Adaptive beamforming algorithms for smart antennas

Abstract

I proposed several adaptive beamforming algorithms for smart antennas. The algorithms are, namely, simplified gradient method (SGM), projection least mean square and minimum mean square error (PLMS & PMMSE), exponential step-size least mean square (ES-LMS), scan multi-user detection (SMUD), and least mean square multi-input multi-output (LMS-MIMO). At first, SGM is very low in its computational complexity, that is O(5N), compared with the conventional algorithms, such as MCGM, that is O($3N^2$+12N), and ILSP, that is O($\emph{KNd}$). In spite of the low computational load, the SGM has also a good performance comparable to the conventional algorithms. At second, PLMS and PMMSE algorithms have the BER performance that is comparable to that of steepest descent decision directed LMS(SD-DD-LMS) and constant modulus LMS (CM-LMS) algorithms. However, because of applying the finite constellation property of the digital signal, the two algorithms are applicable not only for BPSK but also for other modulation techniques such as QPSK and QAM. Besides, the PLMS and PMMSE algorithms are also not complex in comparison with SD-DD-LMS and CM-LMS algorithm. Thus, the two proposed algorithms are good choices in real time applications for smart antennas. At third, the convergence speed of ES-LMS1, ES-LMS2 can be changed by using variable step sized. These two proposed algorithms converge more quickly than the conventional LMS-CM algorithm, thus resulting in a very good capability of tracking the desired signal source. Furthermore, because the algorithms also exposit the finite constellation property of digital signal, they can be applicable not only for BPSK modulation but also for other modulation techniques such as QPSK or QAM. At fourth, SMUD in composed of two stages, scan stage and adaptive beamforming stage. The scan stage roughly detects the incident user signals and the adaptive beamforming stage makes the signal of the each user detected in the scan stage converge to the...