Abstract
An adaptive antenna array system adjusts the main lobe of radiation pattern in the direction of desired signal and points the nulls in the direction of undesired signals or interferers. The essential goal of beamforming is to reduce the complexity of weighting process and to decrease the time needed for adjusting the antenna radiation pattern. In this article a new adaptive weighting algorithm is proposed for both least mean squares (LMS) and constant modulus (CM) algorithms. It is appropriate and applicable for antenna array systems with moving targets and also mobile applications as well as sensor networks. By predicting the relative velocity of source, the next location of the source will be estimated and the array weights will be determined using LMS or CM algorithm before arriving to the new point. For the next time associated to the new sampling point, evaluated weights will be used. Furthermore, by updating these weights between two consecutive times the effects of error propagation will be eliminated. Therefore, in addition to reduction in computational complexity at the time of weight allocation, relatively accurate weight allocation can be obtained. Simulation results of this investigation show that the angular error related to both LMS-based and CM-based algorithms is less than the conventional LMS and CM algorithms at different signal to noise ratios (SNRs). On the other hand, due to considering off-line process, online computational complexity of new algorithms is slightly low with respect to previous ones.
Highlights
Growth in wireless technologies and users’ demands, lead us to extend these systems in rural and urban areas, indoor and outdoor environments, short-range as well as long-range applications and optimizing them for long term scheduling
Instead of hardware changes, major part of processing is done by digital processors in intermediate frequency (IF) or baseband
By appropriate beamforming at the transmitter, receiver or both, we can reach to higher receiving power, better signal to noise plus interference ratio (SNIR), and lower bit error rate (BER)
Summary
Growth in wireless technologies and users’ demands, lead us to extend these systems in rural and urban areas, indoor and outdoor environments, short-range as well as long-range applications and optimizing them for long term scheduling. In [9], authors propose a normalized least mean square (NLMS) adaptive algorithm that incorporates a direction of arrival detection criterion Simulation results of this investigation show that the number of NLMS adapted parameters can be reduced by this method. In [15] a matrix inversion normalized least mean square (MI-NLMS) adaptive beamforming algorithm is described with tracking Simulation results of this method show that BER improvement is proportional to the number of antenna elements employed in the antenna array. In [18, 19], the performance of blind adaptive beamforming algorithms for smart antennas in realistic environments with a constrained constant modulus (CCM) design criterion is described and used for deriving a RLS type optimization algorithm.
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