Adaptive beamforming algorithms for smart antennas in DS-CDMA, MC-CDMA and MIMO systems

Abstract

Recent explosive wireless communications market has demanded increasingly larger capacity and higher speed in data communication than ever. Wireless designers constantly find methods that must have ability to support higher data rate and broadband media communication as well. First, smart antenna array [1]-[3] (so-called adaptive antennas) is foreseen as one of the most promising technologies for reducing interference and increasing capacity of network. And research on adaptive beamforming algorithms for adaptive antennas have been paid a lot of attention and many algorithms [10], [19] such as Least Mean Square (LMS), Recursive Least Square (RLS) have been exploited. Second, Multi-carrier (MC-) CDMA [12] scheme has gained much attention because the signal can be easily transmitted and received using the fast Fourier transform (FFT) and is potentially robust to channel frequency selectivity. Another technique that also has been proposed as candidate for next generation wireless system is Multi-Input Multi-Output (MIMO) which exploits multiple antennas at both transmitter and receiver sides. In this thesis, I present my researches in smart antennas studied at Information and Communications University. First, I have proposed an alternative variable step size for variable step size LMS algorithms for smart antennas applied to the DS-CDMA system, let us call F-VS-LMS. One may know that since LMS algorithm is a member of the family of stochastic gradient algorithms, an appropriate choice of step size parameter is very important for the algorithm to converge. A small step size will ensure small mis-adjustments in steady state, but the algorithm will converge slowly. On the other hand, a large step size will provide faster convergence and better tracking capabilities, but will result in higher mis-adjustments. In other words, there will be a trade-off between the convergence speed and the steady-state mis-adjustments of the conventional LMS algorithm. Thus I proposed...