The Experts below are selected from a list of 57711 Experts worldwide ranked by ideXlab platform
Xiyin Wang - One of the best experts on this subject based on the ideXlab platform.
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a novel tone reservation scheme with Fast Convergence for papr reduction in ofdm systems
Consumer Communications and Networking Conference, 2008Co-Authors: Yuzhong Jiao, Xuejiao Liu, Xiyin WangAbstract:OFDM is facing great opportunities and challenges in current broadband communication era. These opportunities and challenges derive from the native advantages and disadvantages of OFDM technology respectively. Too high PAPR is one of the main problems that prevent OFDM from being used more generally in broadband systems. Many approaches such as clipping and filtering, coding, SLM, PTS, and tone reservation have been studied to reduce the peak magnitude of OFDM symbols. In these approaches, tone reservation is considered as one of the most promising methods because of no additional distortion, no side information, and low implementation cost. In this paper, a novel tone reservation scheme is presented. Its essential idea is that a subcarrier selected from all reserved subcarriers for PAPR reduction should have a phase close to one of phi, pi/2+phi, pi+phi and -pi/2+phi, at the peak location in time domain, where phi is the phase of the peak sample. This results in no complex multiplication and division in the novel scheme. In addition, the floating positions of such selected subcarriers in frequency domain are helpful to the Convergence of the algorithm. The simulation results show that the scheme can provide good performance and Fast Convergence.
Yuzhong Jiao - One of the best experts on this subject based on the ideXlab platform.
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a novel tone reservation scheme with Fast Convergence for papr reduction in ofdm systems
Consumer Communications and Networking Conference, 2008Co-Authors: Yuzhong Jiao, Xuejiao Liu, Xiyin WangAbstract:OFDM is facing great opportunities and challenges in current broadband communication era. These opportunities and challenges derive from the native advantages and disadvantages of OFDM technology respectively. Too high PAPR is one of the main problems that prevent OFDM from being used more generally in broadband systems. Many approaches such as clipping and filtering, coding, SLM, PTS, and tone reservation have been studied to reduce the peak magnitude of OFDM symbols. In these approaches, tone reservation is considered as one of the most promising methods because of no additional distortion, no side information, and low implementation cost. In this paper, a novel tone reservation scheme is presented. Its essential idea is that a subcarrier selected from all reserved subcarriers for PAPR reduction should have a phase close to one of phi, pi/2+phi, pi+phi and -pi/2+phi, at the peak location in time domain, where phi is the phase of the peak sample. This results in no complex multiplication and division in the novel scheme. In addition, the floating positions of such selected subcarriers in frequency domain are helpful to the Convergence of the algorithm. The simulation results show that the scheme can provide good performance and Fast Convergence.
Seonglyun Kim - One of the best experts on this subject based on the ideXlab platform.
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second order power control with asymptotically Fast Convergence
IEEE Journal on Selected Areas in Communications, 2000Co-Authors: Riku Jantti, Seonglyun KimAbstract:This paper proposes a distributed power control algorithm that uses power levels of both current and previous iterations for power update. The algorithm is developed by applying the successive overrelaxation method to the power control problem. The gain from such a second-order algorithm is in Faster Convergence. Convergence analysis of the algorithm in case of feasible systems is provided in this paper. Using the distributed constrained power control (DCPC) as a reference algorithm, we carried out computational experiments on a DS-CDMA system. The results indicate that our algorithm significantly enhances the Convergence speed of power control. A practical version of the proposed algorithm is provided and compared with the bang-bang type algorithm used in the IS-95 and the WCDMA systems. The results show that our algorithm also has a high potential for increasing the radio network capacity. Our analysis assumes that the system is feasible in the sense that we can support every active user by an optimal power control. When the system becomes infeasible because of high traffic load, it calls for other actions such as transmitter removal, which is beyond the scope of the present paper.
Pascal Frossard - One of the best experts on this subject based on the ideXlab platform.
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Polynomial filtering for Fast Convergence in distributed consensus
IEEE Transactions on Signal Processing, 2009Co-Authors: Effrosyni Kokiopoulou, Pascal FrossardAbstract:In the past few years, the problem of distributed consensus has received a lot of attention, particularly in the framework of ad hoc sensor networks. Most methods proposed in the literature address the consensus averaging problem by distributed linear iterative algorithms, with asymptotic Convergence of the consensus solution. The Convergence rate of such distributed algorithms typically depends on the network topology and the weights given to the edges between neighboring sensors, as described by the network matrix. In this paper, we propose to accelerate the Convergence rate for given network matrices by the use of polynomial filtering algorithms. The main idea of the proposed methodology is to apply a polynomial filter on the network matrix that will shape its spectrum in order to increase the Convergence rate. Such an algorithm is equivalent to periodic updates in each of the sensors by aggregating a few of its previous estimates. We formulate the computation of the coefficients of the optimal polynomial as a semidefinite program that can be efficiently and globally solved for both static and dynamic network topologies. We finally provide simulation results that demonstrate the effectiveness of the proposed solutions in accelerating the Convergence of distributed consensus averaging problems.
Davide Anguita - One of the best experts on this subject based on the ideXlab platform.
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Global Rademacher Complexity Bounds: From Slow to Fast Convergence Rates
Neural Processing Letters, 2015Co-Authors: Luca Oneto, Alessandro Ghio, Sandro Ridella, Davide AnguitaAbstract:Previousworksinliteratureshowedthatperformanceestimationsoflearningpro- cedures can be characterized by a Convergence rate ranging between O(n^(−1)) (Fast rate) and O(n^(−1/2)) (slow rate). In order to derive such result, some assumptions on the prob- lem are required; moreover, even when Convergence is Fast, the constants characterizing the bounds are often quite loose. In this work, we prove new Rademacher complexity (RC) based bounds, which do not require any additional assumptions for achieving a Fast Convergence rate O(n^(−1)) in the optimistic case and a slow rate O(n^(−1/2)) in the general case. At the same time, they are characterized by smaller constants with respect to other state-of-the-art RC Fast converging alternatives in literature. The results proposed in this work are obtained by exploiting the fundamental work of Talagrand on concentration inequalities for product mea- sures and empirical processes. As a further issue, we also provide the extension of the results to the semi-supervised learning framework, showing how additional unlabeled samples allow improving the tightness of the derived bound
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IJCNN - Fast Convergence of extended Rademacher Complexity bounds
2015 International Joint Conference on Neural Networks (IJCNN), 2015Co-Authors: Luca Oneto, Alessandro Ghio, Sandro Ridella, Davide AnguitaAbstract:In this work we propose some new generalization bounds for binary classifiers, based on global Rademacher Complexity (RC), which exhibit Fast Convergence rates by combining state-of-the-art results by Talagrand on empirical processes and the exploitation of unlabeled patterns. In this framework, we are able to improve both the constants and the Convergence rates of existing RC-based bounds. All the proposed bounds are based on empirical quantities, so that they can be easily computed in practice, and are provided both in implicit and explicit forms: the formers are the tightest ones, while the latter ones allow to get more insights about the impact of Talagrand's results and the exploitation of unlabeled patterns in the learning process. Finally, we verify the quality of the bounds, with respect to the theoretical limit, showing the room for further improvements in the common scenario of binary classification.