8,617 research outputs found

    Nonlinear Channel Estimation for OFDM System by Complex LS-SVM under High Mobility Conditions

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    A nonlinear channel estimator using complex Least Square Support Vector Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long Term Evolution (LTE) downlink under high mobility conditions. The estimation algorithm makes use of the reference signals to estimate the total frequency response of the highly selective multipath channel in the presence of non-Gaussian impulse noise interfering with pilot signals. Thus, the algorithm maps trained data into a high dimensional feature space and uses the structural risk minimization (SRM) principle to carry out the regression estimation for the frequency response function of the highly selective channel. The simulations show the effectiveness of the proposed method which has good performance and high precision to track the variations of the fading channels compared to the conventional LS method and it is robust at high speed mobility.Comment: 11 page

    Optimizing forecast model complexity using multi-objective evolutionary algorithms

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    Copyright © 2004 World ScientificWhen inducing a time series forecasting model there has always been the problem of defining a model that is complex enough to describe the process, yet not so complex as to promote data ‘overfitting’ – the so-called bias/variance trade-off. In the sphere of neural network forecast models this is commonly confronted by weight decay regularization, or by combining a complexity penalty term in the optimizing function. The correct degree of regularization, or penalty value, to implement for any particular problem however is difficult, if not impossible, to know a priori. This chapter presents the use of multi-objective optimization techniques, specifically those of an evolutionary nature, as a potential solution to this problem. This is achieved by representing forecast model ‘complexity’ and ‘accuracy’ as two separate objectives to be optimized. In doing this one can obtain problem specific information with regards to the accuracy/complexity trade-off of any particular problem, and, given the shape of the front on a set of validation data, ascertain an appropriate operating point. Examples are provided on a forecasting problem with varying levels of noise

    End-to-end Driving via Conditional Imitation Learning

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    Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at https://youtu.be/cFtnflNe5fMComment: Published at the International Conference on Robotics and Automation (ICRA), 201

    Neural scaling laws for an uncertain world

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    Autonomous neural systems must efficiently process information in a wide range of novel environments, which may have very different statistical properties. We consider the problem of how to optimally distribute receptors along a one-dimensional continuum consistent with the following design principles. First, neural representations of the world should obey a neural uncertainty principle---making as few assumptions as possible about the statistical structure of the world. Second, neural representations should convey, as much as possible, equivalent information about environments with different statistics. The results of these arguments resemble the structure of the visual system and provide a natural explanation of the behavioral Weber-Fechner law, a foundational result in psychology. Because the derivation is extremely general, this suggests that similar scaling relationships should be observed not only in sensory continua, but also in neural representations of ``cognitive' one-dimensional quantities such as time or numerosity
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