Showing papers by "Themistoklis Charalambous published in 2012"

Buffer-Aided Relay Selection for Cooperative Diversity Systems without Delay Constraints

Abstract: In this paper, we study the relay selection problem for a finite buffer-aided decode-and-forward cooperative wireless network. A relay selection policy that fully exploits the flexibility offered by the buffering ability of the relay nodes in order to maximize the achieved diversity gain is investigated. This new scheme incorporates the instantaneous strength of the wireless links as well as the status of the finite relay buffers and adapts the relay selection decision on the strongest available link by dynamically switching between relay reception and transmission. In order to analyse the new relay selection policy in terms of outage probability and diversity gain, a theoretical framework that models the evolution of the relay buffers as a Markov chain (MC) is introduced. The construction of the state transition matrix and the related steady state of the MC are studied and their impact on the derivation of the outage probability is investigated. We show that the proposed relay selection scheme significantly outperforms conventional relay selection policies for all cases and ensures a diversity gain equal to two times the number of relays for large buffer sizes.

A Framework to Describe, Analyze and Generate Interactive Motor Behaviors

Abstract: While motor interaction between a robot and a human, or between humans, has important implications for society as well as promising applications, little research has been devoted to its investigation. In particular, it is important to understand the different ways two agents can interact and generate suitable interactive behaviors. Towards this end, this paper introduces a framework for the description and implementation of interactive behaviors of two agents performing a joint motor task. A taxonomy of interactive behaviors is introduced, which can classify tasks and cost functions that represent the way each agent interacts. The role of an agent interacting during a motor task can be directly explained from the cost function this agent is minimizing and the task constraints. The novel framework is used to interpret and classify previous works on human-robot motor interaction. Its implementation power is demonstrated by simulating representative interactions of two humans. It also enables us to interpret and explain the role distribution and switching between roles when performing joint motor tasks.

Stability Analysis and Power Optimization for Energy Harvesting Cooperative Networks

Abstract: In this letter, we investigate the effects of network-layer cooperation in a wireless three-node network with energy-harvesting nodes and bursty data traffic. By modelling energy harvesting in each node as a queue (buffer) that stores the received energy, we study the interaction between data and energy queues when only knowledge of the arrival rates is available. The maximum stable throughput (in packets/slot) of the source as well as the required transmitted power for both a non-cooperative and an orthogonal decode-and-forward cooperative schemes are derived in closed-form. We prove that cooperation achieves a higher maximum stable throughout than direct link for scenarios with poor energy arrival rates.

Contractive Interference Functions and Rates of Convergence of Distributed Power Control Laws

Abstract: The standard interference functions introduced by Yates have been very influential on the analysis and design of distributed power control laws. While powerful and versatile, the framework has some drawbacks: the existence of fixed-points has to be established separately, and no guarantees are given on the rate of convergence of the iterates. This paper introduces contractive interference functions, a slight reformulation of the standard interference functions that guarantees the existence and uniqueness of fixed-points along with linear convergence of iterates. We show that many power control laws from the literature are contractive and derive, sometimes for the first time, analytical convergence rate estimates for these algorithms. We also prove that contractive interference functions converge when executed totally asynchronously and, under the assumption that the communication delay is bounded, derive an explicit bound on the convergence time penalty due to increased delay. Finally, we demonstrate that although standard interference functions are, in general, not contractive, they are all para-contractions with respect to a certain metric. Similar results for two-sided scalable interference functions are also derived.

Distributed network size estimation and average degree estimation and control in networks isomorphic to directed graphs

Abstract: Many properties of interest in graph structures are based on the nodes' average degree (i.e., the average number of edges incident to/from each node). In this work, we present asynchronous distributed algorithms, based on ratio consensus, that can be used to accurately estimate the number of nodes in a multi-component system whose communication topology is described by a directed graph. In addition, we describe an asynchronous distributed algorithm that allows each node to introduce or terminate links in order to reach a target average degree in the network. Such an approach can be useful in many realistic scenarios; for example, for the introduction and removal of renewable energy resources in a power network, while maintaining an average degree that fulfils some structural and dynamical properties and/or optimises some performance indicators of the network. The effectiveness of the proposed algorithms is demonstrated via illustrative examples.

Contractive Interference Functions and Rates of Convergence of Distributed Power Control Laws

Abstract: The standard interference functions introduced by Yates have been very influential on the analysis and design of distributed power control laws. While powerful and versatile, the framework has some drawbacks: the existence of fixed-points has to be established separately, and no guarantees are given on the rate of convergence of the iterates. This paper introduces contractive interference functions, a slight reformulation of the standard interference functions that guarantees the existence and uniqueness of fixed-points along with linear convergence of iterates. We show that many power control laws from the literature are contractive and derive, sometimes for the first time, analytical convergence rate estimates for these algorithms. We also prove that contractive interference functions converge when executed totally asynchronously and, under the assumption that the communication delay is bounded, derive an explicit bound on the convergence time penalty due to increased delay. Finally, we demonstrate that although standard interference functions are, in general, not contractive, they are all para-contractions with respect to a certain metric. Similar results for two-sided scalable interference functions are also derived.

Contractive interference functions and rates of convergence of distributed power control laws

Abstract: The standard interference functions introduced by Yates have been very influential on the analysis and design of distributed power control laws. While powerful and versatile, the framework has some drawbacks: the existence of fixed-points has to be established separately, and no guarantees are given on the rate of convergence of the iterates. This paper introduces contractive interference functions, a slight reformulation of the standard interference functions that guarantees existence and uniqueness of fixed-points and geometric convergence rates. We show that many power control laws from the literature are contractive and derive, sometimes for the first time, convergence rate estimates for these algorithms. Finally, we show that although standard interference functions are not contractive, they are paracontractions with respect to a certain metric space. Extensions to two-sided scalable interference functions are also discussed.

Overload Management in Data Stream Processing Systems with Latency Guarantees

Abstract: Stream processing systems are becoming increasingly important to analyse real-time data generated by modern applications such as online social networks. Their main characteristic is to produce a continuous stream of fresh results as new data are being generated at real-time. Resource provisioning of stream processing systems is difficult due to time-varying workload data that induce unknown resource demands over time. Despite the development of scalable stream processing systems, which aim to provision for workload variations, there still exist cases where such systems face transient resource shortages. During overload, there is a lack of resources to process all incoming data in real-time; data accumulate in memory and their processing latency grows uncontrollably compromising the freshness of stream processing results. In this paper, we present a feedback control approach to design a nonlinear discrete-time controller that has no knowledge of the system to be controlled or the workload for the data and is still able to control the average tuple end-to-end latency in a single-node stream processing system. The results, of our evaluation on a prototype stream processing system, show that our method controls the average tuple end-to-end latency despite the time-varying workload demands and increasing number of queries.

Average Consensus in the Presence of Delays and Dynamically Changing Directed Graph Topologies

Abstract: Classical approaches for asymptotic convergence to the global average in a distributed fashion typically assume timely and reliable exchange of information between neighboring components of a given multi-component system. These assumptions are not necessarily valid in practical settings due to varying delays that might affect transmissions at different times, as well as possible changes in the underlying interconnection topology (e.g., due to component mobility). In this work, we propose protocols to overcome these limitations. We first consider a fixed interconnection topology (captured by a - possibly directed - graph) and propose a discrete-time protocol that can reach asymptotic average consensus in a distributed fashion, despite the presence of arbitrary (but bounded) delays in the communication links. The protocol requires that each component has knowledge of the number of its outgoing links (i.e., the number of components to which it sends information). We subsequently extend the protocol to also handle changes in the underlying interconnection topology and describe a variety of rather loose conditions under which the modified protocol allows the components to reach asymptotic average consensus. The proposed algorithms are illustrated via examples.

Opportunistic relay selection for cooperative networks with buffers

Abstract: In this paper, a relay selection policy is proposed that fully exploits the flexibility offered by the buffering ability of the relay nodes in order to maximize the achieved diversity gain. The suggested scheme incorporates the instantaneous strength of the wireless links as well as the status of the finite relay buffers and the relay selection decision is based on the strongest available link. Hence the switching occurs dynamically between relay reception and transmission. We show that the proposed relay selection scheme significantly outperforms conventional relay selection policies for all cases and ensures a diversity gain equal to two times the number of relays for large buffer sizes.

Dynamic programming with total variational distance uncertainty

Abstract: The aim of this paper is to address optimality of stochastic control strategies via dynamic programming subject to total variational distance uncertainty on the conditional distribution of the controlled process. Utilizing concepts from signed measures, the maximization of a linear functional on the space of probability measures on abstract spaces is investigated, among those probability measures which are within a total variational distance from a nominal probability measure. The maximizing probability measure is found in closed form. These results are then applied to solve minimax stochastic control with deterministic control strategies, under a Markovian assumption on the conditional distributions of the controlled process. The results include: 1) Optimization subject to total variational distance constraints, 2) new dynamic programming recursions, which involve the oscillator seminorm of the value function.

Distributed Formation of Balanced and Bistochastic Weighted Diagraphs in Multi-Agent Systems

Abstract: Consensus strategies find a variety of applications in distributed coordination and decision making in multi-agent systems. In particular, average consensus plays a key role in a number of applications and is closely associated with two classes of digraphs, weight-balanced (for continuous-time systems) and bistochastic (for discrete-time systems). A weighted digraph is called balanced if, for each node, the sum of the weights of the edges outgoing from that node is equal to the sum of the weights of the edges incoming to that node. In addition, a weight-balanced digraph is bistochastic if all weights are nonnegative and, for each node, the sum of weights of edges incoming to that node and the sum of the weights of edges out-going from that node is unity; this implies that the corresponding weight matrix is column and row stochastic (i.e., doubly stochastic). We propose two distributed algorithms: one solves the weight-balance problem and the other solves the bistochastic matrix formation problem for a distributed system whose components (nodes) can exchange information via interconnection links (edges) that form an arbitrary, possibly directed, strongly connected communication topology (digraph). Both distributed algorithms achieve their goals asymptotically and operate iteratively by having each node adapt the (nonnegative) weights on its outgoing edges based on the weights of its incoming links (i.e., based on purely local information). We also provide examples to illustrate the operation, performance, and potential advantages of the proposed algorithms.

Medium access control via contention-based distributed power control

Abstract: A successful distributed power control algorithm requires only local measurements for updating the power level of a transmitting node, so that eventually all transmitters meet their QoS requirements. Nevertheless, the problem arises when the QoS requirements cannot be achieved for all the users in the network. In this paper, a distributed algorithm for wireless ad hoc networks which is contention-based and makes use of a back off mechanism is proposed. This algorithm aims to eliminate overhead communication, improve fairness, allow nodes to operate asynchronously while establishing some performance level. The performance of the algorithm is evaluated via simulations.

Towards distributed transmission scheduling for wireless ad hoc networks

Abstract: In this paper we study distributed transmission scheduling via power control in wireless ad hoc networks with multiple channels. The target for each node is to manage to be admitted into a channel from the available channels in the network. The aim of this work is twofold: (a) to determine how a wireless node, based on its limited information, will decide which channel to access and (b), to propose a distributed algorithm for each wireless node with which once the channel is chosen a decision is made whether to stay in the channel or not. Here, we propose an algorithm that, if adopted by all the nodes in the network, it converges to a solution that admits most of the wireless nodes in the network, based on limited information only. Simulations in MATLAB justify the good performance of the algorithm.

Variable Length Lossless Coding for Variational Distance Class: An Optimal Merging Algorithm

Abstract: In this paper we consider lossless source coding for a class of sources specified by the total variational distance ball centred at a fixed nominal probability distribution. The objective is to find a minimax average length source code, where the minimizers are the codeword lengths -- real numbers for arithmetic or Shannon codes -- while the maximizers are the source distributions from the total variational distance ball. Firstly, we examine the maximization of the average codeword length by converting it into an equivalent optimization problem, and we give the optimal codeword lenghts via a waterfilling solution. Secondly, we show that the equivalent optimization problem can be solved via an optimal partition of the source alphabet, and re-normalization and merging of the fixed nominal probabilities. For the computation of the optimal codeword lengths we also develop a fast algorithm with a computational complexity of order ${\cal O}(n)$.

Lossless Source Codes for a Class Described by Variational Distance and Optimal Merging Algorithm

Abstract: This paper considers lossless uniquely decodable source codes for a class of distributions describedby a ball with respect to the total variational distance, centered at a nominal distribution with a givenradius. The coding problem is formulated using minimax techniques, in which the maximum of theaverage codeword length over the class of distributions is minimized subject to Kraft inequality. Firstly,the maximization over the class of distributions is characterized resulting in an equivalent pay-off.consisting of the maximum and minimum codeword length and the average codeword length withrespect to the nominal distribution. Secondly, an algorithm is introduced which computes the optimalweight vector as a function of the class radius. Finally, the optimal codeword length vector is found asa function of the weight vector. I. I NTRODUCTION Lossless source codes for known probability distributions are investigated for several pay-offs,such as the average codeword length [1] and the average redundancy of the codeword length,the average of an exponential function of the codeword length [2]–[4], and the average of anexponential function of the redundancy of the codeword length [4]–[6]. For the average codewordlength pay-off the average redundancy is bounded below by zero and above by one. On the otherhand, if the true probability distribution of the source is unknown and the code is designed solely