Signal Processing Applications in CDMA Communications
Signal Processing Applications in CDMA Communications
Solving shortest path problem using particle swarm optimization
Applied Soft Computing
Particle swarm optimization with adaptive population size and its application
Applied Soft Computing
Information Sciences: an International Journal
Communication Systems
Frankenstein's PSO: a composite particle swarm optimization algorithm
IEEE Transactions on Evolutionary Computation
Combining GA and iterative searching DOA estimation for CDMA signals
Neural Computing and Applications
A new approach to robust beamforming in the presence of steeringvector errors
IEEE Transactions on Signal Processing
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A Modified PSO Structure Resulting in High Exploration Ability With Convergence Guaranteed
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
PSO-Based Multiobjective Optimization With Dynamic Population Size and Adaptive Local Archives
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Overview of radiolocation in CDMA cellular systems
IEEE Communications Magazine
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This study considers the problem of estimating the direction-of-arrival (DOA) for code-division multiple access (CDMA) signals. In this type of problem, the associated cost function of the DOA estimation is generally a computationally-expensive and highly-nonlinear optimization problem. A fast convergence of the global optimization algorithm is therefore required to attain results within a short amount of time. In this paper, we propose a new application of the modify particle swarm optimization (MPSO) structure to achieve a global optimal solution with a fast convergence rate for this type of DOA estimation problem. The MPSO uses a first-order Taylor series expansion of the objective function to address the issue of enhanced PSO search capacity for finding the global optimum leads to increased performance. The first-order Taylor series approximates the spatial scanning vector in terms of estimating deviation results in and reducing to a simple one-dimensional optimization problem and the estimating deviation has the tendency to fly toward a better search area. Thus, the estimating deviation can be used to update the velocity of the PSO. Finally, several numerical examples are presented to illustrate the design procedure and to confirm the performance of the proposed method.