Distributed Power Control Techniques Based on Game Theory for Wideband Wireless Networks

This thesis describes a theoretical framework for the design and the analysis of distributed (decentralized) power control algorithms for high-throughput wireless networks using ultrawideband (UWB) technologies. The tools of game theory are shown to be expedient for deriving scalable, energy-efficient, distributed power control schemes to be applied to a population of battery-operated user terminals in a rich multipath environment. In particular, the power control issue is modeled as a noncooperative game in which each user chooses its transmit power so as to maximize its own utility, which is defined as the ratio of throughput to transmit power. Although distributed (noncooperative) control is known to be suboptimal with respect to the optimal centralized (cooperative) solution, it is shown via large-system analysis that the game-theoretic distributed algorithm based on Nash equilibrium exhibits negligible performance degradation with respect to the centralized socially optimal configuration. The framework described here is general enough to also encompass the analysis of code division multiple access (CDMA) systems and to show that UWB slightly outperforms CDMA in terms of achieved utility at the Nash equilibrium

Distributed stochastic optimization via matrix exponential learning

In this paper, we investigate a distributed learning scheme for a broad class of stochastic optimization problems and games that arise in signal processing and wireless communications. The proposed algorithm relies on the method of matrix exponential learning (MXL) and only requires locally computable gradient observations that are possibly imperfect and/or obsolete. To analyze it, we introduce the notion of a stable Nash equilibrium and we show that the algorithm is globally convergent to such equilibria - or locally convergent when an equilibrium is only locally stable. We also derive an explicit linear bound for the algorithm's convergence speed, which remains valid under measurement errors and uncertainty of arbitrarily high variance. To validate our theoretical analysis, we test the algorithm in realistic multi-carrier/multiple-antenna wireless scenarios where several users seek to maximize their energy efficiency. Our results show that learning allows users to attain a net increase between 100% and 500% in energy efficiency, even under very high uncertainty.Comment: 31 pages, 3 figure

Ant Colony Optimization for Social Utility Maximization in a Multiuser Communication System

In a multiuser communication system such as cognitive radio or digital subscriber lines, the transmission rate of each user is affected by the channel background noise and the crosstalk interference from other users. This paper presents an efficient ant colony optimization algorithm to allocate each user’s limited power on different channels for maximizing social utility (i.e., the sum of all individual utilities). The proposed algorithm adopts an initial solution that allocates more power on the channel with a lower background noise level. Besides, the cooling concept of simulated annealing is integrated into the proposed method to improve the convergence rate during the local search of the ant colony optimization algorithm. A number of experiments are conducted to validate the effectiveness of the proposed algorithm

Collaborative Information Processing in Wireless Sensor Networks for Diffusive Source Estimation

In this dissertation, we address the issue of collaborative information processing for diffusive source parameter estimation using wireless sensor networks (WSNs) capable of sensing in dispersive medium/environment, from signal processing perspective. We begin the dissertation by focusing on the mathematical formulation of a special diffusion phenomenon, i.e., an underwater oil spill, along with statistical algorithms for meaningful analysis of sensor data leading to efficient estimation of desired parameters of interest. The objective is to obtain an analytical solution to the problem, rather than using non-model based sophisticated numerical techniques. We tried to make the physical diffusion model as much appropriate as possible, while maintaining some pragmatic and reasonable assumptions for the simplicity of exposition and analytical derivation. The dissertation studies both source localization and tracking for static and moving diffusive sources respectively. For static diffusive source localization, we investigate two parametric estimation techniques based on the maximum-likelihood (ML) and the best linear unbiased estimator (BLUE) for a special case of our obtained physical dispersion model. We prove the consistency and asymptotic normality of the obtained ML solution when the number of sensor nodes and samples approach infinity, and derive the Cramer-Rao lower bound (CRLB) on its performance. In case of a moving diffusive source, we propose a particle filter (PF) based target tracking scheme for moving diffusive source, and analytically derive the posterior Cramer-Rao lower bound (PCRLB) for the moving source state estimates as a theoretical performance bound. Further, we explore nonparametric, machine learning based estimation technique for diffusive source parameter estimation using Dirichlet process mixture model (DPMM). Since real data are often complicated, no parametric model is suitable. As an alternative, we exploit the rich tools of nonparametric Bayesian methods, in particular the DPMM, which provides us with a flexible and data-driven estimation process. We propose DPMM based static diffusive source localization algorithm and provide analytical proof of convergence. The proposed algorithm is also extended to the scenario when multiple diffusive sources of same kind are present in the diffusive field of interest. Efficient power allocation can play an important role in extending the lifetime of a resource constrained WSN. Resource-constrained WSNs rely on collaborative signal and information processing for efficient handling of large volumes of data collected by the sensor nodes. In this dissertation, the problem of collaborative information processing for sequential parameter estimation in a WSN is formulated in a cooperative game-theoretic framework, which addresses the issue of fair resource allocation for estimation task at the Fusion center (FC). The framework allows addressing either resource allocation or commitment for information processing as solutions of cooperative games with underlying theoretical justifications. Different solution concepts found in cooperative games, namely, the Shapley function and Nash bargaining are used to enforce certain kinds of fairness among the nodes in a WSN

Cognitive Radio Made Practical: Forward-Lookingness and Calculated Competition

Cognitive radio is more than just radio environment awareness, but more importantly the ability to interact with the environment in the best way possible. Ideally, cognitive radios will form a selfregulating society of mobile radios achieving maximum spectrum utilization. However, challenges arise as mobile radios tend to compete with one another for spectrum, generating harmful interference and damaging performance individually and for the network as a whole. In this paper, we present a framework that allows competing radios to teach and learn from each other’s action so that a desirable equilibrium can be reached. The heart of cognition to establish this is the forward-looking ability, which enables competing radios to see beyond the present time, negotiate and optimize their actions towards a more agreeable equilibrium. Technically speaking, we adopt a belief-directed game where each mobile radio, regarded as player, formulates a belief function to project how the radio environment as a whole would respond to any of its action. This model facilitates engineering of the equilibrium by different choices of the players’ belief functions. Under this model, players will negotiate naturally through a sequence of calculated competition (i.e., cycles of teaching and learning with each other). We apply this methodology to a cognitive orthogonal frequency-division multiple-access (OFDMA) radio network where mobile users are free to access any of the subcarriers and thus compete for radio resources to maximize their rates. Results reveal that the proposed negotiation-by-forward-looking competition mechanism guides users to converge to an equilibrium that benefits not only individual users but the entire network approaching the maximum achievable sum-rate