286 research outputs found
Transmit optimization techniques for physical layer security
PhD ThesisOver the last several decades, reliable communication has received considerable
attention in the area of dynamic network con gurations and
distributed processing techniques. Traditional secure communications
mainly considered transmission cryptography, which has been developed
in the network layer. However, the nature of wireless transmission introduces
various challenges of key distribution and management in establishing
secure communication links. Physical layer security has been
recently recognized as a promising new design paradigm to provide security
in wireless networks in addition to existing conventional cryptographic
methods, where the physical layer dynamics of fading channels
are exploited to establish secure wireless links. On the other hand, with
the ever-increasing demand of wireless access users, multi-antenna transmission
has been considered as one of e ective approaches to improve
the capacity of wireless networks. Multi-antenna transmission applied
in physical layer security has extracted more and more attentions by
exploiting additional degrees of freedom and diversity gains.
In this thesis, di erent multi-antenna transmit optimization techniques
are developed for physical layer secure transmission. The secrecy rate
optimization problems (i.e., power minimization and secrecy rate maximization)
are formulated to guarantee the optimal power allocation.
First, transmit optimization for multiple-input single-output (MISO) secrecy
channels are developed to design secure transmit beamformer that
minimize the transmit power to achieve a target secrecy rate. Besides,
the associated robust scheme with the secrecy rate outage probability
constraint are presented with statistical channel uncertainty, where the
outage probability constraint requires that the achieved secrecy rate
exceeds certain thresholds with a speci c probability. Second, multiantenna
cooperative jammer (CJ) is presented to provide jamming services
that introduces extra interference to assist a multiple-input multipleoutput
(MIMO) secure transmission. Transmit optimization for this CJaided
MIMO secrecy channel is designed to achieve an optimal power
allocation. Moreover, secure transmission is achieved when the CJ introduces
charges for its jamming service based on the amount of the
interference caused to the eavesdropper, where the Stackelberg game
is proposed to handle, and the Stackelberg equilibrium is analytically
derived. Finally, transmit optimization for MISO secure simultaneous
wireless information and power transfer (SWIPT) is investigated, where
secure transmit beamformer is designed with/without the help of arti -
cial noise (AN) to maximize the achieved secrecy rate such that satisfy
the transmit power budget and the energy harvesting (EH) constraint.
The performance of all proposed schemes are validated by MATLAB
simulation results
A dynamic pricipal-agent problem as a feedback Stackelberg differentioal game
We consider situations in which a principal tries to induce an agent to spend e®ort on accumulating a state variable that a®ects the well-being of both parties. The only incentive mechanism that the principal can use is a state-dependent transfer of her own utility to the agent. Formally, the model is a Stackelberg di®erential game in which the players use feedback strategies. Whereas in general Stackelberg di®erential games with feedback strategy spaces the leader's optimization problem has non-standard features that make it extremely hard to solve, in the present case this problem can be rewritten as a standard optimal control problem. Two examples are used to illustrate our approach.
A MPCC-NLP approach for an electric power market problem
The electric power market is changing - it has passed from a regulated market, where
the government of each country had the control of prices, to a deregulated market economy. Each company competes in order to get more clients and maximize its profits. This market is represented by a Stackelberg game with two firms, leader and follower, and the leader anticipates the reaction of the follower.
The problem is formulated as a Mathematical Program with Complementarity Constraints (MPCC). It is shown that the constraint qualifications usually assumed to prove convergence of standard algorithms fail to hold for MPCC. To circumvent this, a reformulation for a nonlinear problem (NLP) is proposed. Numerical tests using the NEOS server platform are presented
Canonical duality theory and algorithm for solving bilevel knapsack problems with applications
A novel canonical duality theory (CDT) is presented for solving general bilevel mixed integer nonlinear optimization governed by linear and quadratic knapsack problems. It shows that the challenging knapsack problems can be solved analytically in term of their canonical dual solutions. The existence and uniqueness of these analytical solutions are proved. NP-hardness of the knapsack problems is discussed. A powerful CDT algorithm combined with an alternative iteration and a volume reduction method is proposed for solving the NP-hard bilevel knapsack problems. Application is illustrated by benchmark problems in optimal topology design. The performance and novelty of the proposed method are compared with the popular commercial codes. © 2013 IEEE
Game theoretic optimisation in process and energy systems engineering: A review
Game theory is a framework that has been used by various research fields in order to represent dynamic correlation among stakeholders. Traditionally, research within the process and energy systems engineering community has focused on the development of centralised decision making schemes. In the recent years, decentralised decision-making schemes have attracted increasing attention due to their ability to capture multi-stakeholder dynamics in a more accurate manner. In this article, we survey how centralised and decentralised decision making has been facilitated by game theoretic approaches. We focus on the deployment of such methods in process systems engineering problems and review applications related to supply chain optimisation problems, design and operations, and energy systems optimisation. Finally, we analyse different game structures based on the degree of cooperation and how fairness criteria can be employed to find fair payoff allocations
A game-theoretic optimisation approach to fair customer allocation in oligopolies
Under the ever-increasing capital intensive environment that contemporary process industries face, oligopolies begin to form in mature markets where a small number of companies regulate and serve the customer base. Strategic and operational decisions are highly dependent on the firms’ customer portfolio and conventional modelling approaches neglect the rational behaviour of the decision makers, with regards to the problem of customer allocation, by assuming either static competition or a leader-follower structure. In this article, we address the fair customer allocation within oligopolies by employing the Nash bargaining approach. The overall problem is formulated as mixed integer program with linear constraints and a nonlinear objective function which is further linearised following a separable programming approach. Case studies from the industrial liquid market highlight the importance and benefits of the proposed game theoretic approach
A reducibility method for the weak linear bilevel programming problems and a case study in principal-agent
© 2018 A weak linear bilevel programming (WLBP) problem often models problems involving hierarchy structure in expert and intelligent systems under the pessimistic point. In the paper, we deal with such a problem. Using the duality theory of linear programming, the WLBP problem is first equivalently transformed into a jointly constrained bilinear programming problem. Then, we show that the resolution of the jointly constrained bilinear programming problem is equivalent to the resolution of a disjoint bilinear programming problem under appropriate assumptions. This may give a possibility to solve the WLBP problem via a single-level disjoint bilinear programming problem. Furthermore, some examples illustrate the solution process and feasibility of the proposed method. Finally, the WLBP problem models a principal-agent problem under the pessimistic point that is also compared with a principal-agent problem under the optimistic point
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