stochasticoptimization assisted joint Channel Estimation (CE) and Multi-User Detection (MUD) were conceived and compared in the context of multi-user Multiple-Input Multiple-Output (MIMO) aided Orthogonal Frequency-D...
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ISBN:
(纸本)9781467318792
stochasticoptimization assisted joint Channel Estimation (CE) and Multi-User Detection (MUD) were conceived and compared in the context of multi-user Multiple-Input Multiple-Output (MIMO) aided Orthogonal Frequency-Division Multiplexing/Space Division Multiple Access (OFDM/SDMA) systems. The development of stochastic optimization algorithms, such as Genetic algorithms (GA), Repeated Weighted Boosting Search (RWBS), Particle Swarm optimization (PSO) and Differential Evolution (DE) has stimulated wide interests in the signal processing and communication research community. However, the quantitative performance versus complexity comparison of GA, RWBS, PSO and DE techniques applied to joint CE and MUD is a challenging open issue at the time of writing, which has to consider both the continuous-valued CE optimization problem and the discrete-valued MUD optimization problem. In this study we fill this gap in the open literature. Our simulation results demonstrated that stochasticoptimization assisted joint CE and MUD is capable of approaching both the Cramer-Rao Lower Bound (CRLB) and the Bit Error Ratio (BER) performance of the optimal ML-MUD, respectively, despite the fact that its computational complexity is only a fraction of the optimal ML complexity.
A procedure to identify the dynamic behaviour of a bolted end-plate beam-column joint, based on modal data, has been developed in this paper. A finite element reference model comprising beams has been built with speci...
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A procedure to identify the dynamic behaviour of a bolted end-plate beam-column joint, based on modal data, has been developed in this paper. A finite element reference model comprising beams has been built with special emphasis on the modelling of the joints. The most uncertain parameters of the models are updated by minimizing the discrepancies between the analytical and the experimental natural frequencies of the model. The minimization is carried out through a novel stochastic adaptive method using only the values of a defined error function. The updated models were tested using modal tests performed on the specimen according to different boundary conditions and static tests. (C) 2012 Civil-Comp Ltd. and Elsevier Ltd. All rights reserved.
stochasticoptimization assisted joint Channel Estimation (CE) and Multi-User Detection (MUD) were conceived and compared in the context of multi-user Multiple-Input Multiple-Output (MIMO) aided Orthogonal Frequency-D...
详细信息
ISBN:
(纸本)9781467318808
stochasticoptimization assisted joint Channel Estimation (CE) and Multi-User Detection (MUD) were conceived and compared in the context of multi-user Multiple-Input Multiple-Output (MIMO) aided Orthogonal Frequency-Division Multiplexing/Space Division Multiple Access (OFDM/SDMA) systems. The development of stochastic optimization algorithms, such as Genetic algorithms (GA), Repeated Weighted Boosting Search (RWBS), Particle Swarm optimization (PSO) and Differential Evolution (DE) has stimulated wide interests in the signal processing and communication research community. However, the quantitative performance versus complexity comparison of GA, RWBS, PSO and DE techniques applied to joint CE and MUD is a challenging open issue at the time of writing, which has to consider both the continuous-valued CE optimization problem and the discrete-valued MUD optimization problem. In this study we fill this gap in the open literature. Our simulation results demonstrated that stochasticoptimization assisted joint CE and MUD is capable of approaching both the Cramer-Rao Lower Bound (CRLB) and the Bit Error Ratio (BER) performance of the optimal ML-MUD, respectively, despite the fact that its computational complexity is only a fraction of the optimal ML complexity.
A new stochastic optimization algorithm referred to by the authors as the 'Mean-Variance optimization' (MVO) algorithm is presented in this paper. MVO falls into the category of the so-called "population-...
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ISBN:
(纸本)9781424481262
A new stochastic optimization algorithm referred to by the authors as the 'Mean-Variance optimization' (MVO) algorithm is presented in this paper. MVO falls into the category of the so-called "population-based stochasticoptimization technique." The uniqueness of the MVO algorithm is based on the strategic transformation used for mutating the offspring based on mean-variance of the n-best dynamic population. The mapping function used transforms the uniformly distributed random variation into a new one characterized by the variance and mean of the n-best population attained so far. The searching space within the algorithm is restricted to the range - zero to one - which does not change after applying the transformation. Therefore the variables are treated always in this band but the function evaluation is carried out in the problem range. The performance of MVO algorithm has been demonstrated on standard benchmark optimization functions. It is shown that MVO algorithm finds the near optimal solution and is simple to implement. The features of MVO make it a potentially an attractive algorithm for solving many real-world optimization problems.
This paper presents a new stochastic approach SAGACIA based on proper integration of simulated annealing algorithm (SAA), genetic algorithm (GA), and chemotaxis algorithm (CA) for solving complex optimization problems...
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This paper presents a new stochastic approach SAGACIA based on proper integration of simulated annealing algorithm (SAA), genetic algorithm (GA), and chemotaxis algorithm (CA) for solving complex optimization problems. SAGACIA combines the advantages of SAA, GA, and CA together. It has the following features: 1) it is not the simple mix of SAA, GA, and CA;2) it works from a population;3) it can be easily used to solve optimization problems either with continuous, variables or with discrete variables, and it does not need coding and decoding,;and 4) it can easily escape from local minima and converge quickly Good solutions can be obtained in a very short time. The search process of SAGACIA can be explained with Markov chains. In this paper, it is proved that SAGACIA has the property of global asymptotical convergence. SAGACIA has been applied to solve such problems as scheduling, the training of artificial neural networks, and the optimizing of complex functions. In all the test cases, the performance of SAGACIA is better than that of SAA, GA, and CA.
A novel common Tabu algorithm for global optimizations of engineering problems is presented. The robustness and efficiency of the presented method are evaluated by using standard mathematical functions and hy solving ...
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A novel common Tabu algorithm for global optimizations of engineering problems is presented. The robustness and efficiency of the presented method are evaluated by using standard mathematical functions and hy solving a practical engineering problem. The numerical results show that the proposed method is (i) superior to the conventional Tabu search algorithm in robustness, and (ii) superior to the simulated annealing algorithm in efficiency. (C) 2001 Elsevier Science B.V. All rights reserved.
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