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Principles of Physical Layer Security in Multiuser Wireless Networks: A Survey
"... This paper provides a comprehensive review of the domain of physical layer security in multiuser wireless networks. The essential premise of physical layer security is to enable the exchange of confidential messages over a wireless medium in the presence of unauthorized eavesdroppers, without rely ..."
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This paper provides a comprehensive review of the domain of physical layer security in multiuser wireless networks. The essential premise of physical layer security is to enable the exchange of confidential messages over a wireless medium in the presence of unauthorized eavesdroppers, without relying on higherlayer encryption. This can be achieved primarily in two ways: without the need for a secret key by intelligently designing transmit coding strategies, or by exploiting the wireless communication medium to develop secret keys over public channels. The survey begins with an overview of the foundations dating back to the pioneering work of Shannon and Wyner on informationtheoretic security. We then describe the evolution of secure transmission strategies from pointtopoint channels to multipleantenna systems, followed by generalizations to multiuser broadcast, multipleaccess, interference, and relay networks. Secretkey generation and establishment protocols based on physical layer mechanisms are subsequently covered. Approaches for secrecy based on channel coding design are then examined, along with a description of interdisciplinary approaches based on game theory and stochastic geometry. The associated problem of physical layer message authentication is also briefly introduced. The survey concludes with observations on potential research directions in this area.
MOBICLIQUES FOR IMPROVING ERGODIC SECRECY IN FADINGWIRETAP CHANNELS UNDER POWER CONSTRAINTS
"... We consider a cooperative secret communication scenario, in which a group of mobile and strictly power constrained nodes, acting as relays, cooperatively transmit to a destination in the presence of an eavesdropper; both destination and eavesdropper are assumed stationary. The cooperative scheme en ..."
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We consider a cooperative secret communication scenario, in which a group of mobile and strictly power constrained nodes, acting as relays, cooperatively transmit to a destination in the presence of an eavesdropper; both destination and eavesdropper are assumed stationary. The cooperative scheme entails motion control and optimal communication, in order to achieve a prescribed level of ergodic secrecy rate. The group of motioncontrolled cooperating relays is here termed as mobiclique. Under this setting, a novel, decentralized motion control scheme is derived, which effectively drives the relays to a formation configuration, ensuring the achievability of the prescribed expected secrecy requirement, while at the same time maximizing the utilization of network resources. The effectiveness of the proposed approach is verified both theoretically and through numerical simulations.
Asymptotically Optimal Discrete Time Nonlinear Filters From Stochastically Convergent State Process Approximations
"... We consider the problem of approximating optimal in the MMSE sense nonlinear filters in a discrete time setting, exploiting properties of stochastically convergent state process approximations. More specifically, we consider a class of nonlinear, partially observable stochastic systems, comprised b ..."
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We consider the problem of approximating optimal in the MMSE sense nonlinear filters in a discrete time setting, exploiting properties of stochastically convergent state process approximations. More specifically, we consider a class of nonlinear, partially observable stochastic systems, comprised by a (possibly nonstationary) hidden stochastic process (the state), observed through another conditionally Gaussian stochastic process (the observations). Under general assumptions, we show that, given an approximating process which, for each time step, is stochastically convergent to the state process in some appropriate sense, an approximate filtering operator can be defined, which converges to the true optimal nonlinear filter of the state in a strong and well defined sense, i.e., compactly in time and uniformly in a completely characterized measurable set of probability measure almost unity, also providing a purely quantitative justification of Egoroffâ€™s Theorem for the problem at hand. The results presented in this paper can form a common basis for the analysis and characterization of a number of heuristic approaches for approximating a large class of optimal nonlinear filters, such as approximate grid based techniques, known to perform well in a variety of applications.
Sequential channel state tracking & spatiotemporal channel prediction in mobile wireless sensor networks
 IEEE Transactions on Signal and Information Processing over Networks, submitted in 2015. Available at: http://arxiv.org/pdf/1502.01780v1.pdf
"... We propose a nonlinear filtering framework for approaching the problems of channel state tracking and spatiotemporal channel gain prediction in mobile wireless sensor networks, in a Bayesian setting. We assume that the wireless channel constitutes an observable (by the sensors/network nodes), spatio ..."
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We propose a nonlinear filtering framework for approaching the problems of channel state tracking and spatiotemporal channel gain prediction in mobile wireless sensor networks, in a Bayesian setting. We assume that the wireless channel constitutes an observable (by the sensors/network nodes), spatiotemporal, conditionally Gaussian stochastic process, which is statistically dependent on a set of hidden channel parameters, called the channel state. The channel state evolves in time according to a known, non stationary, nonlinear and/or non Gaussian Markov stochastic kernel. This formulation results in a partially observable system, with a temporally varying global state and spatiotemporally varying observations. Recognizing the intractability of general nonlinear state estimation, we advocate the use of grid based approximate filters as an effective and robust means for recursive tracking of the channel state. We also propose a sequential spatiotemporal predictor for tracking the channel gains at any point in time and space, providing real time sequential estimates for the respective channel gain map, for each sensor in the network. Additionally, we show that both estimators converge towards the true respective MMSE optimal estimators, in a common, relatively strong sense. Numerical simulations corroborate the practical effectiveness of the proposed approach.