Broadcast is an essential and widely used operation in multihop wireless networks. Minimum latency broadcast scheduling (MLBS) aims to find a collision-free scheduling for broadcast with the minimum latency. Previous ...
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Broadcast is an essential and widely used operation in multihop wireless networks. Minimum latency broadcast scheduling (MLBS) aims to find a collision-free scheduling for broadcast with the minimum latency. Previous work on MLBS mostly assumes that nodes are always active, and, thus, is not suitable for duty-cycled scenarios. In this paper, we investigate the MLBS problem in duty-cycled multihop wireless networks (MLBSDC problem). We prove both the one-to-all and the all-to-all MLBSDC problems to be NP-hard. We propose a novel approximation algorithm called OTAB for the one-to-all MLBSDC problem, and two approximation algorithms called UTB and UNB for the all-to-all MLBSDC problem under the unit-size and the unbounded-size message models, respectively. The approximation ratios of the OTAB, UTB, and UNB algorithms are at most 17 vertical bar T vertical bar, 17 vertical bar T vertical bar + 20, and (Delta + 22)vertical bar T vertical bar, respectively, where vertical bar T vertical bar denotes the number of time slots in a scheduling period, and Delta denotes the maximum node degree of the network. The overhead of our algorithms is at most constant times as large as the minimum overhead in terms of the total number of transmissions. We also devise a method called Prune to further reduce the overhead of our algorithms. Extensive simulations are conducted to evaluate the performance of our algorithms.
An implementation of the Newton-Raphson approach to compute the minimum phase moving-average spectral factor of a finite positive definite correlation sequence is presented. Each step in the successive approximation m...
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An implementation of the Newton-Raphson approach to compute the minimum phase moving-average spectral factor of a finite positive definite correlation sequence is presented. Each step in the successive approximation method involves a system of linear equations that is solved using either the Levinson algorithm backwards (the Jury stability test), or a symmetrized version of the Euclid algorithm. Various properties of the Newton-Raphson map are studied. The algorithm is generalized to other symmetries (other than with respect to the unit circle). The special case of the symmetry with respect to the imaginary axis is presented and related to the Routh-Hurwitz stability test for continuous time transfer function.< >
Maximum consensus estimation plays a critically important role in several robust fitting problems in computer vision. Currently, the most prevalent algorithms for consensus maximization draw from the class of randomiz...
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Maximum consensus estimation plays a critically important role in several robust fitting problems in computer vision. Currently, the most prevalent algorithms for consensus maximization draw from the class of randomized hypothesize-and-verify algorithms, which are cheap but can usually deliver only rough approximate solutions. On the other extreme, there are exact algorithms which are exhaustive search in nature and can be costly for practical-sized inputs. This paper fills the gap between the two extremes by proposing deterministic algorithms to approximately optimize the maximum consensus criterion. Our work begins by reformulating consensus maximization with linear complementarity constraints. Then, we develop two novel algorithms: one based on non-smooth penalty method with a Frank-Wolfe style optimization scheme, the other based on the Alternating Direction Method of Multipliers (ADMM). Both algorithms solve convex subproblems to efficiently perform the optimization. We demonstrate the capability of our algorithms to greatly improve a rough initial estimate, such as those obtained using least squares or a randomized algorithm. Compared to the exact algorithms, our approach is much more practical on realistic input sizes. Further, our approach is naturally applicable to estimation problems with geometric residuals. Matlab code and demo program for our methods can be downloaded from https://***/FQcxpi.
Scheduling of wireless transmissions is a core component of performance optimization of wireless ad-hoc networks. Current radio technologies offer multi-rate transmission capability, which allows to increase the netwo...
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Scheduling of wireless transmissions is a core component of performance optimization of wireless ad-hoc networks. Current radio technologies offer multi-rate transmission capability, which allows to increase the network's throughput. Nevertheless, most approximability results of scheduling algorithms have focused on single-rate radios. In this paper, we propose two formulations for the problem of scheduling wireless requests with multiple data-rates, considering the physical interference model with uniform power assignment. The objective of both problems is to select a subset of communication requests to transmit simultaneously, such that the sum of their data rates is maximized and no collisions occur. In the first formulation, data-rates are given as part of the input. In the second formulation, the data-rate assignment is part of the solution. We show that, under certain constraints on the input, these problems can be approximated by a disk graph model. This means that, despite the global nature of the physical interference model, conflicts between simultaneous requests can be restricted to the local neighborhood of the transmitting nodes. We show how to build the corresponding disk graph instances and prove that a weighted maximum independent set in this graph-based model provides a constant-factor approximation in the physical interference model. Moreover, we implement a polynomial-time approximation scheme, as well as a parallel implementation of the algorithm, to obtain solutions that are within an arbitrarily small factor of being optimal in the disk graph model. (C) 2016 Elsevier B.V. All rights reserved.
We consider the rooted orienteering problem: Given a set P of n points in the plane, a starting point r is an element of P, and a length constraint B, one needs to find a path starting from r that visits as many point...
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We consider the rooted orienteering problem: Given a set P of n points in the plane, a starting point r is an element of P, and a length constraint B, one needs to find a path starting from r that visits as many points of P as possible and of length not exceeding B. We present a (1 - epsilon)-approximation algorithm for this problem that runs in n(O(1/epsilon)) time;the computed path visits at least (1-epsilon) k(opt) points of P, where kopt is the number of points visited by an optimal solution. This is the first polynomial time approximation scheme for this problem. The algorithm also works in higher dimensions.
An inverse problem of reconstructing real permittivity of a plane-parallel layer in a perfectly conducting rectangular waveguide or in free space from experimental data using an explicit expression for the scattering ...
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An inverse problem of reconstructing real permittivity of a plane-parallel layer in a perfectly conducting rectangular waveguide or in free space from experimental data using an explicit expression for the scattering matrix is considered. In general, this problem is improperly posed and may be unsolvable due to inaccuracy of the experimental data, and for a perfect noiseless experiment the solution may be not unique because the scattering coefficients curve has self-intersection points. It is shown that the traditional multi-frequency method of measurements applied in vector network analyzers can be justified. The following facts are rigorously proved in the paper: nonuniqueness of the solution can be removed if the frequency resolution is sufficiently small;and an algorithm for processing measurement results using least squares provides an approximate solution to the problem that converges to the exact one when the quality of the experiment improves, the convergence rate depends on the number of frequencies used in the experiment.
This article investigates the tracking problem of event-triggered prescribed performance fuzzy fault-tolerant control (FTC) for unknown Euler-Lagrange systems with actuator faults and external disturbances. First, the...
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This article investigates the tracking problem of event-triggered prescribed performance fuzzy fault-tolerant control (FTC) for unknown Euler-Lagrange systems with actuator faults and external disturbances. First, the barrier Lyapunov functions (BLFs) and prescribed performance functions are synthesized to guarantee that the tracking errors satisfy the preset transient performance. Different from existing prescribed performance control methods, which require the initial values of the tracking errors to be within the prescribed performance functions, an error transformation method is introduced to ensure that the tracking errors with any bounded initial values can enter the preset boundaries within a preset time. Then, considering the unavailability of system parameters, the fuzzy logic systems are used to approximate unknown parameters of the system. What is more, to solve the problem of limited communication and computing resources in practical systems, an improved event-triggered control (ETC) scheme is proposed, which can reduce the communication and computation burden without satisfying the input-to-state stability assumption. Meanwhile, the Zeno phenomenon can be avoided. Furthermore, the effects of actuator faults and the event-triggered mechanism are handled by Nussbaum gain technology. Finally, the superiority of the proposed control algorithm is verified by simulation results.
We introduce and study the donation center location problem, which has an additional application in network testing and may also be of independent interest as a general graph-theoretic problem. Given a set of agents a...
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We introduce and study the donation center location problem, which has an additional application in network testing and may also be of independent interest as a general graph-theoretic problem. Given a set of agents and a set of centers, where agents have preferences over centers and centers have capacities, the goal is to open a subset of centers and to assign a maximum-sized subset of agents to their most-preferred opened centers, while respecting the capacity constraints. We prove that in general, the problem is hard to approximate within n (1/2-I mu) for any I mu > 0. In view of this, we investigate two special cases. In one, every agent has a bounded number of centers on its preference list, and in the other, all preferences are induced by a line-metric. We present constant-factor approximation algorithms for the former and exact polynomial-time algorithms for the latter.
This paper presents a new approach to the problem of designing a finite impulse response filter of specified length, q, which approximates in uniform frequency (L infinity) norm a given desired (possibly infinite impu...
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This paper presents a new approach to the problem of designing a finite impulse response filter of specified length, q, which approximates in uniform frequency (L infinity) norm a given desired (possibly infinite impulse response) causal, stable filter transfer function. We derive an algorithm-independent lower bound on the achievable approximation error and then present an approximation method which involves the solution of a fixed number of all-pass (Nehari) extension problems and so is called the Nehari shuffle. Upper and lower bounds on the approximation error are derived for the algorithm. These bounds are calculable a priori so the length of filter required to satisfy a given maximum error can be found before designing the filter. Examples indicate that the method closely approaches the derived global lower bound. We compare the new method with the Preuss (complex Remez exchange) algorithm in some examples.
The performance of a code under the maximum-likelihood (ML) decoder highly depends on the weight enumerating function (WEF). However, how to compute efficiently the WEF of a polar code or a polarization-adjusted convo...
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The performance of a code under the maximum-likelihood (ML) decoder highly depends on the weight enumerating function (WEF). However, how to compute efficiently the WEF of a polar code or a polarization-adjusted convolutional (PAC) code is still an open problem. For the design of stand-alone polar codes, we consider enumerating the number of minimum-weight non-zero codewords of the polar code. The block error rate (BLER) under the ML decoder can be approximated as a function of the minimum weight of non-zero codewords and its multiplicity. On the other hand, for the design of PAC codes, we consider enumerating the WEF averaged over the ensemble of random PAC codes. The ML-BLER upper bound can be represented as a function of the WEF. The bit-channel selection algorithms for polar codes and PAC codes are proposed, which take both utilization of the polarization effect and the ML decoding performance as selection criteria. Simulation results show that the proposed stand-alone polar codes are competitive when the block length gets larger. Also, simulation results show that the proposed PAC codes yield excellent performance for a wide range of code rates and block lengths and outperform the 5G polar codes.
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