Maximum Likelihood Algorithms using Iterative Maximization Methods for Joint Estimation in MIMO OFDM Systems

A Modern wireless broadband system of MIMO OFDM (multiple input multiple output orthogonal frequency division multiplexing) is more popular because of good data transmission rate, robustness against multipath fading and better spectral efficiency. This system provides reliable communication and wider coverage. A main challenge to MIMO OFDM system is the retrieval of channel state information (CSI) accurately and the synchronization between the transmitter section and receiver section. Proper estimation of synchronization impairments of the system increases the accuracy of Channel State Information obtained. Thus, the concept of joint estimation of system induced synchronization impairments and channel together is adopted. Hence, Maximum Likelihood Algorithms for the joint estimation of synchronization impairments and channel for MIMOOFDM Systems has been carried out in this work. Maximum Likelihood (ML) Algorithm using grid search method for joint parameter estimation has been carried out and results are analyzed. Higher computational complexity introduced by the method of grid search is a matter of concern in this method. Thus, the method of grid search is replaced by the concept of Newton Raphson that solves the optimization problem with reduced number of computations. Performance of the estimation algorithms is studied through numerical simulations and shows that ML using Newton-Raphson method (MLNR) offers better performance than the conventional ML using grid search method. Theory states that computational complexity is highly reduced on an average implementing Newton-Raphson method.