Studies On Synchronization Technique In OFDM Systems

OFDM is the key technique for the fourth generation (4G) mobile communication. The great advantage of OFDM is that it is robust against frequency selective fading and that it improves frequency utilizing rate simultaneously. Not only does it increase system's capability, but also it can meet the demand of multimedia communication. It can transmit multimedia operation high quality including voice, data, video through wide-frequency channel.But OFDM is very sensitive to frequency offset and phase noise, so synchronization is the key problem for OFDM system. Lack of necessary synchronizer, the orthogonal character of subchannels will be destroyed, it will introduce subcarriers' disturbance ICI. There are three kinds of synchronal modes in OFDM system: symbol timing synchronization, carrier synchronization, sampling-bell synchronization.This paper chiefly studies symbol timing synchronization and carrier synchronization. Be based upon Maximum Likelihood (ML) estimation, it proposes two improved algorithms, one is considered from the point of extent (namely globally optimum ML estimation), the other is considered from the point of deepness (namely iterative ML estimation); it reintroduces an algorithm based on PN characteristic. Finally simulate these algorithms using MATLAB, conclude new algorithms' superiorities.Globally optimum ML estimation is not only considering the range 0≤0 ≤ L-1 of the timing offset parameter, but also considering the range N≤θ≤N + L-l. It can fully characterize the random observation vector over the entire range of the timing offset parameter; while ML estimation only considers the range 0 ≤ θ ≤ Z, -1 of the timing offset parameter. The former may remove the imperfection of the latter.Iterative ML algorithm analyzes ML from the point of iteration. It simplifies implementary process of ML algorithm. Accordingly quicken the implementary speed of the procedure.This paper simulates all the algorithms using MATLAB under AWGN channel and Rayleigh fading channel. The simulations show that globally optimum ML estimation extends the range of ML estimation and iterative ML estimation shortens the implementary time of ML algorithm.