We examine threshold-based transmission strategies for distributed opportunistic medium access, and specifically address the problem of setting the threshold values so as to optimize the aggregate throughput utility o...
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ISBN:
(纸本)9781424449194
We examine threshold-based transmission strategies for distributed opportunistic medium access, and specifically address the problem of setting the threshold values so as to optimize the aggregate throughput utility of the various users. In the case of weighted logarithmic throughput utility (Proportional Fairness) we provide an adaptive algorithm for finding the optimal threshold values in a distributed fashion. Moreover, we discuss how the algorithm may be adapted to achieve packet-level stability with only limited exchange of queue length information among the various users. We also present numerical results to demonstrate the convergence of the proposed adaptive algorithm.
Critical operating modes of the strapdown inertial orientation systems (SIOS) or such rotations, under which the error of the SIOS increases with the unchanged accuracy of the gyros and the unchanged SIOS algorithm, a...
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ISBN:
(纸本)9781538609781
Critical operating modes of the strapdown inertial orientation systems (SIOS) or such rotations, under which the error of the SIOS increases with the unchanged accuracy of the gyros and the unchanged SIOS algorithm, are presented. Euler's theorem is well-known: A rigid body with a fixed point can be rotated to any angle position about some axis by some angle. Two new theorems are proved: 1. A rigid body with a fixed point can be rotated to any angular position so that the projections of the angular velocity vector on the axes connected with the body contain only harmonic components at one frequency. 2. There are rotations under which the error of any SIOS algorithm with a fixed output frequency of gyros information is 100 %. That is the SIOS algorithm completely "does not see" the rotation of the object. To avoid critical operating modes of SIOS the transition from SIOS based on unchanged algorithms to intellectual SIOS based on a strategy with the choice of various algorithms depending on the current conditions of object rotation or adaptive algorithms of SIOS is proposed.
We analyze the adaptive first order algorithm AMSGrad, for solving a constrained stochastic optimization problem with a weakly convex objective. We prove the (O) over tilde (t(-1/2)) rate of convergence for the square...
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ISBN:
(纸本)9781713845393
We analyze the adaptive first order algorithm AMSGrad, for solving a constrained stochastic optimization problem with a weakly convex objective. We prove the (O) over tilde (t(-1/2)) rate of convergence for the squared norm of the gradient of Moreau envelope, which is the standard stationarity measure for this class of problems. It matches the known rates that adaptive algorithms enjoy for the specific case of unconstrained smooth nonconvex stochastic optimization. Our analysis works with mini-batch size of 1, constant first and second order moment parameters, and possibly unbounded optimization domains. Finally, we illustrate the applications and extensions of our results to specific problems and algorithms.
A new derivation of recursive adaptive algorithms which leads to a general formulation of the updating equation is provided. After showing that the classical recursive least square (RLS) algorithm is a special case of...
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This work presents set-membership adaptive algorithms based on time-varying error bounds. A bounding ellipsoidal adaptive constrained (BEACON) recursive least-squares algorithm is described for parameter estimation su...
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ISBN:
(纸本)9781424436446
This work presents set-membership adaptive algorithms based on time-varying error bounds. A bounding ellipsoidal adaptive constrained (BEACON) recursive least-squares algorithm is described for parameter estimation subject to time-varying error bounds. The important issue of error bound specification is addressed in a new framework that takes into account parameter estimation dependency, multi-access (MAI) and inter-symbol interference (ISI) for DS-CDMA communications. An algorithm for tracking and estimating the interference power is presented and incorporated into the time-varying error bound and the BEACON technique. Computer simulations show that the new algorithms are capable of outperforming previously reported techniques with a smaller number of parameter updates and a reduced risk of overbounding or underbounding.
This paper presents a DSP-based implementation of an adaptive beamformer for the W-CDMA reverse link A comparative study of LMS and RLS adaptive algorithms is shown. which leads to the implementation taking into accou...
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ISBN:
(纸本)0780374584
This paper presents a DSP-based implementation of an adaptive beamformer for the W-CDMA reverse link A comparative study of LMS and RLS adaptive algorithms is shown. which leads to the implementation taking into account both performance results and computational load. Results for-fixed-point DSP implementation and the floating point simulations are presented.
This workshop seeks to integrate results from different domains of computer science, computational science, and mathematics. We welcome simulation papers, either hard simulations using finite element or finite differe...
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This workshop seeks to integrate results from different domains of computer science, computational science, and mathematics. We welcome simulation papers, either hard simulations using finite element or finite difference methods, or soft simulations by means of evolutionary computations, and related methods. The workshop focuses on simulations performed by using (a) agent-oriented systems;or (b) adaptive algorithms. Simulations performed by other kind of systems are also welcome. An agent-oriented system seems are attractive tools useful for numerous domains of applications. adaptive algorithms significantly decrease on the computational cost by investing computational resources when needed by the problem. (C) 2017 The Authors. Published by Elsevier B.V.
Graph filters (GFs) have attracted great interest since they can be directly implemented in a diffused way. Thus it is interesting to investigate GFs to implement signal processing operations in a distributed manner. ...
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ISBN:
(纸本)9781665433143
Graph filters (GFs) have attracted great interest since they can be directly implemented in a diffused way. Thus it is interesting to investigate GFs to implement signal processing operations in a distributed manner. However, in most GF models, the input signals are assumed to be time-invariant, static, or change at a very low rate. In addition to that, the GF coefficients are usually set to be node-invariant, i.e. the same for all the nodes. Yet, in general, the input signals may evolve with time and the underlying GF may have parameters dependent on the nodes. Therefore, in this paper, we consider dynamic input signals and both types of GF coefficients, node-variant, i.e. vary on different nodes, and node-invariant. Then, we apply LMS and RLS algorithms for GF design, along with two others called adapt-then-combine (ATC) and combined RLS (CRLS) to estimate the GF coefficients. We study and compare the performance of the algorithms and show that in the case of node-invariant GF coefficients, CRLS gives the best performance with lowest mean-square-displacement (MSD), whereas, for node-variant case, RLS represents the best results. The effect of bias in the input signal has also been examined.
In real time applications signal characteristic and signal noise cannot be determined and predicted which makes very hard while designing digital filters for noise suppression. To outstrip these problem adaptive filte...
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ISBN:
(纸本)9780819489326
In real time applications signal characteristic and signal noise cannot be determined and predicted which makes very hard while designing digital filters for noise suppression. To outstrip these problem adaptive filters are preferable in which filter coefficients are designed based on the situation by using adaptive algorithms. In this paper a simulator containing adaptive algorithms(Least Mean Square(LMS), Normalized Least Mean Square(NLMS), Recursive Least Square(RLS), Signed Least Mean Square(SLMS), Signed Normalized Least Mean Square(SNLMS)) using different applications was developed in MATLAB using Graphical User Interphone(GUI). Performance of all algorithms had been observed and some of the results obtained for different applications had been depicted by using real time noise corrupted voice signal. This proposed simulator helps the end user for depicting and comparing adaptive algorithms for real time applications. Pros and cons of each algorithm are observed manually without any motive of mathematical and paper results.
Given a mixture between two populations of coins, "positive" coins that each have|unknown and potentially different|bias >= 1/2 + Delta and "negative" coins with bias < 1/2 - Delta, we consid...
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ISBN:
(纸本)9781611976465
Given a mixture between two populations of coins, "positive" coins that each have|unknown and potentially different|bias >= 1/2 + Delta and "negative" coins with bias < 1/2 - Delta, we consider the task of estimating the fraction rho of positive coins to within additive error epsilon. We achieve an upper and lower bound of Theta(rho/epsilon(2)Delta(2) log 1/delta) samples for a 1-delta probability of success, where crucially, our lower bound applies to all fully-adaptive algorithms. Thus, our sample complexity bounds have tight dependence for every relevant problem parameter. A crucial component of our lower bound proof is a decomposition lemma (Lemma 5.2) showing how to assemble partially-adaptive bounds into a fully-adaptive bound, which may be of independent interest: though we invoke it for the special case of Bernoulli random variables (coins), it applies to general distributions. We present simulation results to demonstrate the practical efficacy of our approach for realistic problem parameters for crowdsourcing applications, focusing on the "rare events" regime where rho is small. The fine-grained adaptive flavor of both our algorithm and lower bound contrasts with much previous work in distributional testing and learning.
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