Adaptive Beamforming Algorithm For Suppressing Co-channel Interference In OFDM System

Orthogonal frequency division multiplexing (OFDM) has been shown to be a very promising technology for high-speed data transmission and to be robust to interference as a multi-carrier modulation. It is one of key technologies for wireless broadband access systems in future or cellular mobile systems of next generation. It was identified as a core technology of the fourth generation mobile communication system. With the rapid development of wireless communication technology, limited radio resources become increasingly strained. Operators have been difficult to increase network capacity by expanding the width of the spectrum. In order to maximize spectral efficiency, OFDM systems reuse frequency by the way that cell or sector uses the same frequency band between each other. There is a serious co-channel interference problem in the edges of the cell. So suppressing co-channel interference is very necessary and meaningful. Adaptive Beamforming technology can effectively restrain the interference and retain the desired signal, so that maximize signal-to-interference-plus-noise ratio.This paper introduces the basic principles, key technology, the advantages and disadvantages of OFDM systems, and then introduces the principle of adaptive beamforming, and summarizes the beamforming criteria and beamforming algorithm. Adaptive beamforming algorithm is the core of adaptive antenna array system and the most important decisive factor in system performance. It is also the key focus of the study in adaptive array. Most of the adaptive algorithm will need information such as direction of arrival (AOA), training sequences, pilot sequence, the channel impulse response estimates and the help of other auxiliary information. They rarely use less data to achieve the effect of strong interference suppression. In this paper, we establish the OFDM system model and transform the adaptive beamforming problem into solving the generalized eigenvector problem by using the maximum SINR criteria.Generalized eigendecomposition problem is a statistical processing technique having a wide use in modern signal processing applications. Typical applications include feature extraction, pattern recognition, classification, signal estimation and detection, array signal processing. In this paper, the generalized eigendecomposition problem is reinterpreted as an unconstrained nonlinear optimization problem. Adaptive algorithm is proposed for estimating the first principal generalized eigenvector. With the use of shrinkage rules, the algorithm is extended to estimate the former principal generalized eigenvectors. Matlab simulation results show that the algorithm has low computational complexity, fast convergence and high accuracy features and that the algorithm is suitable for real time signal processing applications. Using the algorithm for estimating the first principal generalized eigenvector, we have derived the blind adaptive beamforming algorithm and the simulation results show the algorithm has a ability in suppressing strong co-channel interference.