Minyi Huang

Large-Population Cost-Coupled LQG Problems With Nonuniform Agents: Individual-Mass Behavior and Decentralized

We consider linear quadratic Gaussian (LQG) games in large population systems where the agents evolve according to nonuniform dynamics and are coupled via their individual costs. A state aggregation technique is developed to obtain a set of decentralized control laws for the individuals which possesses an epsiv-Nash equilibrium property. A stability property of the mass behavior is established, and the effect of inaccurate population statistics on an isolated agent is also analyzed by variational techniques.

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We consider uplink power control for lognormal fading channels in the large population case. First, we examine the structure of the control law in a centralized stochastic optimal control setup. We analyze the effect of large populations on the individual control inputs. Next, we split the centralized cost to approach the problem in a game theoretic framework. In this context, we introduce an auxiliary LQG control system and analyze the resulting /spl epsiv/-Nash equilibrium for the control law; subsequently we generalize the methodology developed for the LQG problem to the wireless power control problem to get an approximation for the collective effect of all other users on a given user. The obtained state aggregation technique leads to highly localized control configurations in contrast to the full state based optimal control strategy.

-Nash Equilibria

In cognitive radio (CR) networks, secondary users can cooperatively sense the spectrum to detect the presence of primary users. In this paper, we propose a fully distributed and scalable cooperative spectrum-sensing scheme based on recent advances in consensus algorithms. In the proposed scheme, the secondary users can maintain coordination based on only local information exchange without a centralized common receiver. Unlike most of the existing decision rules, such as the or-rule or the 1-out-of-N rule, we use the consensus of secondary users to make the final decision. Simulation results show that the proposed consensus scheme can have significant lower missing detection probabilities and false alarm probabilities in CR networks. It is also demonstrated that the proposed scheme not only has proven sensitivity in detecting the primary users presence but also has robustness in choosing a desirable decision threshold.

Individual and mass behaviour in large population stochastic wireless power control problems: centralized and Nash equilibrium solutions

We consider linear-quadratic-Gaussian (LQG) games with a major player and a large number of minor players. The major player has a significant influence on others. The minor players individually have negligible impact, but they collectively contribute mean field coupling terms in the individual dynamics and costs. To overcome the dimensionality difficulty and obtain decentralized strategies, the so-called Nash certainty equivalence methodology is applied. The control synthesis is preceded by a state space augmentation via a set of aggregate quantities giving the mean field approximation. Subsequently, within the population limit the LQG game is decomposed into a family of limiting two-player games as each is locally seen by a representative minor player. Next, when solving these limiting two-player games, we impose certain interaction consistency conditions such that the aggregate quantities initially assumed coincide with the ones replicated by the closed loop of a large number of minor players. This procedure leads to a set of decentralized strategies for the original LQG game, which is an

A Distributed Consensus-Based Cooperative Spectrum-Sensing Scheme in Cognitive Radios

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Large-Population LQG Games Involving a Major Player: The Nash Certainty Equivalence Principle

-Nash equilibrium.

Biologically inspired consensus-based spectrum sensing in mobile Ad Hoc networks with cognitive radios

Cognitive radios, which are capable of sensing their surrounding environment and adapting their internal parameters, have been considered in mobile ad hoc networks. Secondary users can cooperatively sense the spectrum to detect the presence of primary users. In this article we present a novel biologically inspired consensus-based cooperative spectrum sensing scheme in CR-MANETs. Our scheme is based on recent advances in consensus algorithms that have taken inspiration from self-organizing behavior of animal groups such as birds, fish, ants, honeybees, and others. Unlike the existing cooperative spectrum sensing schemes, such as the OR-rule or the 1-out-of-N rule, there is no need for a common receiver to do the data fusion for reaching the final decision. A secondary user needs only to set up local interactions without a centralized node in CR-MANETs. Simulation results are presented to show the effectiveness of the proposed scheme.

Stochastic consensus over noisy networks with Markovian and arbitrary switches

This paper considers stochastic consensus problems over lossy wireless networks. We first propose a measurement model with a random link gain, additive noise, and Markovian lossy signal reception, which captures uncertain operational conditions of practical networks. For consensus seeking, we apply stochastic approximation and derive a Markovian mode dependent recursive algorithm. Mean square and almost sure (i.e., probability one) convergence analysis is developed via a state space decomposition approach when the coefficient matrix in the algorithm satisfies a zero row and column sum condition. Subsequently, we consider a model with arbitrary random switching and a common stochastic Lyapunov function technique is used to prove convergence. Finally, our method is applied to models with heterogeneous quantizers and packet losses, and convergence results are proved.

Defense against spectrum sensing data falsification attacks in mobile ad hoc networks with cognitive radios

Cognitive radios (CRs) have been considered for use in mobile ad hoc networks (MANETs). The area of security in Cognitive Radio MANETs (CR-MANETs) has yet to receive much attention. However, some distinct characteristics of CRs introduce new, non-trivial security risks to CR-MANETs. In this paper, we study spectrum sensing data falsification (SSDF) attacks to CR-MANETs, in which intruders send false local spectrum sensing results in cooperative spectrum sensing, and SSDF may result in incorrect spectrum sensing decisions by CRs. We present a consensus-based cooperative spectrum sensing scheme to counter SSDF attacks in CR-MANETs. Our scheme is based on recent advances in consensus algorithms that have taken inspiration from self-organizing behavior of animal groups such as fish. Unlike the existing schemes, there is no need for a common receiver to do the data fusion for reaching the final decision to counter SSDF attacks. Simulation results are presented to show the effectiveness of the proposed scheme.

Social Optima in Mean Field LQG Control: Centralized and Decentralized Strategies

We study a class of linear-quadratic-Gaussian (LQG) control problems with N decision makers, where the basic objective is to minimize a social cost as the sum of N individual costs containing mean field coupling. The exact socially optimal solution (determining a particular Pareto optimum) requires centralized information for each agent and has high implementational complexity. As an alternative we subsequently exploit a mean field structure in the centralized optimal control problem to develop decentralized cooperative optimization so that each agent only uses its own state and a function which may be computed offline; the resulting set of strategies asymptotically achieves the social optimum as N → ∞. A key feature in this scheme is to let each agent optimize a new cost as the sum of its own cost and another component capturing its social impact on all other agents. We also discuss the relationship between the decentralized cooperative solution and the so-called Nash Certainty Equivalence based solution presented in previous work on mean field LQG games.