Showing papers by "Themistoklis Charalambous published in 2016"

A Survey on Buffer-Aided Relay Selection

Abstract: Relays receive and retransmit signals between one or more sources and one or more destinations. Cooperative relaying is a novel technique for wireless communications that increases throughput and extends the coverage of networks. The task of relay selection serves as a building block to realize cooperative relaying. Recently, relays with buffers have been incorporated into cooperative relaying providing extra degrees of freedom in selection, thus improving various performance metrics, such as outage probability, power reduction, and throughput, at the expense of tolerating an increase in packet delay. In this survey, we review and classify various buffer-aided relay selection policies and discuss their importance through applications. The classification is mainly based on the following aspects: 1) duplexing capabilities, 2) channel state information (CSI), 3) transmission strategies, 4) relay mode, and 5) performance metrics. Relay selection policies for enhanced physical-layer security and cognitive communications with reduced interference are also discussed. Then, a framework for modeling such algorithms is presented based on Markov Chain theory. In addition, performance evaluation is conducted for various buffer-aided relay selection algorithms. To provide a broad perspective on the role of buffer-aided relay selection, various issues relevant to fifth-generation (5G) networks are discussed. Finally, we draw conclusion and discuss current challenges, possible future directions, and emerging technologies.

Distributed Finite-Time Computation of Digraph Parameters: Left-Eigenvector, Out-Degree and Spectrum

Abstract: Many of the algorithms that have been proposed in the field of distributed computation rely on assumptions that require nodes to be aware of some global parameters. In this paper, we propose algorithms to compute some network parameters in a distributed fashion and in a finite number of steps. More specifically, given an arbitrary strongly connected network of interconnected nodes, by adapting a distributed finite-time approach, we develop distributed strategies that enable nodes to compute the following network parameters: the left-eigenvector, the out-degree, and the spectrum of weighted adjacency matrices.

Delay- and diversity-aware buffer-aided relay selection policies in cooperative networks

Abstract: Buffer-Aided (BA) relaying has shown tremendous performance improvements in terms of throughput and outage probability, although it has been criticized of suffering from long delays that are restrictive for applications, such as video streaming, Web browsing, and file sharing. In this paper, we propose novel relay selection policies aiming at reducing the average delay by incorporating the buffer size of the relays into the decision making of the relay selection process. More specifically, we first propose two new delay-aware policies. One is based on the hybrid relay selection algorithm, where the relay selection takes into account the queue sizes so that the delay is reduced and the diversity is maintained. The other approach is based on the max – link relay selection algorithm. For the max – link algorithm, a delay-aware only approach starves the buffers and increases the outage probability of the system. Thus, for max – link, we propose a delay- and diversity-aware BA relay selection policy targeting the reduction of the average delay, while maintaining the diversity of the transmission. The proposed policies are analyzed by means of Markov Chains and expressions for the outage, throughput, and delay are derived. The asymptotic performance of the policies is also discussed. The improved performance in terms of delays and the use of the proposed algorithms are demonstrated via extensive simulations and comparisons, signifying, at the same time, the need for adaptive mechanisms to handle the interplay between delay and diversity.

Location-Aided Pilot Contamination Avoidance for Massive MIMO Systems

Abstract: Pilot contamination, defined as the interference during the channel estimation process due to reusing the same pilot sequences in neighboring cells, can severely degrade the performance of massive multiple-input multiple-output systems. In this paper, we propose a location-based approach to mitigating the pilot contamination problem for uplink multiple-input multiple-output systems. Our approach makes use of the approximate locations of mobile devices to provide good estimates of the channel statistics between the mobile devices and their corresponding base stations. Specifically, we aim at avoiding pilot contamination even when the number of base station antennas is not very large, and when multiple users from different cells, or even in the same cell, are assigned the same pilot sequence. First, we characterize a desired angular region of the target user at the serving base station based on the number of base station antennas and the location of the target user, and make the observation that in this region the interference is close to zero due to the spatial separability. Second, based on this observation, we propose pilot coordination methods for multi-user multi-cell scenarios to avoid pilot contamination. The numerical results indicate that the proposed pilot contamination avoidance schemes enhance the quality of the channel estimation and thereby improve the per-cell sum rate offered by target base stations.

Optimal Radio Frequency Energy Harvesting With Limited Energy Arrival Knowledge

Abstract: We develop optimal sleeping and harvesting policies for radio frequency (RF) energy harvesting devices, formalizing the following intuition: when the ambient RF energy is low, devices consume more energy being awake than what can be harvested and should enter sleep mode; when the ambient RF energy is high, on the other hand, it is essential to wake up and harvest. Toward this end, we consider a scenario with intermittent energy arrivals described by a two-state Gilbert–Elliott Markov chain model. The challenge is that the state of the Markov chain can only be observed during the harvesting action, and not while in sleep mode. Two scenarios are studied under this model. In the first scenario, we assume that the transition probabilities of the Markov chain are known and formulate the problem as a partially observable Markov decision process (POMDP). We prove that the optimal policy has a threshold structure and derive the optimal decision parameters. In the practical scenario where the ratio between the reward and the penalty is neither too large nor too small, the POMDP framework and the threshold-based optimal policies are very useful for finding non-trivial optimal sleeping times. In the second scenario, we assume that the Markov chain parameters are unknown and formulate the problem as a Bayesian adaptive POMDP and propose a heuristic posterior sampling algorithm to reduce the computational complexity. The performance of our approaches is demonstrated via numerical examples.

Filtering with fidelity for time-varying Gauss-Markov processes

Abstract: In this paper, we revisit the relation between Nonanticipative Rate Distortion (NRD) theory and real-time realizable filtering theory. Specifically, we give the closed form expression for the optimal nonstationary (time-varying) reproduction distribution of the Finite Time Horizon (FTH) Nonanticipative Rate Distortion Function (NRDF) and we establish its connection to real-time realizable filtering theory via a realization scheme utilizing time-varying fully observable multidimensional Gauss-Markov processes. As an application we provide the optimal filter with respect to a mean square error constraint. Unlike classical filtering theory, our filtering approach based on FTH NRDF is performed with waterfilling. We also derive a universal lower bound to the mean square error of any causal estimator to Gaussian processes based on the closed form expression of FTH NRDF. Our theoretical results are demonstrated via an illustrative example.

Robust Linear Quadratic Regulator for uncertain systems

Abstract: This paper develops a Linear Quadratic Regulator (LQR), which is robust to disturbance variability, by using the total variation distance as a metric. The robust LQR problem is formulated as a minimax optimization problem, resulting in a robust optimal controller which in addition to minimizing the quadratic cost it also minimizes the level of disturbance variability. A procedure for solving the LQR problem is also proposed and an example is presented which clearly illustrates the effectiveness of our developed methodology.

A delay-aware hybrid relay selection policy

Abstract: In this paper, we propose a novel relay selection policy based on the Hybrid Relay Selection (HRS) relay selection protocol that takes into account the state of the buffers and aims at reducing the average packet delays in the network. The proposed protocol, called the Delay-Aware HRS (DA — HRS) protocol is analyzed by means of Markov Chains and expressions for the outage probability, throughput and delay are derived. The distributed implementation of the protocol is also discussed. The performance of our proposed protocol is demonstrated via extensive simulations and comparisons with the classical HRS.

Totally asynchronous distributed estimation of eigenvector centrality in digraphs with application to the PageRank problem

Abstract: We propose a distributed coordination mechanism which enables nodes in a directed graph to accurately estimate their eigenvector centrality (eigencentrality) even if they update their values at times determined by their own clocks. The clocks need neither be synchronized nor have the same speed. The main idea is to let nodes adjust the weights on outgoing links to compensate for their update speed: the higher the update frequency, the smaller the link weights. Our mechanism is used to develop a distributed algorithm for computing the PageRank vector, commonly used to assign importance to web pages and rank search results. Although several distributed approaches in the literature can deal with asynchronism, they cannot handle the different update speeds that occur when servers have heterogeneous computational capabilities. When existing algorithms are executed using heterogeneous update speeds, they compute incorrect PageRank values. The advantages of our algorithm over existing approaches are verified through illustrative examples.

On the use of fuzzy logic controllers to comply with virtualized application demands in the cloud

Abstract: As virtualization technologies enable real-time CPU allocation, it is important to build controllers that adjust the allocation in a timely fashion avoiding resource saturation and hence, dissatisfaction of the end users of services. In this work, adaptive neuro-fuzzy inference, trained on Kalman and H ∞ filters, has been used to adjust the CPU allocations based on observations of past utilization. When evaluating the performance of the proposed controller it is demonstrated that it provides even better performance than the filters it is trained on. In addition, there are no assumptions on the noise characteristics and due to the fact that the neuro-fuzzy controller can, in general, capture non-linear level processes, our controller is more robust than linear model based approaches, such as the Kalman and the H ∞ filters.

How Variability and Effort Determine Coordination at Large Forces

Abstract: Motor control is a challenging task for the central nervous system, since it involves redundant degrees of freedom, nonlinear dynamics of actuators and limbs, as well as noise. When an action is carried out, which factors does your nervous system consider to determine the appropriate set of muscle forces between redundant degrees-of-freedom? Important factors determining motor output likely encompass effort and the resulting motor noise. However, the tasks used in many previous motor control studies could not identify these two factors uniquely, as signal-dependent noise monotonically increases as a function of the effort. To address this, a recent paper introduced a force control paradigm involving one finger in each hand that can disambiguate these two factors. It showed that the central nervous system considers both force noise and amplitude, with a larger weight on the absolute force and lower weights on both noise and normalized force. While these results are valid for the relatively low force range considered in that paper, the magnitude of the force shared between the fingers for large forces is not known. This paper investigates this question experimentally, and develops an appropriate Markov chain Monte Carlo method in order to estimate the weightings given to these factors. Our results demonstrate that the force sharing strongly depends on the force level required, so that for higher force levels the normalized force is considered as much as the absolute force, whereas the role of noise minimization becomes negligible.

Optimal Nonanticipative Estimation Schemes for Time-Varying Gauss-Markov Processes

Abstract: In this paper, we derive recursive filters for time-varying multidimensional Gauss-Markov processes, which satisfy a mean square error fidelity, using the concept of Finite Time Horizon (FTH) Nonanticipative Rate Distortion Function (NRDF) and its connection to real-time realizable filtering theory. Moreover, we derive a universal lower bound on the mean square error of any estimator of time-varying multidimensional Gauss-Markov processes in terms of conditional mutual information. Unlike classical Kalman filters, the proposed filter is constructed from the solution of a reverse-waterfilling problem, which ensures that the mean square error fidelity is met. Our theoretical results are demonstrated via illustrative examples.

Location-Aided Pilot Decontamination for Massive MIMO Systems.

Abstract: One of the key limitation of massive MIMO systems is pilot contamination, which is defined as the interference during uplink channel estimation due to re-use of the same pilots in surrounding cells. In this paper, we propose a location-based approach to the pilot contamination problem for uplink MIMO systems. Our approach makes use of the approximate locations of mobile devices to provide good estimates of the channel statistics between the mobile devices and their corresponding base stations (BSs). We aim at minimizing the pilot contamination even when the number of BS antennas is not very large, and when multiple users from different cells, or even the same cell, are assigned the same pilot sequence. First, we characterize a desired angular region of the target user at the target BS in which interference is very low or zero, based on the number of BS antennas and the location of the target user. Second, based on this observation, we propose various pilot coordination methods for multi-user multi-cell scenarios to eliminate pilot contamination.

Infinite horizon discounted dynamic programming subject to total variation ambiguity on conditional distribution

Abstract: We analyze the infinite horizon minimax discounted cost Markov Control Model (MCM), for a class of controlled process conditional distributions, which belong to a ball, with respect to total variation distance metric, centered at a known nominal controlled conditional distribution with radius R ∈ [0, 2], in which the minimization is over the control strategies and the maximization is over conditional distributions. Through our analysis (i) we derive a new discounted dynamic programming equation, (ii) we show the associated contraction property, and (iii) we develop a new policy iteration algorithm. Finally, the application of the new dynamic programming and the corresponding policy iteration algorithm are shown via an illustrative example.

Optimal Estimation via Nonanticipative Rate Distortion Function and Applications to Time-Varying Gauss-Markov Processes

Abstract: In this paper, we develop {finite-time horizon} causal filters using the nonanticipative rate distortion theory. We apply the {developed} theory to {design optimal filters for} time-varying multidimensional Gauss-Markov processes, subject to a mean square error fidelity constraint. We show that such filters are equivalent to the design of an optimal \texttt{\{encoder, channel, decoder\}}, which ensures that the error satisfies {a} fidelity constraint. Moreover, we derive a universal lower bound on the mean square error of any estimator of time-varying multidimensional Gauss-Markov processes in terms of conditional mutual information. Unlike classical Kalman filters, the filter developed is characterized by a reverse-waterfilling algorithm, which ensures {that} the fidelity constraint is satisfied. The theoretical results are demonstrated via illustrative examples.