We present a new accelerated gradient-based method for solving smooth unconstrained optimization problems. The new method exploits additional information about the objective function and is built by embedding a heavy-ball type of momentum into the Fast Gradient Method (FGM). We devise a generalization of the estimating sequences, which allows for encoding any form of information about the objective function that can aid in further accelerating the minimization process. In the black box framework, we propose a construction for the generalized estimating sequences, which is obtained by exploiting the history of the previously constructed estimating functions. Moreover, we prove that the proposed method requires at most κ 2 ln 1 ϵ + O ( 1 ) iterations to find a point x with f ( x ) − f ∗ ≤ ϵ , where ϵ is the desired tolerance and κ is the condition number of the problem. Our theoretical results are corroborated by numerical experiments on various types of optimization problems, often dealt with in different areas of the information processing sciences. Both synthetic and real-world datasets are utilized to demonstrate the efficiency of our proposed method in terms of decreasing the distance to the optimal solution, the norm of the gradient and the function value.

This paper analyzes the joint Rate Distortion Function (RDF) of correlated multivariate Gaussian sources with individual square-error distortions. Leveraging Hotelling's canonical variable form, presented is a closed-form characterization of the joint RDF, that involves a system of nonlinear equations. Furthermore, for the special case of symmetric distortions (i.e., equal distortions), the joint RDF is explicitly expressed in terms of two water-filling variables. The results greatly improve our understanding and advance the development of closed-form solutions of the joint RDF for multivariate Gaussian sources with individual square-error distortions.

In this work, we consider mixed traffic with time-sensitive users; a deadline-constrained user, and an AoI-oriented user. To develop an efficient scheduling policy, we cast a novel optimization problem formulation for minimizing the average AoI while satisfying the timely throughput constraints. The optimization problem is a Constrained Markov Decision Process (CMDP). We relax the constrained problem to an unconstrained Markov Decision Process (MDP) problem by utilizing Lyapunov optimization theory. The unconstrained problem is solved for each frame by applying backward dynamic programming. Simulation results show that the timely throughput constraints are satisfied while minimizing the average AoI. Also, simulation results show the convergence of the algorithm for different values of the weighted factor and the trade-off between the AoI and the timely throughput.

Wireless Inertial Measurement Units provide motion capture data with a low hardware cost while offering a lot of mobility for the user. The current solutions rely on Wi-Fi or custom radio protocols, which are usually access point-centre having all the traffic routed through a single access point , limiting direct interaction capabilities with the surrounding devices. Bluetooth Low Energy (BLE) forms point-to-point networks directly between two devices without additional networking, thus, a BLE based motion capture suit could enable seamless direct cooperation between two robots or a human and a robot. In this paper, the feasibility of BLE 5 for motion capturing is investigated by designing and testing an implementation of such a motion capture system using existing commercial hardware. More specifically, a mobile phone was utilized as the receiver device for real-time visualization, whereas BLE sensors were used in order to test the feasibility of the technology for actual use and to identify the bottlenecks in the system for future optimizations. The designed prototype system achieved similar or better performance for the static accuracy and delays in the system when compared to the currently existing commercial suits, while providing a feasible throughput for real-time motion capturing.

A plethora of problems arising in signal processing, machine learning and statistics can be cast as large-scale optimization problems with a composite objective structure. Such problems are typically solved by utilizing iterative first-order algorithms. In this work, we devise a new accelerated gradient-based estimating sequence technique for solving large-scale optimization problems with composite objective structure. Specifically, we introduce a new class of estimating functions, which are obtained by utilizing both a tight lower bound on the objective function, as well as the gradient mapping technique. Then, using the proposed estimating functions, we construct a class of Composite Objective Multi-step Estimating-sequence Techniques (COMET), which are endowed with an efficient line-search procedure. We prove that our proposed COMET enjoys the accelerated convergence rate, and our newly established convergence results allow for step-size adaptation. Our theoretical findings are supported by extensive computational experiments on various problem types and real-world datasets. Moreover, our numerical results show evidence of the robustness of the proposed method to the imperfect knowledge of the smoothness and strong convexity parameters.

In 5G and beyond communication systems, the notion of latency gets great momentum in wireless connectivity as a metric for serving real-time communications requirements. However, in many applications, research has pointed out that latency could be inefficient to handle applications with data freshness requirements. Recently, Age of Information (AoI) metric, which can capture the freshness of the data, has attracted a lot of attention. In this work, we consider mixed traffic with time-sensitive users; a deadline-constrained user, and an AoI-oriented user. To develop an efficient scheduling policy, we cast a novel optimization problem formulation for minimizing the average AoI while satisfying the timely throughput constraints. The formulated problem is cast as a Constrained Markov Decision Process (CMDP). We relax the constrained problem to an unconstrained Markov Decision Process (MDP) problem by utilizing the Lyapunov optimization theory and it can be proved that it is solved per frame by applying backward dynamic programming algorithms with optimality guarantees. In addition, we provide a low-complexity algorithm guaranteeing that the timely-throughput constraint is satisfied. Simulation results show that the timely throughput constraints are satisfied while minimizing the average AoI. Simulation results show the convergence of the algorithms for different values of the weighted factor and the trade-off between the AoI and the timely throughput.

The multiple-predecessor following (MPF) topology is used in vehicle platoons to make it robustly string stable and reduce the minimum employable time headway. It has been demonstrated that communication imperfections such as time delays coming from wireless communications can affect string stability as well as the minimum time headway required to guarantee string stability. Specifically, it was shown that the larger the time delay, the longer the minimum time headway will be. However, by utilizing on-board vehicle sensors, such as radar, lidar and cameras, the distance and speed of nearby vehicles can be measured almost instantaneously, i.e., with almost no delay. Another effective parameter on string stability and minimum time headway is the heterogeneity of the vehicles. Due to the immense complexity of the MPF topology, string stability analysis of this topology in literature has been confined to homogeneous platoons. In this paper, we consider the case of heterogeneous platoons under the MPF topology with the use of the combination of sensors and wireless communications for receiving information. Following that, we find conditions to guarantee the internal and string stability for the heterogeneous case and propose the minimum time headway required to guarantee string stability. Finally, we provide a table, in which we propose the minimum time headway for two other wireless communication scenarios as well: (i) having no communication delay and (ii) having fully-delayed information, i.e., all information, whether it comes from the ego vehicle or its predecessors, is delayed. In addition to exploring the analysis of string stability for the vehicles with more possible connections (vehicles after the$r^{th}$vehicle, when information from$r$immediate vehicles is used), we study the string stability conditions (with which we aim at avoiding collisions) and find the minimum time headway for the first few vehicles (vehicle$r$and all its predecessors). Numerical results clearly show the effectiveness of the proposed lower bounds.

We present a new type of heavy-ball momentum term, which is used to construct a class of generalized estimating sequences. These allow for accelerating the minimization process by exploiting the information accumulated in the previous iterates. Combining a newly introduced momentum term with the estimating sequences framework, we devise, as an example, a new black-box accelerated first-order method for solving smooth unconstrained optimization problems. We prove that the proposed method exhibits an improvement over the rate of the celebrated fast gradient method by at least a factor of $\frac{1}{{\sqrt 2 }}$, and establish that lower bound on the number of iterations carried through until convergence is $\mathcal{O}\left( {\sqrt {\frac{\kappa }{2}} } \right)$. Finally, the practical performance benefits of the proposed method are demonstrated by numerical experiments.

We propose a linear time-difference-of-arrival (TDOA) measurement model to improve distributed estimation performance for localized target tracking. We design distributed filters over sparse (possibly large-scale) communication networks using consensus-based data-fusion techniques. The proposed distributed and localized tracking protocols considerably reduce the sensor network’s required connectivity and communication rate. We, further, consider κ-redundant observability and fault-tolerant design in case of losing communication links or sensor nodes. We present the minimal conditions on the remaining sensor network (after link/node removal) such that the distributed observability is still preserved and, thus, the sensor network can track the (single) maneuvering target. The motivation is to reduce the communication load versus the processing load, as the computational units are, in general, less costly than the communication devices. We evaluate the tracking performance via simulations in MATLAB.

Motivated by recent development in networking and parallel data-processing, we consider a distributed and localized finite-sum (or fixed-sum) allocation technique to solve resource-constrained convex optimization problems over multi-agent networks (MANs). Such networks include (smart) agents representing an intelligent entity capable of communication, processing, and decision-making. In particular, we consider problems subject to practical nonlinear constraints on the dynamics of the agents in terms of their communications and actuation capabilities (referred to as the node dynamics), e.g., networks of mobile robots subject to actuator saturation and quantized communication. The considered distributed sum-preserving optimization solution further enables adding purposeful nonlinear constraints, for example, sign-based nonlinearities, to reach convergence in predefined-time or robust to impulsive noise and disturbances in faulty environments. Moreover, convergence can be achieved under minimal network connectivity requirements among the agents; thus, the solution is applicable over dynamic networks where the channels come and go due to the agent’s mobility and limited range. This paper discusses how various nonlinearity constraints on the optimization problem (e.g., collaborative allocation of resources) can be addressed for different applications via a distributed setup (over a network).

This paper derives the optimal test channel distribution and the complete characterization of the classical Gorbunov and Pinsker (1973), Gorbunov and Pinsker (1974) nonanticipatory epsilon entropy of multivariate Gaussian Markov sources with square-error fidelity, which remained an open problem since 1974. The paper also formulates a state dependent nonanticipatory epsilon entropy, in which past reproductions are available to the decoder and not to the encoder, the test channel is specified with respect to an auxiliary (state) random process, and the reproduction process is a causal function of past reproduction and the auxiliary random process. This variation is analogous to the Wyner and Ziv (1976) and Wyner (1978) rate distortion function (RDF), of memoryless sources. It is shown that the operational rate of zero-delay codes, with past reproductions available to the decoder but not to the encoder is bounded below by the state dependent nonanticipatory epsilon entropy rate. For the case of multivariate Gaussian Markov sources with square-error fidelity, the optimal test channel distribution and the complete characterization of the state dependent of nonanticipatory epsilon entropy are derived, and also shown that that the two nonanticipatory epsilon entropies coincide. The derivations are new; they are based on structural properties of the stochastic realizations of the reproduction process that induce the optimal test channel distributions. They are derived using, achievable lower bounds on information theoretic measures, properties of mean-square estimation theory, Hadamard’s inequality, and canonical correlation coefficients of a tuple of multivariate jointly Gaussian random processes. Applications of the nonanticipatory epsilon entropy and its state dependent variation are discussed to the areas of control of unstable Gaussian systems over limited memory channels, design of causal estimators for Gaussian Markov sources with a fidelity criterion, computation of the rate loss of causal and zero-delay codes of Gaussian Markov sources with respect to non-causal codes.

For platoons under the multiple-predecessor following (MPF) topology, communication delays can compromise both the internal stability and string stability. The most straightforward solution to guarantee stability is by increasing the time headway. However, time headway plays a significant role in road capacity and increasing its value is in contrast with the idea of platooning. In this study, internal stability and string stability of platoons suffering from communication delays are investigated and a lower bound for the time headway is proposed. Using this bound, platoons do not need to massively increase the time headway in order to compensate for the effects of communications delays. Finally, we evaluate the proposed lower bound on the time headway and the simulation results demonstrate its effectiveness.

We present robust dynamic resource allocation mechanisms to allocate application resources meeting Service Level Objectives (SLOs) agreed between cloud providers and customers. In fact, two filter-based robust controllers, i.e.,$\mathcal {H}_{\infty }$filter and Maximum Correntropy Criterion Kalman filter (MCC-KF), are proposed. The controllers are self-adaptive, with process noise variances and covariances calculated using previous measurements within a time window. In the allocation process, a bounded client mean response time ($\mathop {\mathrm{mRT}}$) is maintained. Both controllers are deployed and evaluated on an experimental testbed hosting the RUBiS (Rice University Bidding System) auction benchmark web site. The proposed controllers offer improved performance under abrupt workload changes, shown via rigorous comparison with current state-of-the-art. On our experimental setup, the Single-Input-Single-Output (SISO) controllers can operate on the same server where the resource allocation is performed; while Multi-Input-Multi-Output (MIMO) controllers are on a separate server where all the data are collected for decision making. SISO controllers take decisions not dependent to other system states (servers), albeit MIMO controllers are characterized by increased communication overhead and potential delays. While SISO controllers offer improved performance over MIMO ones, the latter enable a more informed decision making framework for resource allocation problem of multi-tier applications.

Full-duplex relaying is an enabling technique of sixth generation (6G) mobile networks, promising tremendous rate and spectral efficiency gains. In order to improve the performance of full-duplex communications, power control is a viable way of avoiding excessive loop interference at the relay. Unfortunately, power control requires channel state information of both source-relay and relay-destination channels, as well as of the loop interference channel, thus resulting in increased overheads. Aiming to offer a low-complexity alternative for power control in full-duplex relay networks, we adopt reward-based learning in the sense of multi-armed bandits. More specifically, we provide bandit-based power control algorithms, relying on acknowledgements/negative-acknowledgements observations by the relay. The proposed algorithms avoid the need for channel state information acquisition and exchange, and can be employed in a distributed manner. Performance evaluation results in terms of outage probability, average throughput and accumulated regret over time highlight an interesting performance-complexity trade-off compared to optimal power control with full channel knowledge and significant performance gains over the cases without power control and random power level selection.

In this paper we analyze the joint rate distortion function (RDF), for a tuple of correlated sources taking values in abstract alphabet spaces (i.e., continuous) subject to two individual distortion criteria. First, we derive structural properties of the realizations of the reproduction Random Variables (RVs), which induce the corresponding optimal test channel distributions of the joint RDF. Second, we consider a tuple of correlated multivariate jointly Gaussian RVs,$X_{1}:\Omega\rightarrow \mathbb{R}^{p_{1}}, X_{2}:\Omega\rightarrow \mathbb{R}^{p_{2}}$with two square-error fidelity criteria, and we derive additional structural properties of the optimal realizations, and use these to characterize the RDF as a convex optimization problem with respect to the parameters of the realizations. We show that the computation of the joint RDF can be performed by semidefinite programming. Further, we derive closed-form expressions of the joint RDF, such that Gray's [1] lower bounds hold with equality, and verify their consistency with the semidefinite programming computations.

We analyze the performance of amplify-and-forward (AF) automatic repeat request (ARQ) for a two-hop cooperative system with reliability constrains. For this setup, we first derive the closed-form outage probability expression. Next, we present a power allocation scheme that allows us to achieve a target outage probability, while minimizing the outage-weighted average power expenditure for asymmetric power allocation between the source and relay. This is cast as an optimization problem, and the optimal power allocation (OPA) is obtained in closed form by invoking the Karush-Kuhn-Tucker (KKT) conditions. We evaluate numerically the OPA strategy between different AFARQ transmission rounds and we show that the proposed scheme provides large power gains with respect to the optimized point-to-point ARQ scheme, as well as with respect to the equal power allocation (EPA) strategy.

The complete characterization of the Gorbunov and Pinsker [1], [2] nonanticipatory epsilon entropy of multivariate Gauss-Markov sources with square-error fidelity is derived, which remained an open problem since 1974. Specifically, it is shown that the optimal matrices of the stochastic realization of the optimal test channel or reproduction distribution, admit spectral representations with respect to the same unitary matrices, and that the optimal reproduction process is generated, subject to pre-processing and post-processing by memoryless parallel additive Gaussian noise channels. The derivations and analyses are new and bring out several properties of such optimization problems over the space of conditional distributions and their realizations.

In this work, we combine the two notions of timely delivery of information to study their interplay; namely, deadline-constrained packet delivery due to latency constraints and freshness of information. More specifically, we consider a two-user multiple access setup with random-access, in which user 1 is a wireless device with a queue and has external bursty traffic which is deadline-constrained, while user 2 monitors a sensor and transmits status updates to the destination. We provide analytical expressions for the throughput and drop probability of user 1, and an analytical expression for the average Age of Information (AoI) of user 2 monitoring the sensor. The relations reveal that there is a trade-off between the average AoI of user 2 and the drop rate of user 1: the lower the average AoI, the higher the drop rate, and vice versa. Simulations corroborate the validity of our theoretical results.

This paper describes an indoor navigation approach using estimation and control for horizontal translational motion and heading angle for quadrotor Unmanned Aerial Vehicles (UAVs) via Ultra-Wideband (UWB)-based localization. In particular, to cope with noisy measurements, emanating from model uncertainties, and Non-Line-Of-Sight (NLOS) conditions, a Linear Quadratic Regulator (LQR) is deployed along with a Maximum Correntropy Criterion Kalman Filter (MCC-KF). This approach has proven improved robustness compared to the traditional Kalman Filter (KF) against non-Gaussian noise. A testbed with a quadrotor was developed for evaluating the performance of our proposed approach. We demonstrate, via the experimental setup, that the MCC-KF outperforms the use of KF in the presence of shots of mixed noise and communication delays, enabling onboard robust estimation and control via UWB-based localization.

This paper investigates the impact of the communication frequency on the remote control of automated vehicles. In particular, we consider a remote controller, which receives vehicles' state information and issues control commands based on a model predictive control (MPC) framework, to steer the vehicles to reach their respective target position intervals at given specific times. We present a framework where both state information (from the vehicles to the controller) and control actions (from the controller to the vehicles) are communicated through a wireless network. Due to limited communication resources and possible channel impairments, information is not necessarily always provided to the destination (either the controller or the vehicles). Herein, we particularly focus on the communications to the controller and investigate the effect of frequency and last instant of communication. Our results quantify the impact of these factors on the system performance, and subsequently, underline the need for an efficient resource allocation scheme.

We consider the problem of controlling a vehicle moving towards an intersection by means of a remote controller over an unreliable channel. This channel affects both uplink communication (when the vehicle sends its state information to the controller) and downlink information (when the vehicle receives control actions from the controller). We propose a probabilistic framework to compute control actions at the controller in the presence of such unreliable communications. The controller is evaluated under different channel conditions and compared to two nominal controllers, one that assumes perfect communication and one that assumes no communication. We find that for low packet loss rates, the proposed controller leads to less aggressive control actions than the former and generally lower cost than the latter. We additionally consider the mismatch between the perceived knowledge of the channel at the controller, and the actual channel conditions. We evaluate the performance of our controller under this mismatch, which is of interest when the controller is designed.

Buffer-aided relays can improve the diversity of multi-hop networks, however, they increase packet delays. Thus, various delay-aware protocols have been developed, but without considering the transmission diversity. Moreover, most works adopt ideal assumptions, such as symmetric links, perfect channel state information (CSI), and error-free feedback channels. So, we propose a low-complexity (LoCo) link selection algorithm, herein called LoCo-Link. The proposed algorithm may experience delays during CSI updates, and hence, by using outdated CSI its performance may deteriorate. To alleviate this issue, we next propose a distributed version of LoCo-Link (d-LoCo-Link) dealing with outdated CSI. In both algorithms, the source performs broadcasting toward multiple relays; when the packets are transmitted by a relay to the destination, they are discarded from all other relays. This coordination relies on feedback channels. For non error-free feedback channels, we propose a scheme in which the relays listen to the transmission of the best relay and drop duplicate packets. Results show that LoCo-Link surpasses other algorithms, by decreasing the delay in asymmetric networks. Moreover, d-LoCo-Link avoids diversity losses due to outdated CSI, while the effect of non error-free feedback channels is mitigated by taking advantage of the inter-relay channels.

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.

This paper presents a 2D Delaunay triangulation core for surface reconstruction implemented on a Field Programmable Gate Array (FPGA) chip. The core implementation is derived using high-level synthesis from a C++ description of an incremental 2D Delaunay triangulation algorithm. This description was modified accordingly so that it can be embedded into a FPGA chip using hardware description language. Goal of this work is to increase the execution speed of the algorithm so as to allow for real-time operation. Towards this end, we performed an optimization process using high level synthesis directives which pipeline regions of the code in order to achieve delay optimization. We show preliminary results using standard benchmark models for surface reconstruction, which show the performance of our design.

Virtualized servers have been the key for the efficient deployment of cloud applications. As the application demand increases, it is important to dynamically adjust the CPU allocation of each component in order to save resources for other applications and keep performance high, e.g., the client mean response time (mRT) should be kept below a Quality of Service (QoS) target. In this work, a new form of Kalman filter, called the Maximum Correntropy Criterion Kalman Filter (MCC-KF), has been used in order to predict, and hence, adjust the CPU allocations of each component while the RUBiS auction site workload changes randomly as the number of clients varies. MCC-KF has shown high performance when the noise is non-Gaussian, as it is the case in the CPU usage. Numerical evaluations compare our designed framework with other current state-of-the-art using real-data via the RUBiS benchmark website deployed on a prototype Xen-virtualized cluster.

While buffer-aided relaying improves the diversity of a multi-hop network, its deployment introduces time-delays, thus rendering buffering unreliable for delay-intolerant applications. To alleviate excessive delays, various studies propose delayaware protocols, but at the expense of reduced diversity, and consequently, increased outage probability. Attempts to maintain the diversity of the system while trying to reduce delays, however, may lead to even higher delays, especially in asymmetric topologies. In this work, we propose a Low-Complexity (LoCo) link selection algorithm, herein called LoCo - Link, that aims at reducing packet delays and enhancing the performance of practical asymmetric two-hop networks. The complexity of LoCo - Link is derived and compared with other state-of-the-art relay selection policies. The performance of the proposed algorithm is evaluated in terms of outage probability, average throughput and average delay, focusing on scenarios with asymmetric links.

In this paper, we propose novel relay selection policies that aim at reducing the average delay by incorporating the buffer size of the relay nodes into the relay selection process. More specifically, we propose two delay-aware protocols that are based on the max - link relay selection protocol. First, a delay-aware only approach while it reduces the delays considerably it starves the buffers and increases the outage probability of the system. Towards this end, we propose a delay- and diversity-aware buffer-aided relay selection policy that aims at reducing the average delay considerably and at the same time maintaining good diversity. The protocols are analyzed by means of Markov Chains and expressions for the outage, throughput and delay are derived. The performance and use of our proposed algorithms is demonstrated via extensive simulations and comparisons.

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.

In this paper, we present a relay selection scheme which combines the spectral efficiency of successive opportunistic relaying with the robustness of single-link relay selection. More specifically, we propose a scheme that minimizes the total energy expenditure per time slot under an inter-relay interference cancellation scheme. The new relay selection policy is analyzed in terms of outage probability and diversity by modeling the evolution of relay buffers as a Markov Chain. We construct the state transition matrix of the Markov Chain and obtain its stationary distribution, which in turn, yields the outage probability. The proposed scheme outperforms relevant state-of-the-art relay selection schemes in terms of throughput, diversity, energy efficiency and average delay, as demonstrated via representative numerical examples.

In this work, we study a cooperative network with multiple full-duplex buffer-aided relays. A hybrid cooperative relaying policy is proposed that employs power adaptation and consists of two alternative schemes: (i) full-duplex transmission through the relay which requires the least total power expenditure and loop interference is mitigated through power adaptation; (ii) buffer-aided max - link selection with power adaptation, when full-duplexity is not feasible. Aiming to reduce the overhead of channel state information (CSI) acquisition and processing, we propose a suboptimal distributed method for relay selection, for which the network performance is not degraded significantly. We show that power adaptation offers reduced overhead of CSI acquisition. Numerical results and comparisons with other state-of-the-art relaying schemes are provided and performance evaluation in terms of throughput, power minimization and switching rate, show the benefits of the proposed hybrid scheme.

In this paper, we study a simple X-relay configuration where the shared relay operates in full-duplex (FD) mode. The relay node may have limited spatial degrees of freedom, and as a result, it may not be able to handle both the loop interference and the multiuser interference. Hence, a decision on the precoding scheme is necessitated. It is often the case that the relay does not have the option of real-time switching between different precoding schemes, either due to hardware limitations of the relay or increased complexity of the problem. Hence, we investigate a “static” precoding decision where the relay node decides on its precoding scheme based only on statistical knowledge of the channel conditions. To perform this decision, the behavior of the system is formulated as a Markov chain and the outage probability of the system is derived in a closed-form with the precoding decision as a parameter. The outage probability is minimized by optimally choosing the precoding scheme, using easily verifiable conditions on the statistical knowledge of the channel conditions. Simulations validate the investigated scheme.

Resource management of virtualized servers in data centers has become a critical task, since it enables cost-effective consolidation of server applications. Resource management is an important and challenging task, especially for multitier applications with unpredictable time-varying workloads. Work in resource management using control theory has shown clear benefits of dynamically adjusting resource allocations to match fluctuating workloads. However, little work has been done toward adaptive controllers for unknown workload types. This work presents a new resource management scheme that incorporates the Kalman filter into feedback controllers to dynamically allocate CPU resources to virtual machines hosting server applications. We present a set of controllers that continuously detect and self-adapt to unforeseen workload changes. Furthermore, our most advanced controller also self-configures itself without any a priori information and with a small 4.8% performance penalty in the case of high-intensity workload changes. In addition, our controllers are enhanced to deal with multitier server applications: by using the pair-wise resource coupling between tiers, they improve server response to large workload increases as compared to controllers with no such resource-coupling mechanism. Our approaches are evaluated and their performance is illustrated on a 3-tier Rubis benchmark website deployed on a prototype Xen-virtualized cluster.

This paper presents lossless prefix codes optimized with respect to a payoff criterion consisting of a convex combination of maximum codeword length and average codeword length. The optimal codeword lengths obtained are based on a new coding algorithm, which transforms the initial source probability vector into a new probability vector according to a merging rule. The coding algorithm is equivalent to a partition of the source alphabet into disjoint sets on which a new transformed probability vector is defined as a function of the initial source probability vector and scalar parameter. The payoff criterion considered encompasses a tradeoff between maximum and average codeword length; it is related to a payoff criterion consisting of a convex combination of average codeword length and average of an exponential function of the codeword length, and to an average codeword length payoff criterion subject to a limited length constraint. A special case of the first related payoff is connected to coding problems involving source probability uncertainty and codeword overflow probability, whereas the second related payoff compliments limited length Huffman coding algorithms.

We investigate the problem of neighbor discovery in multichannel wireless ad hoc and sensor networks with epidemic information dissemination. Previous works have considered neighbor discovery in a single channel where at most one node can be discovered per time instant. To reduce the effect of collisions observed in single channel solutions, we formulate models for multichannel neighbor discovery and allow for epidemic dissemination of information. As a result, nodes can discover all their neighbors faster, either directly or indirectly by hopping between orthogonal channels and exploring the neighbors in each of them. We show analytically, by simulations, and by experimental evaluations that the expected neighbor discovery time is reduced considerably compared to single channel neighbor discovery solutions.

In this paper we consider a simple cooperative network consisting of a source, a destination and a cluster of decode-and-forward relays characterized by the half-duplex constraint. At each time-slot the source and (possibly) one of the relays transmit a packet to another relay and the destination, respectively. When the source and a relay transmit simultaneously, inter-relay interference is introduced at the receiving relay. In this work, with the aid of buffers at the relays, we mitigate the detrimental effect of inter-relay interference through either interference cancellation or mitigation. More specifically, we propose the min-power opportunistic relaying protocol that minimizes the total energy expenditure per time slot under an inter-relay interference cancellation scheme. The min-power relay-pair selection scheme, apart from minimizing the energy expenditure, also provides better throughput and lower outage probability than existing works in the literature. The performance of the proposed scheme is demonstrated via illustrative examples and simulations in terms of outage probability and average throughput.

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.

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.

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.

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.

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.

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.

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.

This paper presents prefix codes which minimize various criteria constructed as a convex combination of maximum codeword length and average codeword length, or, a convex combination of the average of an exponential function of the codeword length and the average codeword length. This framework encompasses as a special case several criteria previously investigated in the literature, while relations to universal coding is discussed. The coding algorithm derived is parametric resulting in re-adjusting the initial source probabilities via a weighted probability vector according to a merging rule. An algorithm is presented to compute the weighting vector.