solutions to
improve the overall performance of PHY-SI in certain important communication
scenarios. In particular, we highlight the extension of several concepts
borrowed from conventional single-service communications, such as artificial
noise (AN), eigenmode transmission etc., to the scenario of PHY-SI. These
techniques are shown to be effective in the design of reliable and robust
PHY-SI schemes. Finally, several potential research directions are identified
for future work.Comment: 12 pages, 7 figure
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 Artificial-Noise Aided Transmit Design for Multi-User MISO Systems with Integrated Services
This paper considers artificial noise (AN)-aided transmit designs for
multi-user MISO systems in the eyes of service integration. Specifically, we
combine two sorts of services, and serve them simultaneously: one multicast
message intended for all receivers and one confidential message intended for
only one receiver. The confidential message is kept perfectly secure from all
the unauthorized receivers. Our goal is to jointly design the optimal input
covariances for the multicast message, confidential message and AN, such that
the achievable secrecy rate region is maximized subject to the sum power
constraint. This secrecy rate region maximization (SRRM) problem is a nonconvex
vector maximization problem. To handle it, we reformulate the SRRM problem into
a provably equivalent scalar optimization problem and propose a searching
method to find all of its Pareto optimal points. The equivalent scalar
optimization problem is identified as a secrecy rate maximization (SRM) problem
with the quality of multicast service (QoMS) constraints. Further, we show that
this equivalent QoMS-constrained SRM problem, albeit nonconvex, can be
efficiently handled based on a two-stage optimization approach, including
solving a sequence of semidefinite programs. Moreover, we also extend the SRRM
problem to an imperfect channel state information (CSI) case where a worst-case
robust formulation is considered. In particular, while transmit beamforming is
generally a suboptimal technique to the SRRM problem, we prove that it is
optimal for the confidential message transmission whether in the perfect CSI
scenario or in the imperfect CSI scenario. Finally, numerical results
demonstrate that the AN-aided transmit designs are effective in expanding the
achievable secrecy rate regions.Comment: Part of this work has been presented in IEEE GlobalSIP 2015 and in
IEEE ICASSP 201
Magic wavelengths for the transition in ytterbium atom
The static and dynamic electric-dipole polarizabilities of the
and states of Yb are calculated by using the relativistic ab
initio method. Focusing on the red detuning region to the
transition, we find two magic wavelengths at
1035.7(2) nm and 612.9(2) nm for the
transition and three magic wavelengthes at 1517.68(6) nm, 1036.0(3) nm and
858(12) nm for the transitions. Such
magic wavelengths are of particular interest for attaining the
state-insensitive cooling, trapping, and quantum manipulation of neutral Yb
atom.Comment: 13 pages, 3 figure
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