312 research outputs found
Transmit design for MIMO wiretap channel with a malicious jammer
In this paper, we consider the transmit design for multi-input multi-output
(MIMO) wiretap channel including a malicious jammer. We first transform the
system model into the traditional three-node wiretap channel by whitening the
interference at the legitimate user. Additionally, the eavesdropper channel
state information (ECSI) may be fully or statistically known, even unknown to
the transmitter. Hence, some strategies are proposed in terms of different
levels of ECSI available to the transmitter in our paper. For the case of
unknown ECSI, a target rate for the legitimate user is first specified. And
then an inverse water-filling algorithm is put forward to find the optimal
power allocation for each information symbol, with a stepwise search being used
to adjust the spatial dimension allocated to artificial noise (AN) such that
the target rate is achievable. As for the case of statistical ECSI, several
simulated channels are randomly generated according to the distribution of
ECSI. We show that the ergodic secrecy capacity can be approximated as the
average secrecy capacity of these simulated channels. Through maximizing this
average secrecy capacity, we can obtain a feasible power and spatial dimension
allocation scheme by using one dimension search. Finally, numerical results
reveal the effectiveness and computational efficiency of our algorithms.Comment: 2015 IEEE 81st Vehicular Technology Conference (VTC Spring
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
Joint Base Station and IRS Deployment for Enhancing Network Coverage: A Graph-Based Modeling and Optimization Approach
Intelligent reflecting surface (IRS) can be densely deployed in complex
environment to create cascaded line-of-sight (LoS) paths between multiple base
stations (BSs) and users via tunable IRS reflections, thereby significantly
enhancing the coverage performance of wireless networks. To achieve this goal,
it is vital to optimize the deployed locations of BSs and IRSs in the wireless
network, which is investigated in this paper. Specifically, we divide the
coverage area of the network into multiple non-overlapping cells and decide
whether to deploy a BS/IRS in each cell given a total number of BSs/IRSs
available. We show that to ensure the network coverage/communication
performance, i.e., each cell has a direct/cascaded LoS path with at least one
BS, as well as such LoS paths have the average number of IRS reflections less
than a given threshold, there is a fundamental trade-off with the deployment
cost or the number of BSs/IRSs needed. To optimally characterize this
trade-off, we formulate a joint BS and IRS deployment problem based on graph
theory, which, however, is difficult to be optimally solved due to the
combinatorial optimization involved. To circumvent this difficulty, we first
consider a simplified problem with given BS deployment and propose the optimal
as well as an efficient suboptimal IRS deployment solution to it, by applying
the branch-and-bound method and iteratively removing IRSs from the candidate
locations, respectively. Next, an efficient sequential update algorithm is
proposed for solving the joint BS and IRS deployment problem. Numerical results
are provided to show the efficacy of the proposed design approach and
optimization algorithms for the joint BS and IRS deployment. The trade-off
between the network coverage performance and the number of deployed BSs/IRSs
with different cost ratios is also unveiled.Comment: 30 pages, 10 figure
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