Browsing by Subject "particle swarm optimization (PSO)"

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Open Access
Antenna analysis
(2009) Tunç, Celal Alp
Multiple-input-multiple-output (MIMO) wireless communication systems have been attracting huge interest, since a boost in the data rate was shown to be possible, using multiple antennas both at the transmitter and receiver. It is obvious that the electromagnetic effects of the multiple antennas have to be included in the wireless channel for an accurate system design, though they are often neglected by the early studies. In this thesis, the MIMO channel is investigated from an electromagnetics point of view. A full-wave channel model based on the method of moments solution of the electric field integral equation is developed and used in order to evaluate the MIMO channel matrix accurately. The model is called the channel model with electric fields (MEF) and it calculates the exact fields via the radiation integrals, and hence, it is rigorous except the random scatterer environment. The accuracy of the model is further verified by the measurement results. Thus, it is concluded that MEF achieves the accuracy over other approaches which are incapable of analyzing antenna effects in detail. Making use of the presented technique, MIMO performance of printed dipole arrays is analyzed. Effects of the electrical properties of printed dipoles on the MIMO capacity are explored in terms of the relative permittivity and thickness of the dielectric material. Appropriate dielectric slab configurations yielding high capacity printed dipole arrays are presented. The numerical efficiency of the technique (particularly for freestanding and printed dipoles) allows analyzing MIMO performance of arrays with large number of antennas, and high performance array design in conjunction with well-known optimization tools. Thus, MEF is combined with particle swarm optimization (PSO) to design MIMO arrays of dipole elements for superior capacity. Freestanding and printed dipole arrays are analyzed and optimized, and the adaptive performance of printed dipole arrays in the MIMO channel is investigated. Furthermore, capacity achieving input covariance matrices for different types of arrays are obtained numerically using PSO in conjunction with MEF. It is observed that, moderate capacity improvement is possible for small antenna spacing values where the correlation is relatively high, mainly utilizing nearly full or full covariance matrices. Otherwise, the selection of the diagonal covariance is almost the optimal solution.MIMO performance of printed rectangular patch arrays is analyzed using a modified version of MEF. Various array configurations are designed, manufactured, and their MIMO performance is measured in an indoor environment. The channel properties, such as the power delay profile, mean excess delay and delay spread, are obtained via measurements and compared with MEF results. Very good agreement is achieved.
Open Access
Development of new array signal processing techniques using swarm intelligence
(2010) Güldoğan, Mehmet Burak
In this thesis, novel array signal processing techniques are proposed for identifi- cation of multipath communication channels based on cross ambiguity function (CAF) calculation, swarm intelligence and compressed sensing (CS) theory. First technique detects the presence of multipath components by integrating CAFs of each antenna output in the array and iteratively estimates direction-of-arrivals (DOAs), time delays and Doppler shifts of a known waveform. Second technique called particle swarm optimization-cross ambiguity function (PSO-CAF) makes use of the CAF calculation to transform the received antenna array outputs to delay-Doppler domain for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components are identified by using a simple amplitude thresholding in the delay-Doppler domain. PSO is used to estimate parameters of the multipath components in each cluster. Third proposed technique combines CS theory, swarm intelligence and CAF computation. Performance of standard CS formulations based on discretization of the multipath channel parameter space degrade significantly when the actual channel parameters deviate from the assumed discrete set of values. To alleviate this “off-grid”problem, a novel technique by making use of the PSO, that can also be used in applications other than the multipath channel identification is proposed. Performances of the proposed techniques are verified both on sythetic and real data.