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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. [ABSTRACT FROM AUTHOR]

This paper proposes the integration of non-orthogonal multiple access (NOMA) and broadcasting with buffer-aided (BA) full-duplex (FD) relaying to improve the spectral efficiency of a single-source two-user network. Furthermore, opportunistic relay selection is employed, activating one or two FD relays to support the end-to-end communication. The network performance is improved through buffers, allowing to switch to half-duplex (HD) transmission/reception when FD operation is not feasible. A hybrid BA FD/HD NOMA relay selection algorithm is proposed, herein called $\mathop {\mathrm{BAHyNOMA}}$ , orchestrating the three aforementioned techniques, towards a simple yet efficient and robust relay selection. $\mathop {\mathrm{BAHyNOMA}}$ is evaluated in terms of outage probability, average sum-rate and average delay, and it is observed that the average sum-rate can be doubled, while maintaining low latency. [ABSTRACT FROM AUTHOR]

In this paper, we develop finite-time horizon causal filters for general processes taking values in Polish spaces using the nonanticipative rate distortion function (NRDF). Subsequently, we apply the NRDF to design optimal filters for time-varying vector-valued Gauss-Markov processes, subject to a mean-squared error (MSE) distortion. Unlike the classical Kalman filter design, the developed filters based on the NRDF are characterized parametrically by a dynamic reverse-waterfilling optimization problem obtained via Karush-Kuhn-Tucker conditions. We develop algorithms that provide, in general, tight upper bounds to the optimal solution to the dynamic reverse-waterfilling optimization problem subject to a total and per-letter MSE distortion constraint. Under certain conditions, these algorithms produce the optimal solutions. Further, we establish a universal lower bound on the total and per-letter MSE of any estimator of a Gaussian random process. Our theoretical framework is demonstrated via simple examples. [ABSTRACT FROM AUTHOR]

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 $\mathop {\mathrm {LoCo\!\!-\!\!Link}}$. 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 $\mathop {\mathrm {LoCo\!\!-\!\!Link}}$ ($\mathop {\mathrm {d\!\!-\!\!LoCo\!\!-\!\!Link}}$) 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 $\mathop {\mathrm {LoCo\!\!-\!\!Link}}$ surpasses other algorithms, by decreasing the delay in asymmetric networks. Moreover, $\mathop {\mathrm {d\!\!-\!\!LoCo\!\!-\!\!Link}}$ 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. [ABSTRACT FROM AUTHOR]

In networks with multiple wireless nodes and especially in fast changing environments, the continuous acquisition of channel state information (CSI) may become infeasible. In this correspondence paper, we study the relay selection problem for a cooperative network, where full-duplex relays are equipped with buffers and only statistical CSI is available at the transmitters (CSIT). For this setting, a novel relay selection algorithm is proposed, named $\mathop {\mathrm{HyQoS}}$ , relying only on statistical CSIT and performing power adaptation. $\mathop {\mathrm{HyQoS}}$ solves an optimization problem for selecting the optimal set of relays. We consider two optimization approaches, first, minimization of the total power consumed in the network, and, second, maximization of the throughput per energy unit, while guaranteeing a specific quality of service, in terms of successful transmission probability. The power allocation problem is formulated as a nonlinear optimization one, taking into account inter-relay interference and residual loop interference. Numerical results show that $\mathop {\mathrm{HyQoS}}$ can reduce the power expenditure and increase the throughput, by using only statistical CSIT. [ABSTRACT FROM AUTHOR]

In this paper, a model-based perimeter control policy for large-scale urban vehicular networks is proposed. Assuming a homogeneously loaded vehicle network and the existence of a well-posed Network Fundamental Diagram (NFD), we describe a protected network throughout its aggregated dynamics including nonlinear exit flow characteristics. Within this framework of constrained optimal boundary flow gating, two main performance metrics are considered: (a) first, connected to the NFD, the concept of average network travel time and delay as a performance metric is defined; (b) second, at boundaries, we take into account additional external network queue dynamics governed by uncontrolled inflow demands. External queue capacities in terms of finite-link lengths are used as the second performance metric. Hence, the corresponding performance requirement is an upper bound of external queues. While external queues represent vehicles waiting to enter the protected network, internal queue describes the protected network’s aggregated behavior. By controlling the number of vehicles joining the internal queue from the external ones, herewith a network traffic flow maximization solution subject to the internal and external dynamics and their performance constraints is developed. The originally non-convex optimization problem is transformed to a numerically efficiently convex one by relaxing the performance constraints into time-dependent state boundaries. The control solution can be interpreted as a mechanism which transforms the unknown arrival process governing the number of vehicles entering the network to a regulated process, such that prescribed performance requirements on travel time in the network and upper bound on the external queue are satisfied. Comparative numerical simulation studies on a microscopic traffic simulator are carried out to show the benefits of the proposed method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The repetitive tracking task for time‐varying systems (TVSs) with non‐repetitive time‐varying parameters at each trial, which is also called non‐repetitive TVSs, is realized in this article using iterative learning control (ILC). A machine learning (ML) based nominal model update mechanism, which utilizes the linear regression technique to update the nominal model at each ILC trial using only the current trial information, is proposed for non‐repetitive TVSs in order to enhance the ILC performance. Given that the ML mechanism forces the model uncertainties to remain within the ILC robust tolerance, an ILC update law is proposed to deal with non‐repetitive TVSs. How to tune parameters inside ML and ILC algorithms to achieve the desired aggregate performance is also provided. The robustness and reliability of the proposed method are verified by real experiments. Real data comparison with current state‐of‐the‐art methods demonstrates its superior control performance in terms of controlling precision. This article broadens ILC applications from time‐invariant systems to non‐repetitive TVSs, adopts ML regression technique to estimate non‐repetitive time‐varying parameters between two ILC trials and proposes a detailed parameter tuning mechanism to achieve desired performance, which are the main contributions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

There are several results on the stability of nonlinear positive systems in the presence of time delays. However, most of them assume that the delays are constant. This paper considers time-varying, possibly unbounded, delays and establishes asymptotic stability and bounds the decay rate of a significant class of nonlinear positive systems which includes positive linear systems as a special case. Specifically, we present a necessary and sufficient condition for delay-independent stability of continuous-time positive systems whose vector fields are cooperative and homogeneous. We show that global asymptotic stability of such systems is independent of the magnitude and variation of the time delays. For various classes of time delays, we are able to derive explicit expressions that quantify the decay rates of positive systems. We also provide the corresponding counterparts for discrete-time positive systems whose vector fields are nondecreasing and homogeneous. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: In this note we prove the unconditional stability of the Foschini–Miljanic algorithm. Our results show that the Foschini–Miljanic algorithm is unconditionally stable (convergent) even in the presence of bounded time-varying communication delays, and in the presence of topology changes. The implication of our results may be important for the design of Code Division Multiple Access (CDMA) based wireless networks. [Copyright &y& Elsevier]

Networks with device-to-device (D2D) technology allow for two possible communication modes: traditional communication via the base station, and direct communication between the users. Recent studies show that in-band full-duplex (IBFD) operations can be advantageously combined with D2D communication to improve the spectral efficiency. However, no algorithms for selecting the communication mode of mobile users in IBFD networks have yet appeared in the literature. In this paper, we design a distributed mode selection scheme for users in D2D-enabled IBFD networks. The proposed scheme maximizes the users’ prob-ability of successful communication by leveraging only existing signaling mechanisms. [ABSTRACT FROM AUTHOR]

We analyze the per unit-time infinite horizon average cost Markov control model, subject to a total variation distance ambiguity on the controlled process conditional distribution. This stochastic optimal control problem is formulated as a minimax optimization problem in which the minimization is over the admissible set of control strategies, while the maximization is over the set of conditional distributions which are in a ball, with respect to the total variation distance, centered at a nominal distribution. We derive two new equivalent dynamic programming equations, and a new policy iteration algorithm. The main feature of the new dynamic programming equations is that the optimal control strategies are insensitive to inaccuracies or ambiguities in the controlled process conditional distribution. The main feature of the new policy iteration algorithm is that the policy evaluation and policy improvement steps are performed using the maximizing conditional distribution, which is obtained via a water filling solution of aggregating states together to form new states. Throughout the paper, we illustrate the new dynamic programming equations and the corresponding policy iteration algorithm to various examples. [ABSTRACT FROM AUTHOR]

• Accelerated first-order method for solving large-scale optimization problems with composite objective structure. • New composite estimating functions constructed by using the gradient mapping technique and a tighter lower bound on the objective function. • Efficient line-search strategy and novel convergence analysis results. • Initialization of the proposed method can be made robust to the imperfect knowledge of the strong convexity parameter. 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. [ABSTRACT FROM AUTHOR]

The aim of this paper is to address optimality of stochastic control strategies via dynamic programming subject to total variation distance ambiguity on the conditional distribution of the controlled process. We formulate the stochastic control problem using minimax theory, in which the control minimizes the payoff while the conditional distribution, from the total variation distance set, maximizes it. First, we investigate the maximization of a linear functional on the space of probability measures on abstract spaces, among those probability measures which are within a total variation distance from a nominal probability measure, and then we give the maximizing probability measure in closed form. Second, we utilize the solution of the maximization to solve minimax stochastic control with deterministic control strategies, under a Markovian and a non-Markovian assumption, on the conditional distributions of the controlled process. The results of this part include (1) minimax optimization subject to total variation distance ambiguity constraint; (2) new dynamic programming recursions, which involve the oscillator seminorm of the value function, in addition to the standard terms; and (3) a new infinite horizon discounted dynamic programming equation, the associated contractive property, and a new policy iteration algorithm. Finally, we provide illustrative examples for both the finite and infinite horizon cases. For the infinite horizon case, we invoke the new policy iteration algorithm to compute the optimal strategies. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed consensus tracking control problem for general linear multi-agent systems with external disturbances and heterogeneous time-varying input and communication delays under a directed communication graph topology, containing a spanning tree. First, for all agents whose state matrix has no eigenvalues with positive real parts, a communication-delay-related observer, which is used to construct the controller, is designed for followers to estimate the leader's state information. Second, by means of the output regulation theory, the results are relaxed to the case that only the leader's state matrix needs to be asymptotically stable or marginally stable and, under these relaxed conditions, the controller is redesigned. Both cases lead to a closed-loop error system of which the stability is guaranteed via a Lyapunov–Krasovskii functional with sufficient conditions in terms of input-delay-dependent linear matrix inequalities (LMIs). An extended LMI is proposed which, in conjunction with the rest of LMIs, results in a solution with a larger upper bound on delays than what would be feasible without it. It is highlighted that the integration of communication-delay-related observer and input-delay-related LMI to construct a fully distributed controller (which requires no global information) is scalable to arbitrarily large networks. The efficacy of the proposed scheme is demonstrated via an illustrative numerical example. [ABSTRACT FROM AUTHOR]

This paper proposes two nonlinear dynamics to solve constrained distributed optimization problem for resource allocation over a multi-agent network. In this setup, coupling constraint refers to resource-demand balance which is preserved at all-times. The proposed solutions can address various model nonlinearities, for example, due to quantization and/or saturation. Further, it allows to reach faster convergence or to robustify the solution against impulsive noise or uncertainties. We prove convergence over weakly connected networks using convex analysis and Lyapunov theory. Our findings show that convergence can be reached for general sign-preserving odd nonlinearity. We further propose delay-tolerant mechanisms to handle general bounded heterogeneous time-varying delays over the communication network of agents while preserving all-time feasibility. This work finds application in CPU scheduling and coverage control among others. This paper advances the state-of-the-art by addressing (i) possible nonlinearity on the agents/links, meanwhile handling (ii) resource-demand feasibility at all times, (iii) uniform-connectivity instead of all-time connectivity, and (iv) possible heterogeneous and time-varying delays. To our best knowledge, no existing work addresses contributions (i) - (iv) altogether. Simulations and comparative analysis are provided to corroborate our contributions. • Distributed all-time feasible strategies that solve resource allocation. • Algorithm with tolerating heterogeneous bounded delays over the communication network. • Convergence over uniformly-connected networks, even disconnected at some times. • Addressing possible nonlinearity on the nodes or links for different applications. [ABSTRACT FROM AUTHOR]