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Sparse linear arrays (SLAs) provide a high number of degrees of freedom (DOF) and reduce the mutual coupling effect between sensors of the array. Many design methods for SLAs have been proposed in the last two decades but most of the existing SLA design methods fail to achieve minimum number of sensors for a desired DOF. In this paper, a design method is proposed which utilizes the branch and bound (B & B) optimization algorithm to give a SLA with the desired DOF and minimum number of physical sensors. The proposed SLA design method is obtained by generalizing the minimum sensor array (MSA) design method which is recently proposed in the literature. This generalized method provides the same DOF as the MSA method but with a smaller number of physical sensors. In addition, a version of this design method is proposed which provides more robustness against mutual coupling effect between the sensors of the array. Simulation results demonstrate the superiority of the performance of the SLAs obtained with the proposed method over the arrays obtained with the MSA method and also over the other existing SLAs. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new predistortion linearization technique based on the memory polynomial model of the power amplifier (PA) and a multi-bit delta-sigma modulator (DSM), in order to avoid reverse modeling and the associated instability problems. In this topology, a multi-bit DSM is utilized as a digital predistorter by locating the forward model of the PA in its feedback path. The technique has two main advantages. First, it applies the forward model instead of the reverse model of the PA, which leads to less implementation complexity and, therefore, greater robustness against instability. Second, using a dynamic model instead of a static model makes it more applicable for the linearization of wideband signals, due to the compensation for memory effects. To justify its advantages, linearizers based on both static and dynamic models have been designed and tested. For a 4MHz WiMAX signal, the measurement results show an improvement of 17.2dB in the signal-to-noise and distortion ratio (SNDR) by using a dynamic polynomial model, compared to an improvement of 13dB for a static model. [Copyright &y& Elsevier]