Comparison of Different Smart Antenna Beamforming Algorithms in Interference Suppression
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Date
2016-07
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Addis Ababa University
Abstract
Nowadays demand for using wireless technologies is increasing tremendously all over the world. This growth will necessitate demand for high capacity of network, frequency spectrum reuse and minimum channel interference. Smart antennas are one of the most promising technologies that will enable a higher capacity in wireless networks by effectively reducing multipath and co-channel interference. This is achieved by focusing the radiation only in the desired direction and adjusting itself to changing traffic conditions or signal environments. Thus, smart antennas are an effective counter measure to achieve these requirements because they offer less interference, flexibility, less weight, high speed, phase control independent of frequency and low propagation loss. Smart antennas combine the antenna array with signal processing to optimize automatically the beam pattern in response to the received signal. Basic concept in smart antenna technology is beamforming, its mainly used to create the radiation pattern of the antenna arrays by adding constructively the phase of the signals in the direction of desired targets and nulling the pattern of undesired targets.
The main aim of this thesis is to study systematic comparison of the performances such as side lobe level, null and mean square error of different adaptive algorithms for beamforming for smart antenna system. The strategy used to achieve the major aim is an in-depth investigation of three adaptive algorithms, the Least Mean Square (LMS), Recursive Least Squares (RLS) and the Sample Matrix Inversion (SMI). The Simulation results provided showed that the beam steering ability and nullifying capability was satisfactory for those algorithms but this performances degrades with increase in number of users. LMS algorithm had slow convergence rate beside simplicity. SMI and LMS algorithm have better performance in placing deep null towards the interferer position than RLS algorithms when separation between each users is less than half the first null beam width. The effect of antenna array number, antenna element spacing as well as increasing number of interferers on nulling the undesired user and sidelobes level has also been studied.
Key words: Beamforming, Sidelobes, Smart Antennas, LMS, SMI, RLS
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Beamforming; Sidelobes; Smart Antennas; Lms; Smi; Rls