Spectrum Sharing in Dynamic Spectrum Access Networks: WPE-II Written Report

A study by Federal Communication Commission shows that most of the spectrum in current wireless networks is unused most of the time, while some spectrum is heavily used. Recently dynamic spectrum access (DSA) has been proposed to solve this spectrum inefficiency problem, by allowing users to opportunistically access to unused spectrum. One important question in DSA is how to efficiently share spectrum among users so that spectrum utilization can be increased and wireless interference can be reduced. Spectrum sharing can be formalized as a graph coloring problem. In this report we focus on surveying spectrum sharing techniques in DSA networks and present four representative techniques in different taxonomy domains, including centralized, distributed with/without common control channel, and a real case study of DSA networks --- DARPA neXt Gen- eration (XG) radios. Their strengths and limitations are evaluated and compared in detail. Finally, we discuss the challenges in current spectrum sharing research and possible future directions

Event-Triggered Algorithms for Leader-Follower Consensus of Networked Euler-Lagrange Agents

This paper proposes three different distributed event-triggered control algorithms to achieve leader-follower consensus for a network of Euler-Lagrange agents. We firstly propose two model-independent algorithms for a subclass of Euler-Lagrange agents without the vector of gravitational potential forces. By model-independent, we mean that each agent can execute its algorithm with no knowledge of the agent self-dynamics. A variable-gain algorithm is employed when the sensing graph is undirected; algorithm parameters are selected in a fully distributed manner with much greater flexibility compared to all previous work concerning event-triggered consensus problems. When the sensing graph is directed, a constant-gain algorithm is employed. The control gains must be centrally designed to exceed several lower bounding inequalities which require limited knowledge of bounds on the matrices describing the agent dynamics, bounds on network topology information and bounds on the initial conditions. When the Euler-Lagrange agents have dynamics which include the vector of gravitational potential forces, an adaptive algorithm is proposed which requires more information about the agent dynamics but can estimate uncertain agent parameters. For each algorithm, a trigger function is proposed to govern the event update times. At each event, the controller is updated, which ensures that the control input is piecewise constant and saves energy resources. We analyse each controllers and trigger function and exclude Zeno behaviour. Extensive simulations show 1) the advantages of our proposed trigger function as compared to those in existing literature, and 2) the effectiveness of our proposed controllers.Comment: Extended manuscript of journal submission, containing omitted proofs and simulation

Generalized controllers for rigid formation stabilization with application to event-based controller design

This paper discusses generalized controllers for rigid formation shape stabilization. We provide unified analysis to show convergence using different controllers reported in the literature, and further prove an exponential stability of the formation system when using the general form of shape controllers. We also show that different agents can use different controllers for controlling different distances to achieve a desired rigid formation, which enables the implementation of heterogeneous agents in practice for formation shape control. We further propose an event-triggered rigid formation control scheme based on the generalized controllers. The triggering condition, event function and convergence analysis are discusse

A distributed control law with guaranteed convergence rate for identically coupled linear systems

This paper investigates the stabilization and optimization problems for a group of identically linear agents with undirected interaction topology. It is shown that a distributed control law based on local measurements and relative information exchanged f

A Declarative Perspective on Adaptive MANET Routing

In this paper, we present a declarative perspective on adaptable extensible MANET protocols. Our work builds upon declarative networking, a recent innovation for building extensible network architectures using declarative languages. We make the following contributions. First, we demonstrate that traditional MANET protocols, ranging from proactive, reactive, to epidemic can be expressed in a compact fashion as declarative networks, and we validate experimentally the use of declarative techniques to implement traditional MANETs emulated on a testbed cluster. Second, we show that the declarative framework enables policy-driven adaptation, in which a generic set of declarative rule-based policies are used to make runtime decisions on the choice of MANET protocols. Third, we present some initial ideas on fine-grained protocol composition and adaptation, where a typical MANET protocol can be composed and adapted from simpler components

Cologne: A Declarative Distributed Constraint Optimization Platform

This paper presents Cologne, a declarative optimization platform that enables constraint optimization problems (COPs) to be declaratively specified and incrementally executed in distributed systems. Cologne integrates a declarative networking engine with an off-theshelf constraint solver. We have developed the Colog language that combines distributed Datalog used in declarative networking with language constructs for specifying goals and constraints used in COPs. Cologne uses novel query processing strategies for processing Colog programs, by combining the use of bottom-up distributed Datalog evaluation with top-down goal-oriented constraint solving. Using case studies based on cloud and wireless network optimizations, we demonstrate that Cologne (1) can flexibly support a wide range of policy-based optimizations in distributed systems, (2) results in orders of magnitude less code compared to imperative implementations, and (3) is highly efficient with low overhead and fast convergence times

Evolution of Social Power in Social Networks with Dynamic Topology

The recently proposed DeGroot-Friedkin model describes the dynamical evolution of individual social power in a social network that holds opinion discussions on a sequence of different issues. This paper revisits that model, and uses nonlinear contraction analysis, among other tools, to establish several novel results. First, we show that for a social network with constant topology, each individual's social power converges to its equilibrium value exponentially fast, whereas previous results only concluded asymptotic convergence. Second, when the network topology is dynamic (i.e., the relative interaction matrix may change between any two successive issues), we show that each individual exponentially forgets its initial social power. Specifically, individual social power is dependent only on the dynamic network topology, and initial (or perceived) social power is forgotten as a result of sequential opinion discussion. Last, we provide an explicit upper bound on an individual's social power as the number of issues discussed tends to infinity; this bound depends only on the network topology. Simulations are provided to illustrate our results.The work of Mengbin Ye, Brian D. O. Anderson, and Changbin Yu was supported by the Australian Research Council under Grant DP-130103610 and Grant DP-160104500, by 111-Project D17019, by NSFC Projects 61385702 and 61761136005, and by Data61-CSIRO. The work of Mengbin Ye was supported by an Australian Government Research Training Program Scholarship. The work of Ji Liu and Tamer Bas¸ar was supported by the Office of Naval Research MURI Grant N00014-16-1-2710, and by NSF under Grant CCF 11-11342. Recommended by Associate Editor C. M. Kellett