Evaluation and Compensation of Mutual Coupling and Other Non-idealities in Small Antenna Arrays

Smart antenna technology is a challenging area in the development of wireless communications. Using smart antennas the quality of a radio link can be improved by many ways. Smart antennas are active antenna arrays or groups with changeable complex-valued weights at inputs and outputs. Good electrical matching of the array and the similarity and ideality of element patterns is usually expected. This dissertation focuses on the problems in the smart antenna arrays caused mainly by mutual coupling. Mutual coupling causes reflected power in the feeding system, input/output signal correlation and corruption of the element patterns. The arrays used in this thesis are small microstrip arrays. The used frequency is about 5.3 GHz. For several arrays the element patterns and scattering matrices are measured and used in calculations and measurements. Also simulated patterns and scattering matrices are used. Due to mutual coupling the element patterns in an array are usually corrupted and therefore pattern correction should be used in smart antennas to improve the use of adaptive algorithms. In linear pattern correction the element patterns are reshaped using all antenna elements in the array. It is a computational method using a correction matrix between true and idealized inputs/outputs of array branches. For this pattern correction two basically different methods are used. The least squares error method can be used to find the correction matrix if the actual element patterns and the wanted element patterns are known, whereas in the scattering matrix method the correction matrix is defined only with the scattering matrix. These methods are compared in this thesis and the least squares error method is found to result in clearly better array patterns. The disadvantage of the scattering matrix method is that it does not compensate ground plate diffraction. However, the scattering matrix is easier to obtain than the element patterns and its use can give better understanding of the coupling mechanisms and therefore help the antenna design. Thus its use in pattern correction is examined more accurately. An extension of the least squares pattern correction method is done by correcting the array to a virtual array with different element spacing. The results show, that the element spacing in the virtual array should not differ significantly from the spacing in the real array. In addition to the pattern correction with a correction matrix the use of the real patterns for beamforming is examined. In a modified least squares method for beamforming the weighting (cost function) is used. The beamforming with and without robust weighting is compared on the relative scale and the use of weighting give better results. When antenna elements in an array are placed closer to each other, mutual coupling increases. At the same time the correlation between received signals increases. However, the signal correlation is usually caused by the signal propagation, and the effect of mutual coupling is minor. But, when signals arrive from many different directions, the pattern correlation caused by mutual coupling gives a realistic estimate of the signal correlation. The pattern correlation is a pure array characteristic and can be found easily. In this thesis the connection between pattern correlation and mutual coupling is examined. Equations are derived for this connection using scattering parameters or reflected power. These equations allow estimate mutual coupling from pattern correlation and vice versa, which is important for antenna array development. A more detailed formulation of the connection is done for lossless two-element arrays. In practice, when there are losses in the array, mutual coupling is not necessarily usable in estimation of pattern correlation.