A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning

In this tutorial paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalent rate constraint approach, the Lyapunov stability drift approach and the approximate Markov Decision Process (MDP) approach using stochastic learning. These approaches essentially embrace most of the existing literature regarding delay-aware resource control in wireless systems. They have their relative pros and cons in terms of performance, complexity and implementation issues. For each of the approaches, the problem setup, the general solution and the design methodology are discussed. Applications of these approaches to delay-aware resource allocation are illustrated with examples in single-hop wireless networks. Furthermore, recent results regarding delay-aware multi-hop routing designs in general multi-hop networks are elaborated. Finally, the delay performance of the various approaches are compared through simulations using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201

Environmental analysis for application layer networks

Die zunehmende Vernetzung von Rechnern über das Internet lies die Vision von Application Layer Netzwerken aufkommen. Sie umfassen Overlay Netzwerke wie beispielsweise Peer-to-Peer Netzwerke und Grid Infrastrukturen unter Verwendung des TCP/IP Protokolls. Ihre gemeinsame Eigenschaft ist die redundante, verteilte Bereitstellung und der Zugang zu Daten-, Rechen- und Anwendungsdiensten, während sie die Heterogenität der Infrastruktur vor dem Nutzer verbergen. In dieser Arbeit werden die Anforderungen, die diese Netzwerke an ökonomische Allokationsmechanismen stellen, untersucht. Die Analyse erfolgt anhand eines Marktanalyseprozesses für einen zentralen Auktionsmechanismus und einen katallaktischen Markt. --Grid Computing

Back to the Future: Economic Self-Organisation and Maximum Entropy Prediction

This paper shows that signal restoration methodology is appropriate for predicting the equilibrium state of certain economic systems. A formal justification for this is provided by proving the existence of finite improvement paths in object allocation problems under weak assumptions on preferences, linking any initial condition to a Nash equilibrium. Because a finite improvement path is made up of a sequence of systematic best-responses, backwards movement from the equilibrium back to the initial condition can be treated like the realisation of a noise process. This underpins the use of signal restoration to predict the equilibrium from the initial condition, and an illustration is provided through an application of maximum entropy signal restoration to the Schelling model of segregation

Rational Information Choice in Financial Market Equilibrium

Adding a stage of signal acquisition to the expected utility model shows that Bayesian updating results in a well defined law of demand for financial information when asset return distributions are conjugate priors to signals such as in the gamma-Poisson case. Signals have a positive marginal utility value that falls in their number if and only if investors are risk averse, asset markets large, and variance-mean ratios of asset returns high in fully revealing rational expectations equilibrium. Expected asset price increases in the number of signals so that expected excess return drops. The diminishing excess return prevents Bayesian investors from unbounded information demand even if signals are costless, unless the riskfree asset is removed. Signals mutually benefit homogeneous investors because revealing asset price permits updating so that a Pareto criterion judges competitive equilibrium as not sufficiently informative. However, asset price responses make incentives for signal acquisition dependent on portfolios so that welfare and distributional consequences become intricately linked when investors are heterogeneous.

Fast Optimization with Zeroth-Order Feedback in Distributed, Multi-User MIMO Systems

In this paper, we develop a gradient-free optimization methodology for efficient resource allocation in Gaussian MIMO multiple access channels. Our approach combines two main ingredients: (i) an entropic semidefinite optimization based on matrix exponential learning (MXL); and (ii) a one-shot gradient estimator which achieves low variance through the reuse of past information. This novel algorithm, which we call gradient-free MXL algorithm with callbacks (MXL0$^{+}$), retains the convergence speed of gradient-based methods while requiring minimal feedback per iteration$-$a single scalar. In more detail, in a MIMO multiple access channel with $K$ users and $M$ transmit antennas per user, the MXL0$^{+}$ algorithm achieves $\epsilon$-optimality within $\text{poly}(K,M)/\epsilon^2$ iterations (on average and with high probability), even when implemented in a fully distributed, asynchronous manner. For cross-validation, we also perform a series of numerical experiments in medium- to large-scale MIMO networks under realistic channel conditions. Throughout our experiments, the performance of MXL0$^{+}$ matches$-$and sometimes exceeds$-$that of gradient-based MXL methods, all the while operating with a vastly reduced communication overhead. In view of these findings, the MXL0$^{+}$ algorithm appears to be uniquely suited for distributed massive MIMO systems where gradient calculations can become prohibitively expensive.Comment: Final version; to appear in IEEE Transactions on Signal Processing; 16 pages, 4 figure

The Political Economy of Large-Scale Land Acquisition in Sierra Leone: An Empirical Application of a Computable General Political Economy Equilibrium Modelling Approach

The transfer of large areas of agricultural land from small-holder farmers to international investors, particularly in Sub-Saharan Africa, has become one of the mostly contested topics in International development circles. Even though there is a broad agreement among both critics and opponents that these land transfers have profound welfare implications, there is a dearth in the academic literature about their distributional and welfare effects at both the household and country level. Moreover, beyond pure economic anaylsis, political economy analysis have rarely been provided. This thesis, which is comprised of four articles, attempts to fill these gaps by adopting a Computational General Political Economy Equilibrium (CGPE) Modelling approach to undertake an empirical political economy analysis of the role of political preferences, policy beliefs and political power in the large-scale land acquisition policy processes in Sierra Leone. It also undertakes a quantitative assessment of the distributional and welfare effects of large-scale land acquisition at both the household and country level in Sierra Leone. Our results suggest that even though some stakeholders put extremely high political weight on maximizing the profit of large-scale land investors, land market policies are not driven by land grabber preferences. Furthermore, land market policies would be only significantly inefficient if small-scale farmers hold rational expectation beliefs. These findings have significant implications for development institutions, and donor organisations seeking to promote stakeholder engagements and bolster evidence-based policy making in developing countries like Sierra Leone. We suggest that, to overcome the wide variance in the estimated policy beliefs among key policy stakeholders, a transdisciplinary research apprroach that allows for the scientifc and political community to interact and narrow policy belief differences, is required